Rafal Kucharski Lab

2025

arXiv

URB - Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles

Akman, Ahmet Onur, Psarou, Anastasia, Hoffmann, Michał, Gorczyca, Łukasz, Kowalski, Łukasz, Gora, Paweł, Jamróz, Grzegorz, and Kucharski, Rafał

arXiv preprint arXiv:2505.17734 2025

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Connected Autonomous Vehicles (CAVs) promise to reduce congestion in future urban networks, potentially by optimizing their routing decisions. Unlike for human drivers, these decisions can be made with collective, data-driven policies, developed by machine learning algorithms. Reinforcement learning (RL) can facilitate the development of such collective routing strategies, yet standardized and realistic benchmarks are missing. To that end, we present \our: Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles. \our is a comprehensive benchmarking environment that unifies evaluation across 29 real-world traffic networks paired with realistic demand patterns. \our comes with a catalog of predefined tasks, four state-of-the-art multi-agent RL (MARL) algorithm implementations, three baseline methods, domain-specific performance metrics, and a modular configuration scheme. Our results suggest that, despite the lengthy and costly training, state-of-the-art MARL algorithms rarely outperformed humans. Experimental results reported in this paper initiate the first leaderboard for MARL in large-scale urban routing optimization and reveal that current approaches struggle to scale, emphasizing the urgent need for advancements in this domain.
arXiv

SimFLEX: a methodology for comparative analysis of urban areas for implementing new on-demand feeder bus services

Vasiutina, Hanna, Shulika, Olha, Bujak, Michał, Ghasemi, Farnoud, and Kucharski, Rafał

arXiv preprint arXiv:2504.17538 2025

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On-demand feeder bus services present an innovative solution to urban mobility challenges, yet their success depends on thorough assessment and strategic planning. Despite their potential, a comprehensive framework for evaluating feasibility and identifying suitable service areas remains underdeveloped. Simulation Framework for Feeder Location Evaluation (SimFLEX) uses spatial, demographic, and transport-specific data to run microsimulations and compute key performance indicators (KPIs), including service attractiveness, waiting time reduction, and added value. SimFLEX employs multiple replications to estimate demand and mode choices and integrates OpenTripPlanner (OTP) for public transport routing and ExMAS for calculating shared trip attributes and KPIs. For each demand scenario, we model the traveler learning process using the method of successive averages (MSA), stabilizing the system. After stabilization, we calculate KPIs for comparative and sensitivity analyzes. We applied SimFLEX to compare two remote urban areas in Krakow, Poland - Bronowice and Skotniki - the candidates for service launch. Our analysis revealed notable differences between analyzed areas: Skotniki exhibited higher service attractiveness (up to 30%) and added value (up to 7%), while Bronowice showed greater potential for reducing waiting times (by nearly 77%). To assess the reliability of our model output, we conducted a sensitivity analysis across a range of alternative-specific constants (ASC). The results consistently confirmed Skotniki as the superior candidate for service implementation. SimFLEX can be instrumental for policymakers to estimate new service performance in the considered area, publicly available and applicable to various use cases. It can integrate alternative models and approaches, making it a versatile tool for policymakers and urban planners to enhance urban mobility.
T&T

Can we start sharing our rides again? The post-pandemic ride-pooling market

Shulika, Olha, and Kucharski, Rafal

Transport and Telecommunication 2025

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Before the pandemic, ride-pooling was a promising mode of urban mobility, marked by increasing service providers and increasing traveller adoption, critical to its efficiency and sustainability. However, the COVID-19 pandemic caused significant disruption, with services suspended, business models altered, and reduced traveller confidence. In the post-pandemic era, understanding the future of ride-pooling is crucial. This article reviews the market through literature, pooling availability, and traveller behaviour studies. We find that the core elements of the ride-pooling model remain intact, with potential to appeal to travellers, drivers, platforms, and policymakers. Changes in travel behaviour due to the pandemic appear temporary, with a high willingness to share rides and reduce costs. However, whether ride-pooling can regain its growth remains uncertain. Despite unprecedented start-up activity, the financial prospects are unclear, posing challenges to its resurgence as a sustainable mobility solution.
arXiv

RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles

Akman, Ahmet Onur, Psarou, Anastasia, Gorczyca, Łukasz, Varga, Zoltán György, Jamróz, Grzegorz, and Kucharski, Rafał

arXiv preprint arXiv:2502.20065 2025

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RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous vehicles (AVs). The proposed framework simulates the daily route choices of driver agents in a city, including two types: human drivers, emulated using behavioral route choice models, and AVs, modeled as MARL agents optimizing their policies for a predefined objective. RouteRL aims to advance research in MARL, transport modeling, and human-AI interaction for transportation applications. This study presents a technical report on RouteRL, outlines its potential research contributions, and showcases its impact via illustrative examples.
arXiv

Autonomous Vehicles Using Multi-Agent Reinforcement Learning for Routing Decisions Can Harm Urban Traffic

Psarou, Anastasia, Akman, Ahmet Onur, Gorczyca, Łukasz, Hoffmann, Michał, Varga, Zoltán György, Jamróz, Grzegorz, and Kucharski, Rafał

arXiv preprint arXiv:2502.13188 2025

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Autonomous vehicles (AVs) using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization may destabilize traffic environments, with human drivers possibly experiencing longer travel times. We study this interaction by simulating human drivers and AVs. Our experiments with standard MARL algorithms reveal that, even in trivial cases, policies often fail to converge to an optimal solution or require long training periods. The problem is amplified by the fact that we cannot rely entirely on simulated training, as there are no accurate models of human routing behavior. At the same time, real-world training in cities risks destabilizing urban traffic systems, increasing externalities, such as CO2 emissions, and introducing non-stationarity as human drivers adapt unpredictably to AV behaviors. Centralization can improve convergence in some cases, however, it raises privacy concerns for the travelers’ destination data. In this position paper, we argue that future research must prioritize realistic benchmarks, cautious deployment strategies, and tools for monitoring and regulating AV routing behaviors to ensure sustainable and equitable urban mobility systems.
Sci. Rep.

Social implications of coexistence of CAVs and human drivers in the context of route choice

Jamróz, Grzegorz, Akman, Ahmet Onur, Psarou, Anastasia, Varga, Zoltán György, and Kucharski, Rafał

Scientific Reports 2025

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Suppose in a stable urban traffic system populated only by human driven vehicles (HDVs), a given proportion (e.g. 10 %) is replaced by a fleet of Connected and Autonomous Vehicles (CAVs), which share information and pursue a collective goal. Suppose these vehicles are centrally coordinated and differ from HDVs only by their collective capacities allowing them to make more efficient routing decisions before the travel on a given day begins. Suppose there is a choice between two routes and every day each driver makes a decision which route to take. Human drivers maximize their utility. CAVs might optimize different goals, such as the total travel time of the fleet. We show that in this plausible futuristic setting, the strategy CAVs are allowed to adopt may result in human drivers either benefitting or being systematically disadvantaged and urban networks becoming more or less optimal. Consequently, some regulatory measures might become indispensable.
TR:C

MoMaS: Two-sided Mobility Market Simulation Framework for Modeling Platform Growth Trajectories

Ghasemi, Farnoud, and Kucharski, Rafal

Transportation Research Part C: Emerging Technologies 2025

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Mobility platforms such as Uber and DiDi have been introduced in cities worldwide, each demonstrating varying degrees of success, employing diverse strategies, and exerting distinct impacts on urban mobility. We have observed various growth trajectories in two-sided mobility markets and understood the underlying mechanisms. However, to date, a realistic microscopic model of these markets including phenomena such as network effects has been missing. State-of-the-art methods well estimate the macroscopic equilibrium conditions in the market, but struggle to reproduce the individual human behavior behind and complex growth patterns sensitive to platform strategy and policies. To bridge this gap, we introduce the MoMaS (two-sided Mobility Market Simulation) framework to represent growth mechanism in two-sided mobility markets based on the realistic behavior adjustment of drivers and travelers reactive to platform strategy. In the proposed framework, traveler and driver agents learn the platform utility from multiple channels: their own experience, peers’ word-of-mouth, and the platform’s marketing, all-together constituting the agent’s perceived utility of the platform. Each of these channels is modeled and updated by our S-shaped learning model day-to-day which stabilizes, and at the same time, remains sensitive to the system changes. The platform can simulate any strategy on five levers: trip fare, commission rate, discount rate, incentive rate, and marketing. While detailed empirical data and actual strategies for platform growth remain largely unknown, MoMaS allows to reproduce series of plausible growth trajectories that were previously unattainable. The framework facilitates the modeling of individual-level behaviors such as reluctance, neutrality, and loyalty, alongside aggregate-level dynamics like critical mass, bandwagon effects, and both positive and negative cross-side network effects. We illustrate the capabilities of MoMaS through an extensive set of real-world experiments. Our results demonstrate that once the platform acquires critical mass, it triggers a significant positive cross-side network effect, accelerating growth. However, this can be reversed if a negative cross-side network effect is triggered, leading to the collapse of the platform. MoMaS is applicable for real-sized problems and available on public repository along with reproducible experiments.
TR:A

The implications of drivers’ ride acceptance decisions on the operations of ride-sourcing platforms

Ashkrof, Peyman, Ghasemi, Farnoud, Kucharski, Rafał, Almeida Correia, Gonçalo Homem, Cats, Oded, and Arem, Bart

Transportation Research Part A: Policy and Practice 2025

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As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, we study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, we delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. We find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver’s revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.
TB&S

Has the COVID-19 pandemic affected travellers’ willingness to wait with real-time crowding information?

Drabicki, Arkadiusz, Cats, Oded, and Kucharski, Rafał

Travel Behaviour and Society 2025

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Travel preferences in public transport (PT) have been substantially affected by the COVID-19 crisis, with rising emphasis on on-board safety and comfort aspects. Hence, real-time crowding information (RTCI) might have become even more instrumental in supporting travel decisions in congested urban PT systems. This study investigates the willingness to wait (WTW) to reduce (or avoid) overcrowding with RTCI in urban PT (bus and tram) journeys, analysing pre- vs. post-COVID travel behaviour attitudes. Stated-preference data and (subsequently estimated) choice models indicate that, while the pre-COVID WTW was primarily driven by mere possibility to avoid an overcrowded first departure, the post-COVID propensity to wait is strongly associated with expectations of seat availability in second departure as well. The ex-post WTW with RTCI seems to have become less-dependent on individual characteristics and more prominent for time-critical (obligatory) trips as well. Our findings underpin the rising relevance of passenger overcrowding in urban PT journeys. Moreover, they help better understand the potential of RTCI in post-pandemic recovery of PT ridership.

2024

ITSC

Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets

Ghasemi, Farnoud, Tabatabaei, Seyed Hassan, Ghanadbashi, Saeedeh, Kucharski, Rafał, and Golpayegani, Fatemeh

In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC) 2024

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Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform’s ability to leverage network effects, leading to inefficient growth. In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market. The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform’s viability in the long term.
arXiv

Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares

Bujak, Michal, and Kucharski, Rafal

arXiv preprint arXiv:2411.03370 2024

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In a ride-pooling system, travellers experience discomfort associated with a detour and a longer travel time, which is compensated with a sharing discount. Most studies assume travellers receive either a flat discount or, in rare cases, a proportional to the inconvenience. We show the system benefits from individually tailored fares. We argue that fares that optimise an expected profit of an operator also improve system-wide performance if they include travellers’ acceptance. Our pricing method is set in a heterogeneous population, where travellers have varying levels of value-of-time and willingness-to-share, unknown to the operator. A high fare discourages clients from the service, while a low fare reduces the profit margin. Notably, a shared ride is only realised if accepted by all co-travellers (decision is driven by the latent behavioural factors). Our method reveals intriguing properties of the shareability topology. Not only identifies rides efficient for the system and supports them with reduced fares (to increase their realisation probability), but also identifies travellers unattractive for the system (e.g. due to incompatibility with other travellers) and effectively shifts them to private rides via high fares. Unlike in previous methods, such approach naturally balances the travellers satisfaction and the profit maximisation. With an experiment set in NYC, we show that this leads to significant improvements over the flat discount baseline: the mileage (proxy for environmental externalities) is reduced by 4.5% and the operator generates more profit per mile (over 20% improvement). We argue that ride pooling systems with fares that maximise profitability are more sustainable and efficient if they include travellers’ satisfaction.
preprint

Optimising network efficiency in the epidemic scenario

Magdalena, Proszewska, Michal, Bujak, Rafal, Kucharski, Jacek, Tabor, and Marek, Smieja

2024

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We consider the problem of reducing virus spreading in the system network (graph) while keeping the utility of the whole system at the maximal level. To balance the above two opposite goals, we propose Deep Epidemic Efficiency Network (DEEN), an unsupervised clustering method, which optimises graph efficiency in an epidemic scenario using Graph Convolutional Neural Networks and a novel loss function. Given the desired virus transmission, it constructs a graph partition for which the predefined transmission rate is not exceeded and utility function is maximised. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network. In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by 13%.The model can be instrumental in future pandemic outbreaks when we need to balance between maintaining efficiency and preventing the spread of the virus.
npj

Hyper pooling private trips into high occupancy transit like attractive shared rides

Kucharski, Rafał, and Cats, Oded

npj Sustainable Mobility and Transport 2024

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The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.
Transportation

Ride-pooling service assessment with heterogeneous travellers in non-deterministic setting

Bujak, Michal, and Kucharski, Rafal

Transportation 2024

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Ride-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers’ ride-pooling attitudes. So far, ride-poling analyses assumed homogeneity of ride-pooling travellers. This, as we demonstrate, leads to a false assessment of ride-pooling system performance. We experiment with an actual NYC demand from 2016 and classify travellers into four groups of various ride-pooling behaviours (value of time and penalty for sharing), as reported in the recent SP study from Netherlands. We replicate their behavioural characteristics, according to the population distribution, to obtain meaningful performance estimations. Results vary significantly from the homogeneous benchmark: mileage savings were lower, while the utility gains for travellers were greater. Observing performance of heterogeneous travellers, we find that those with a low value of time are most beneficial travellers in the pooling system, while those with an average penalty for sharing benefit the most. Notably, despite the highly variable travellers’ behaviour, the confidence intervals for the key performance indicators are reasonably narrow and system-wide performance remains predictable. Our results show that the incorrect assumption of homogeneous traits leads to a high dissatisfaction of 18.5% and a cancellation rate of 36%. Such findings shed a new light on the expected performance of large scale ride-pooling systems.
JoTG

Spatiotemporal variability of ride-pooling potential – Half a year New York City experiment

Shulika, Olha, Bujak, Michal, Ghasemi, Farnoud, and Kucharski, Rafal

Journal of Transport Geography 2024

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Ride-pooling systems, despite being an appealing urban mobility mode, still struggle to gain momentum. While we know the significance of critical mass in reaching system sustainability, less is known about the spatiotemporal patterns of system performance. Here, we use 1.5 million NYC taxi trips (sampled over a six-month period) and experiment to understand how well they could be served with pooled services. We use an offline utility-driven ride-pooling algorithm and observe the pooling potential with six performance indicators: mileage reductions, travellers’ utility gains, share of pooled rides, occupancy, detours, and potential fleet reduction. We report distributions and temporal profiles of about 35 thousand experiments covering weekdays, weekends, evenings, mornings, and nights. We report complex spatial patterns, with gains concentrated in the core of the network and costs concentrated on the peripheries. The greatest potential shifts from the North in the morning to the Central and South in the afternoon. Offering pooled rides at the fare 32% lower than private ride-hailing seems to be sufficient to attract pooling yet dynamically adjusting it to the demand level and spatial pattern may be efficient. The patterns observed in NYC were replicated on smaller datasets in Chicago and Washington, DC, the occupancy grows with the demand with similar trends.
AAMAS

Modelling the Rise and Fall of Two-sided Markets

Ghasemi, Farnoud, and Kucharski, Rafal

In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems 2024

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Two-sided markets disrupted our economies, reshaping markets as diverse as tourism (airbnb), mobility (Uber) and food deliveries (UberEats). New market leaders arose leveraging on platform-based business model, questioning well-established paradigms. The underlying processes behind their growth are non-trivial, inherently microscopic, and leverage on complex human interactions. Platforms need to reach critical mass of both supply and demand to trigger the so-called cross-sided network effects. To this end, platforms adopt a variety of strategies to first create the market, then expand it and finally successfully compete with others. Such a complex social system with many non-linear interactions and learning processes calls for a dedicated modelling approach. State-of-the-art methods well estimate the macroscopic equilibrium conditions, but struggle to reproduce the complex growth patterns and individual human behaviour behind. To bridge this gap, we propose the microscopic S-shaped learning model where agents build their perception on the new service with time, affected by both endogenous (service quality) and exogenous (marketing and word-of-mouth) factors cumulated from experiences. We illustrate it with the case of two-sided mobility platform (Uber), where the platform applies a series of marketing actions leading to rise and then fall on the market where 200 drivers serve 2000 travellers on the complex urban network of Amsterdam. Our model is the first to reproduce not only behaviourally sound, but also empirically observed growth trajectories, it remains sensitive to a variety of marketing strategies, allows reproducing the competition between platforms and is designed to be integrated with machine learning algorithms to identify the optimal market entry strategy.
arXiv

Optimizing Ride-Pooling Revenue: Pricing Strategies and Driver-Traveller Dynamics

Akhtar, Usman, Ghasemi, Farnoud, and Kucharski, Rafal

arXiv preprint arXiv:2403.13384 2024

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Ride-pooling, to gain momentum, needs to be attractive for all the parties involved. This includes also drivers, who are naturally reluctant to serve pooled rides. This can be controlled by the platform’s pricing strategy, which can stimulate drivers to serve pooled rides. Here, we propose an agent-based framework, where drivers serve rides that maximise their utility. We simulate a series of scenarios in Delft and compare three strategies. Our results show that drivers, when they maximize their profits, earn more than in both the solo-rides and only-pooled rides scenarios. This shows that serving pooled rides can be beneficial as well for drivers, yet typically not all pooled rides are attractive for drivers. The proposed framework may be further applied to propose discriminative pricing in which the full potential of ride-pooling is exploited, with benefits for the platform, travellers, and (which is novel here) to the drivers.

2023

Developing an efficient road-based batch freight delivery technology for intercity connections with a focus on resource conservation

Shulika, O, Orda, O, Potaman, N, and Yashchuk, Y

Транспортні системи і технології 2023

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In this paper, we propose the solution of the scientific and applied problem of batch freight delivery in the intercity by road in terms of system’s limited resource of the system due to the development of long-distance freight delivery technology in terms of resource savings and the observation of the established level of reliability of the freight delivery functioning of the logistics system (FDLS). For certain parameters of cargo flow with limited resources of the transport market entities involved in the delivery process, the formation of a resource-saving delivery technology allows maximising the effect of the functioning of the system of delivery of consignment cargo by road in intercity traffic. To solve the problem, we used methods of mathematical modelling, a systematic approach, optimization methods (functions of many variables), the provisions of probability theory and mathematical statistics, methods of regression analysis. For the given operating conditions of the developed logistics system, the rational technology of long-distance batch freight delivery has been determined in terms of resource savings. It will reduce overall costs by 13.9% for the given level of the readiness of FDLS.
Explainability of surrogate models for traffic signal control

Gora, Pawel, Bogucki, Dominik, and Bolum, M Latif

2023

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Traffic signal control is one of the most important means of managing and optimizing road transport in cities. Developing a new generation of efficient traffic signal control algorithms is challenging and it is expected that methods based on AI may bring advantages. However, explainability and interpretability of these techniques are often limited and sometimes it might be difficult even for traffic engineers to understand the decisions taken by such systems. In this chapter we focus on explainability of one of the AI-based techniques used for traffic signal-control which employs evolutionary algorithms to find heuristically optimal signal settings for given traffic conditions, and surrogate models based on graph neural networks to evaluate the quality of signal settings much faster than by traffic simulations. The considered surrogate model is a graph neural network in which the topology of connections between neurons is built based on the topology of the road network graph (neurons correspond to intersections with traffic signals, or road segments between the intersections). Therefore, it can be suspected that analyzing behaviour of these neural networks may also bring better understanding of the urban road traffic and the spatio-temporal impact of traffic conditions on various intersections. In order to investigate the effects of input features on the output of the graph neural networks, we calculated the Shapley values and applied the Zorro method. Thanks to applying these techniques, it was possible to assess importance of intersections and their impact on the times of waiting on red signals in the considered areas on points from randomly generated datasets and on datasets with 500settings found using genetic algorithm (considered as close to local optima). It turned out that both methods produce quite consistent results. Thanks to them, it was possible to identify the most critical intersections in the road network topology which might be important from the traffic engineering perspective in tasks related to traffic signal control.
SNAM

Network structures of urban ride-pooling problems and their properties

Bujak, Michal, and Kucharski, Rafal

Social Network Analysis and Mining 2023

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Travellers, when sharing their rides in a so-called ride-pooling system, form complex networks. Despite being the algorithmic backbone to the ride-pooling problems, the shareability graphs have not been explicitly analysed yet. Here, we formalise them, study their properties and analyse relations between topological properties and expected ride-pooling performance. We introduce and formalise two representations at the two crucial stages of pooling analysis. On the NYC dataset, we run two simulations with the link generation formulas. One is when we increase discount offered to the travellers for shared rides (our control variable) and observe the phase transition. In the second, we replicate the non-deterministic behaviour of travellers in ride-pooling. This way, we generate probabilistic, weighted networks. We observed a strong correlation between the topological properties of ride-pooling networks and the system performance. Introduced class of networks paves the road to applying the network science methods to a variety of ride-pooling problems, like virus spreading, optimal pricing or stability analysis.
RTBM

Should I stay or should I board? Willingness to wait with real-time crowding information in urban public transport

Drabicki, Arkadiusz, Cats, Oded, Kucharski, Rafał, Fonzone, Achille, and Szarata, Andrzej

Research in Transportation Business & Management 2023

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Overcrowding is a major phenomenon affecting travel experience in urban public transport, whose negative impacts can be potentially mitigated with real-time crowding information (RTCI) on public transport vehicle departures. In this study, we investigate the willingness to wait (WTW) with instantaneous RTCI to avoid the in-vehicle (over)crowding the passenger faces, focusing specifically on urban crowding context (i.e. bus and tram systems). We conduct a stated-preference survey in Krakow (Poland), where we examine the choice probability between boarding now a more crowded vehicle vs. waiting at the stop for a less-crowded PT departure, and estimate a series of discrete choice models. Results show that 50–70% of respondents consider skipping a first departure which is excessively overcrowded and 10–30% would skip a vehicle with moderate standing crowding on-board. Acceptable waiting times typically range between 2 and 13 min, depending on crowding level and propensity to arrive on-time, but may even exceed 20 min in individual cases. These findings indicate that RTCI can induce a substantial WTW, affecting travel behaviour. We discuss its implications for mitigating service disruptions and demand management policies, including prospective support for public transport recovery in the aftermath of covid-19 crisis.
arXiv

Ride Acceptance Behaviour Investigation of Ride-sourcing Drivers Through Agent-based Simulation

Ghasemi, Farnoud, Ashkrof, Peyman, and Kucharski, Rafal

arXiv preprint arXiv:2310.05588 2023

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Ride-sourcing platforms such as Uber and Lyft offer drivers (i.e., platform suppliers) considerable freedom of choice in multiple aspects. At the operational level, drivers can freely accept or decline trip requests that can significantly impact system performance in terms of travellers’ waiting time, drivers’ idle time and income. Despite the extensive research into the supply-side operations, the behavioural aspects, particularly drivers’ ride acceptance behaviour remains so far largely unknown. To this end, we reproduce the dynamics of a two-sided mobility platform on the road network of Delft using an agent-based simulator. Then, we implement a ride acceptance decision model enabling drivers to apply their acceptance strategies. Our findings reveal that drivers who follow the decision model, on average, earn higher income compared to drivers who randomly accept trip requests. The overall income equality between drivers with the acceptance decision is higher and travellers experience lower waiting time in this setting.
arXiv

Dynamics of the Ride-Sourcing Market: A Coevolutionary Model of Competition between Two-Sided Mobility Platforms

Ghasemi, Farnoud, Drabicki, Arkadiusz, and Kucharski, Rafał

arXiv preprint arXiv:2310.05543 2023

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There is a fierce competition between two-sided mobility platforms (e.g., Uber and Lyft) fueled by massive subsidies, yet the underlying dynamics and interactions between the competing plat-forms are largely unknown. These platforms rely on the cross-side network effects to grow, they need to attract agents from both sides to kick-off: travellers are needed for drivers and drivers are needed for travellers. We use our coevolutionary model featured by the S-shaped learning curves to simulate the day-to-day dynamics of the ride-sourcing market at the microscopic level. We run three scenarios to illustrate the possible equilibria in the market. Our results underline how the correlation inside the ride-sourcing nest of the agents choice set significantly affects the plat-forms’ market shares. While late entry to the market decreases the chance of platform success and possibly results in "winner-takes-all", heavy subsidies can keep the new platform in competition giving rise to "market sharing" regime.
Energies

Influence of Transport Demand Parameters on Environmental Pollution for Deliveries by Cargo Bikes in City Areas with Traffic Restrictions

Vasiutina, Hanna, Naumov, Vitalii, Szarata, Andrzej, and Rybicki, Stanisław

Energies 2023

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In light of global environmental degradation, which is largely affected by the transport sector, increasing attention is paid to enhancing the quality of life in urban areas. Policymakers are taking steps to reduce transport-related pollution and accelerate the shift to sustainable city development. They introduce stricter requirements for fuel quality and transport emissions, impose fines for traffic congestion, increase parking fees, establish low-pollution zones, etc. The implementation of measures that may not be perceived positively requires careful planning and analysis. This involves using suitable techniques and software to evaluate and support planned strategies. We propose a comprehensive approach to assess the ecological effect of using a delivery fleet that incorporates cargo bikes, operating in conjunction with light vans within a restricted urban area. The presented methodology was implemented using Python programming language and includes simulation tools for the execution of alternative delivery scenarios and a model for determining emissions generated by the analyzed supply scheme. The developed approach was applied to selected areas of Krakow (Poland) and San Sebastian (Spain), and the obtained models were used to study the influence of transport demand on the decrease of transport-caused environmental pollution. Simulation results indicate a statistically significant influence of the mean consignment weight on the emission reduction obtained due to the use of cargo bikes.

2022

Tmertica:A

The shareability potential of ride-pooling under alternative spatial demand patterns

Soza-Parra, Jaime, Kucharski, Rafał, and Cats, Oded

Transportmetrica A: Transport Science 2022

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In this study, we set out to explore how various spatial patterns of travel demand drive the effectiveness of ride-pooling services. To do so, we generate a broad range of synthetic, yet plausible demand patterns. We experiment with the number of attraction centres, the dispersion of destinations around these centres, and the trip length distribution. We apply a strategic ride-pooling algorithm across the generated demand patterns to identify shareability potential using a series of metrics related to ridepooling. Our findings indicate that, under a fixed demand level, vehicle-hour reduction due to ride-pooling can range between 18 and 59%. These results depend on the concentration of travel destinations around the centre and the trip length distribution. Ride-pooling becomes more efficient when trips are longer and destinations are more concentrated. A shift from a monocentric to a polycentric demand pattern is found to have a limited impact on the prospects of ride-pooling.
PLOS

Simulating two-sided mobility platforms with MaaSSim

Kucharski, Rafał, and Cats, Oded

PLoS ONE 2022

Abs HTML

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape. Distributed supply of individual drivers, matched with travellers via intermediate platform yields a new class of phenomena not present in urban mobility before. Such disruptive changes to transportation systems call for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the complex dynamics of platform-driven urban mobility. In this work, we present MaaSSim, a lightweight agent-based simulator reproducing the transport system used by two kinds of agents: (i) travellers, requesting to travel from their origin to destination at a given time, and (ii) drivers supplying their travel needs by offering them rides. An intermediate agent, the platform, matches demand with supply. Agents are individual decision-makers. Specifically, travellers may decide which mode they use or reject an incoming offer; drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, allowing to represent agents’ taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is a flexible open-source python library capable of realistically reproducing complex interactions between agents of a two-sided mobility platform. MaaSSim is available from a public repository, along with a set of tutorials and reproducible use-case scenarios, as demonstrated with a series of illustrative examples and a comprehensive case study.
Transportation

Mitigating bus bunching with real-time crowding information

Drabicki, Arkadiusz, Kucharski, Rafał, and Cats, Oded

Transportation 2022

Abs HTML

A common problem in public transport systems is bus bunching, characterized by a negative feedback loop between service headways, number of boarding passengers and dwell times. In this study, we examine whether providing real-time crowding information (RTCI) at the stop regarding the two next vehicle departures can stimulate passengers to wait for a less-crowded departure, and thus alleviate the bunching effect. To this end, we leverage on results from own stated-preference survey and develop a boarding choice model. The model accounts for the presence of RTCI and is implemented within dynamic public transport simulation framework. Application to the case-study model of a major bus corridor in Warsaw (Poland) reveals that RTCI can induce a significant probability (30–70%) of intentionally skipping an overcrowded bus and waiting for a later departure instead. This behaviour, in turn, results in significantly lower vehicle headway and load variations, without deteriorations in total waiting utility. Overall, journey experience improves by 6%, and crucially—the prevalence of denial-of-boarding and excessive on-board overcrowding is substantially reduced, by ca. 40%. Results of our study indicate that the willingness to wait induced by RTCI can be a potential demand management strategy in counteracting bunching, with benefits already attainable at limited RTCI response rates.
Tmetrica:B

Evolution of labour supply in ridesourcing

Ruijter, Arjan, Cats, Oded, Kucharski, Rafal, and Lint, Hans

Transportmetrica B: Transport Dynamics 2022

Abs HTML

Contrary to traditional transit services, supply in ridesourcing systems emerges from individual labour decisions of gig workers. The effect of decentralisation in supply on the evolution of on-demand transit services is largely unknown. To this end, we propose a dynamic model comprising of the subsequent supply-side processes: (i) initial exposure to information about the platform, (ii) a long-term registration decision, and (iii) daily participation decisions, subject to day-to-day learning based on within-day matching outcomes. We construct a series of experiments to study the effect of supply market properties and pricing strategies. We find that labour supply in ridesourcing may be non-linear and undergo several transitions, inducing significant variations in income levels and level of service over time.Our results provide indications that the ridesourcing market may benefit from a cap in supply and regulation of the commission fee.
OE

Quantum games and interactive tools for quantum technologies outreach and education

Seskir, Zeki C, Migdał, Piotr, Weidner, Carrie, Anupam, Aditya, Case, Nicky, Davis, Noah, Decaroli, Chiara, Ercan, İlke, Foti, Caterina, Gora, Paweł, and others,

Optical Engineering 2022

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We provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the quantum community in recent years. We present selected tools as described by their developers, including “Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and the Virtual Quantum Optics Laboratory.” In addition, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population.
PLOS

Beyond the dichotomy: How ride-hailing competes with and complements public transport

Cats, Oded, Kucharski, Rafal, Danda, Santosh Rao, and Yap, Menno

Plos one 2022

Abs HTML

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.
CIiT

Using city-bike stopovers to reveal spatial patterns of urban attractiveness

Banet, Krystian, Naumov, Vitalii, and Kucharski, Rafał

Current Issues in Tourism 2022

Abs HTML

We demonstrate how digital traces of city-bike trips may become useful to identify urban space attractiveness. We exploit their unique feature – stopovers: short, non-traffic-related stops made by cyclists during their trips. As we demonstrate with the case study of Kraków (Poland), when applied to a big dataset, meaningful patterns appear, with hotspots (places with long and frequent stopovers) identified at both the top tourist and leisure attractions as well as emerging new places. We propose a generic method, applicable to any spatiotemporal city-bike traces, providing results meaningful to understand the general urban space attractiveness and its dynamics. With the proposed filtering (to mitigate a selection bias) and empirical cross-validation (to rule-out false-positive classifications) results effectively reveal spatial patterns of urban attractiveness. Valuable for decision-makers and analysts to enhance understanding of urban space consumption patterns by tourists and residents.
Transportation

Mitigating bus bunching with real-time crowding information

Drabicki, Arkadiusz, Kucharski, Rafał, and Cats, Oded

Transportation 2022

Abs HTML

A common problem in public transport systems is bus bunching, characterized by a negative feedback loop between service headways, number of boarding passengers and dwell times. In this study, we examine whether providing real-time crowding information (RTCI) at the stop regarding the two next vehicle departures can stimulate passengers to wait for a less-crowded departure, and thus alleviate the bunching effect. To this end, we leverage on results from own stated-preference survey and develop a boarding choice model. The model accounts for the presence of RTCI and is implemented within dynamic public transport simulation framework. Application to the case-study model of a major bus corridor in Warsaw (Poland) reveals that RTCI can induce a significant probability (30–70%) of intentionally skipping an overcrowded bus and waiting for a later departure instead. This behaviour, in turn, results in significantly lower vehicle headway and load variations, without deteriorations in total waiting utility. Overall, journey experience improves by 6%, and crucially—the prevalence of denial-of-boarding and excessive on-board overcrowding is substantially reduced, by ca. 40%. Results of our study indicate that the willingness to wait induced by RTCI can be a potential demand management strategy in counteracting bunching, with benefits already attainable at limited RTCI response rates.
Tmetrica:B

Evolution of labour supply in ridesourcing

Ruijter, Arjan, Cats, Oded, Kucharski, Rafal, and Lint, Hans

Transportmetrica B: Transport Dynamics 2022

Abs HTML

Contrary to traditional transit services, supply in ridesourcing systems emerges from individual labour decisions of gig workers. The effect of decentralisation in supply on the evolution of on-demand transit services is largely unknown. To this end, we propose a dynamic model comprising of the subsequent supply-side processes: (i) initial exposure to information about the platform, (ii) a long-term registration decision, and (iii) daily participation decisions, subject to day-to-day learning based on within-day matching outcomes. We construct a series of experiments to study the effect of supply market properties and pricing strategies. We find that labour supply in ridesourcing may be non-linear and undergo several transitions, inducing significant variations in income levels and level of service over time.Our results provide indications that the ridesourcing market may benefit from a cap in supply and regulation of the commission fee.
PLOS

Beyond the dichotomy: How ride-hailing competes with and complements public transport

Cats, Oded, Kucharski, Rafal, Danda, Santosh Rao, and Yap, Menno

Plos one 2022

Abs HTML

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.
arXiv

Exploring Computational Complexity Of Ride-Pooling Problems

Akhtar, Usman, and Kucharski, Rafal

arXiv preprint arXiv:2208.02504 2022

Abs HTML

Ride-pooling is computationally challenging. The number of feasible rides grows with the number of travelers and the degree (capacity of the vehicle to perform a pooled ride) and quickly explodes to the sizes making the problem not solvable analytically. In practice, heuristics are applied to limit the number of searches, e.g., maximal detour and delay, or (like we use in this study) attractive rides (for which detour and delay are at least compensated with the discount). Nevertheless, the challenge to solve the ride-pooling remains strongly sensitive to the problem settings. Here, we explore it in more detail and provide an experimental underpinning to this open research problem. We trace how the size of the search space and computation time needed to solve the ride-pooling problem grows with the increasing demand and greater discounts offered for pooling. We run over 100 practical experiments in Amsterdam with 10-minute batches of trip requests up to 3600 trips per hour and trace how challenging it is to propose the solution to the pooling problem with our ExMAS algorithm. We observed strong, non-linear trends and identified the limits beyond which the problem exploded and our algorithm failed to compute. Notably, we found that the demand level (number of trip requests) is less critical than the discount. The search space grows exponentially and quickly reaches huge levels. However, beyond some level, the greater size of the ride-pooling problem does not translate into greater efficiency of pooling. Which opens the opportunity for further search space reductions.
Energies

Estimating the emissions reduction due to the use of cargo bikes: Case studies for the selected European cities

Vasiutina, Hanna, Naumov, Vitalii, Szarata, Andrzej, and Rybicki, Stanisław

Energies 2022

Abs HTML

Contemporary cities need new solutions to reduce the harmful impact of freight transport, such as traffic congestion, air pollution, and noise emissions, which have increased in the last few years. This especially concerns central parts of cities due to the architectural features of urban space. The concept of green logistics intends to replace currently used combustion engine vehicles with zero-emissions technologies such as the use of cargo bikes to deliver goods to final recipients located in the city districts with dense urban development. However, the use of cargo bikes for certain architectural, transport, and business conditions must be justified: the positive impact of that innovative means of transport should be evaluated and assessed by decision-makers. We propose a method to evaluate the environmental impact of cargo bikes that replace conventional vehicles in the central district of a city. Our approach assumes simulations of freight travel demand in the selected city area, the estimation of the part of trips to be replaced by cargo bikes, and, finally, the evaluation of the total distance covered by the replaced combustion engine vehicles to show the reduction of CO2 generated by the conventional vehicles. The case studies of the central districts for selected partner cities (Vitoria-Gasteiz, San Sebastian, Dubrovnik, and Mechelen) that participate in the CityChangerCargoBike project are shown to demonstrate the developed methodology.
Energies

Simulating a Macrosystem of Cargo Deliveries by Road Transport Based on Big Data Volumes: A Case Study of Poland

Naumov, Vitalii, Szarata, Andrzej, and Vasiutina, Hanna

Energies 2022

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Sustainability

Shaping the optimal technology for servicing the long-distance deliveries of packaged cargo by road transport

Naumov, Vitalii, Shulika, Olha, Orda, Oleksandra, Vasiutina, Hanna, Bauer, Marek, and Oliskevych, Myroslav

Sustainability 2022

Abs HTML

Simulation models of transport systems are a key tool for solving many problems in the field of management of these systems. The methodologies for creating such models use datasets on both transport infrastructure and demand for the delivery of goods or passenger transport, however, many factors are considered based on assumptions due to the complexity. This article describes the approach to modeling the cargo transportation system for road transport in Poland based on data obtained by the Central Statistical Office from the TD-E survey. This approach avoids many assumptions about demand as the demand parameters are estimated based on a sample representing the general population—a set of all economic entities generating freight traffic. Basic procedures in the developed approach have been implemented as Python scripts. As a result of the use of the proposed methodology, a country-wide road transport model was obtained based on the TD-E survey from 2018. The adequacy of the developed model was assessed based on the results of the General Traffic Measurement from 2015. The obtained model is of satisfactory quality (the coefficient of determination equals 0.62), which can be improved after calibrating the space resistance functions and improving the traffic distribution procedure.
TMiR

Krajowy model ciężarowego transportu drogowego na podstawie wyników badań TD-E

Naumov, Vitalii, Szarata, Andrzej, and Vasiutina, Hanna

Transport Miejski i Regionalny 2022

2021

EJOR

How to split the costs and charge the travellers sharing a ride? Aligning system’s optimum with users’ equilibrium

Fielbaum, Andres, Kucharski, Rafał, Cats, Oded, and Alonso-Mora, Javier

European Journal of Operational Research 2021

Abs HTML

Emerging on-demand sharing alternatives, in which one resource is utilised simultaneously by a circumstantial group of users, entail several challenges regarding how to coordinate such users. A very relevant case refers to how to form groups in a mobility system that offers shared rides, and how to split the costs within the travellers of a group. These are non-trivial tasks, as two objectives conflict: 1) minimising the total costs of the system, and 2) finding an equilibrium where each user is content with her assignment. Aligning both objectives is challenging, as users are not aware of the externalities induced to the rest. In this paper, we propose protocols to share the costs within a ride so that optimal solutions can also constitute equilibria. To do this, we model the situation as a non-cooperative game, which can be seen as a game-version of the well-known set cover problem. We show that the traditional notions of equilibrium in game theory (Nash and Strong) are not useful here, and prove that determining whether a Strong Equilibrium exists is an NP-Complete problem, by reducing it to the k-Exact-Cover problem. Hence, we propose three alternative equilibrium notions (stronger than Nash and weaker than Strong), depending on how users can coordinate. For each of these equilibrium notions, we propose a (possibly overcharging) cost-sharing protocol that yields the optimal solutions equilibria. Simulations for Amsterdam reveal that our protocols can achieve stable solutions that are close to the optimum, and that having a central coordinator can have a large impact.
SciRep

Modelling virus spreading in ride-pooling networks

Kucharski, Rafał, Cats, Oded, and Sienkiewicz, Julian

Scientific Reports 2021

Abs HTML

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.
Tmetrica:A

If you are late, everyone is late: late passenger arrival and ride-pooling systems’ performance

Kucharski, Rafał, Fielbaum, Andres, Alonso-Mora, Javier, and Cats, Oded

Transportmetrica A: Transport Science 2021

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Sharing rides in on-demand systems allow passengers to reduce their fares and service providers to increase revenue, though at the cost of adding uncertainty to the system. Notably, the uncertainty of ride-pooling systems stems not only from travel times but also from unique features of sharing, such as the dependency on other passengers’ arrival time at their pick up points. In this work, we theoretically and experimentally analyse how late arrivals at pick up locations impact shared rides’ performance. We find that the total delay is equally distributed among sharing passengers. However, delay composition gradually shifts from on-board delay only for the first passenger to waiting delay at the origin for the last passenger. Sadly, trips with more passengers are more adversely impacted. Strategic behaviour analysis reveals Nash equilibria that might emerge. We analyse the system-wide effects and find that when lateness increases passengers refrain from sharing and eventually opt-out.
Tmetrica:A

Modelling the effects of real-time crowding information in urban public transport systems

Drabicki, Arkadiusz, Kucharski, Rafał, Cats, Oded, and Szarata, Andrzej

Transportmetrica A: Transport Science 2021

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Public transport (PT) overcrowding is a notorious problem in urban transport networks. Its negative effects upon travel experience can be potentially addressed by disseminating real-time crowding information (RTCI) to passengers. However, impacts of RTCI provision in urban PT networks remain largely unknown. This study aims to contribute by developing an extended dynamic PT simulation model that enables a thorough analysis of instantaneous RTCI consequences. In the model, RTCI is generated and disseminated across the network, and then utilised in passengers’ sequential en-route choices. A case-study demonstration of the RTCI algorithm on urban PT network model of Kraków (Poland) shows that instantaneous RTCI has the potential to improve passengers’ travel experience, although it is also susceptible to inaccuracy. RTCI provision can yield total travel utility improvements of 3% in typical PM peak-hour, with reduced impacts of the worst overcrowding effects (in terms of denied-boarding and in-vehicle travel disutility in overcrowded conditions) of 30%.
Energies

Evaluating the environmental impact of using cargo bikes in cities: A comprehensive review of existing approaches

Vasiutina, Hanna, Szarata, Andrzej, and Rybicki, Stanisław

Energies 2021

Abs HTML

The impact of the use of cargo bicycles for delivery processes on the environment is undeniably positive: it leads to the reduction of pollutants, noise, and vibrations caused by traditional vehicles; decreases traffic jams; causes more effective use of public space; and others. But how should such an effect be measured? What tools should be used to justify the necessity for change to more sustainable means of transport? How can we improve the state of the environment considering the interests of logistics service providers? There is a large amount of scientific literature dedicated to this problem: by using different modeling approaches, authors attempt to address the issue of sustainable transport. This paper conducts a literature review in the field of green cargo deliveries, investigates the benefits and drawbacks of integrating cargo bikes in urban logistics schemes, and examines methodologies and techniques for evaluating the impact of using cargo bicycles on the environment. By providing an opportunity to get acquainted with the situation in the sphere of green deliveries, the authors aim to encourage a breakthrough in the field of sustainable transport that may be achieved by using cargo bikes in modern cities. We review the existing approaches and tools for modeling transport emissions and state the significant positive environmental consequences.
Determining the acceptable walking distance when servicing cars by parking facilities

Severyn, Oleksandr, Kholodova, Olha, Shulika, Olha, Semchenko, Nataliia, and Novikova, Yevgeniya

In AIP Conference Proceedings 2021

Abs HTML

A definition of the “attractive” average walking distance in the downtown using the “gravity centre” criterion to determine the location of the vehicle parking facility has been suggested. The application of the criterion is based on the demanded quantity of parking spaces near the trip destination. The calculations have shown the possibility of reducing the walking distance when covering the vast majority of users, which will contribute to a positive decision by the majority of potential users to use the suggested parking facility, and will also provide a certain guarantee of improving the economic performance of the parking systems.
Modelling of the route network for perishable cargo delivery in the regional traffic on the basis of petri nets

Potaman, O, Shulika, O Orda, and Orda, O

Naukovo-Tekhnіchnij Zbіrnik \guillemotleftKomunal’ne Gospodarstvo MіsT 2021

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The article is devoted to the features of the organization and technology of delivery of perishable goods in small batches by road in regional traffic. It has been established that when planning this type of cargo delivery, it becomes necessary to determine such routes bypassing specified points, at which the time of delivery of perishable goods to points of sale will be minimal. Thus, as a criterion that determines the rationality of building a route network for the delivery of perishable goods in regional traffic, has been defined the time of cargo’s delivery in small batches in regional traffic. The route network for the delivery of perishable goods with a minimum delivery time is considered rational. To analyze the execution time of each delivery phase and take into account the time and quantitative parameters that affect the delivery process, the study built a model which is based on the theory of Petri nets. The parameters of the model were established on the basis of the reporting data of the private enterprise "Samoilenko A.I.". The model took into account time parameters and quantitative factors affecting the process of delivery of perishable goods in regional traffic. An experiment was carried out using the developed model. The obtained value of the integral error of the data discrepancy throughout the system allows us to assume that the constructed model adequately reproduces the process of delivery of perishable goods in small batches by road in regional traffic. Taking into account the results of delivery process’ modeling, using the method of a short connecting network, a rational route network was built in the study, which consists of six routes. The effect was determined as a time difference for the delivery system along the network of rational route network and along the existing network, which amounted to 131 minutes.
A Methodological approach to the real-time data analysis from the viaTOLL system

Naumov, Vitalii, Szarata, Andrzej, and Vasiutina, Hanna

2021

Abs HTML

Measuring the traffic parameters may be considered as one of the key operations in traffic management. With the contemporary monitoring systems, this issue transformed from the problem of gathering data to the issue of big data processing. In this chapter, we propose the methodological approach to the processing and analysing of the real-time flow of information from the traffic toll system. The core classes library developed in Python programming language implements the methodology of data processing. The results of the data analysis from the viaTOLL system (Poland) illustrate the use of the proposed approach.
Modeling demand for deliveries by cargo bicycles in the Old Town of Kraków

Naumov, Vitalii, Vasiutina, Hanna, and Solarz, Agnieszka

Transportation research procedia 2021

Abs HTML

Transport demand modeling is a crucial stage to be implemented for a variety of optimization techniques in the field of transportation management. The paper describes the model for simulations of demand for goods’ deliveries by cargo bicycles in the area with the restrictions of motorized traffic. The proposed model is based on the classical four-step approach with additional procedures for generations of demand as the flow of requests for cargo deliveries. The paper presents the case study of the demand generation in the Old Town district of Kraków city: the obtained simulation results are characterized by high adequacy. The simulation model, implemented in Python programming language, can be used as the main tool for solving optimization problems, such as the localization choice for loading points or substantiation of the number of cargo bicycles used for deliveries in the region.
Evolution of Labour Supply in Ridesourcing, TRBAM-21-01320

Ruijter, Arjan, Oded, Cats, Hans, van Lint, and Rafał, Kucharski

In TRB 100th Annual Meeting, Washington DC 2021
Beyond the Dichotomy: How Uber Competes with as well as and Complements Public Transport, TRBAM-21-01134

Cats O., Yapp M., and Danda,

In TRB 100th Annual Meeting, Washington DC 2021
Method of Ensuring the Technical Readiness of Transport Companies Fleet Due to the Region’s Capabilities

Holovnia, Serhii, Naumov, Vitalii, and Shulika, Olha

Research Methods in Modern Urban Transportation Systems and Networks 2021

Abs HTML

To ensure the activity of motor transport companies, it is necessary to guarantee the required level of technical readiness of the fleet. Given the diversity of vehicle models and the territorial distribution of companies, the task of creating their own static and mobile repair units may be economically impractical. To choose a rational variant for the organization of vehicle maintenance in a region, it is necessary to develop tools that will provide both technical readiness and a given level of costs. We have developed a method that allows finding a balance between a given level of readiness of vehicles and the funds allocated for its maintenance. In contrast to the existing methodologies, our approach considers the possibility of receiving group requests by companies and assesses the ability of third-party companies to service vehicles depending on their own flow of customers. The proposed method allows considering a set of motor transport companies in the region as one system for which the most expedient variant of involvement of motor companies in the servicing process can be assessed.

2020

Procedia Comput. Sci

Microscopic traffic simulation models for connected and automated vehicles (CAVs)–state-of-the-art

Gora, Paweł, Katrakazas, Christos, Drabicki, Arkadiusz, Islam, Faqhrul, and Ostaszewski, Piotr

Procedia Computer Science 2020

Abs HTML

Research on connected and automated vehicles (CAVs) has been gaining substantial momentum in recent years. However, the vast amount of literature sources results in a wide range of applied tools and datasets, assumed methodology to investigate the potential impacts of future CAVs traffic, and, consequently, differences in the obtained findings. This limits the scope of their comparability and applicability and calls for a proper standardization in this field of research. The objective of this paper is to contribute towards bridging this gap by providing a summary of the state-of-the-art literature review regarding microscopic simulation models for connected and automated vehicles.
Computing

A cache-based method to improve query performance of linked Open Data cloud

Akhtar, Usman, Sant’Anna, Anita, Jihn, Chang-Ho, Razzaq, Muhammad Asif, Bang, Jaehun, and Lee, Sungyoung

Computing 2020

Abs HTML

The proliferation of semantic big data has resulted in a large amount of content published over the Linked Open Data (LOD) cloud. Semantic Web applications consume these data by issuing SPARQL queries. One of the main challenges faced by querying the LOD web cloud on account of the inherent distributed nature of LOD is its high search latency and lack of tools to connect the SPARQL endpoints. In this paper, we propose an Adaptive Cache Replacement strategy (ACR) that aims to accelerate the overall query processing of the LOD cloud. ACR alleviates the burden on SPARQL endpoints by identifying subsequent queries learned from clients historical query patterns and caching the result of these queries. For cache replacement, we propose an exponential smoothing forecasting method to replace the less valuable cache content. In the experimental study, we evaluate the performance of the proposed approach in terms of hit rates, query time and overhead. The proposed approach is found to outperform existing state-of-the-art approaches, increase hit rates by 5.46%, and reduce the query times by 6.34%.
Web planning tool for deliveries by cargo bicycles in Kraków Old Town

Naumov, Vitalii, Vasiutina, Hanna, and Starczewski, Jakub

In Modern Traffic Engineering in the System Approach to the Development of Traffic Networks: 16th Scientific and Technical Conference" Transport Systems. Theory and Practice 2019" Selected Papers 16 2020

Abs HTML

The paper proposes a mathematical model and implemented software for a freight delivery system, where cargo bicycles are used as a mean of transport. The proposed mathematical model uses a graph representation to describe a transport network, where consignees are associated with the vertices, while the graph edges reflect the sections of the road net. Requests for a freight delivery are characterized with a set of numerical parameters, such as: the consignment size, the package dimensions, the time interval between requests from clients and the delivery distance. The mathematical model was used in the web planning tool for deliveries by cargo bikes in the Kraków Old Town. In the paper, the authors briefly describe some features of the developed web application: the procedures of making orders by freight owners, creation of the delivery routes for carriers and other planning and management operations.
Forming an automated technology to actively monitor the transportation of dangerous cargoes by railroad

Lavrukhin, Oleksandr, Schevcenko, V, Kulova, D, Kim, K, Vernyhora, R, Kyman, A, and Shulika, O

Восточно-Европейский журнал передовых технологий 2020

Abs HTML

The approach has been proposed to form automated technology in order to actively monitor the transportation of dangerous goods by railroad. The reported approach to the dynamic description of trains’ status has been developed on the basis of modifying the language of trains’ status in the form of abstract modeling of operational processes. This will ensure the fastest implementation of decision-making algorithms by the operational personnel at the powerful support from the automated complex of dispatching control. It has been determined that the maximum effect of the implementation of the proposed approach could be obtained by synthesizing it with a system of active monitoring of moving units’ traffic. Modeling an arbitrary case involving a train, which can occur in real operational circumstances, has made it possible to predict the violation of a normative train schedule and to simulate the cognitive process of decision-making by a train dispatcher to rationally resolve a complicated train situation under changing operational conditions while accounting for a significant number of factors. Modifying the language of train-related cases could adequately produce the spatial-time description of train situations along a simulated section, whereby it is the closest to the language of the dispatching personnel. The advantages of the proposed approach are that it makes it possible to form a database and a knowledge base as quickly as possible in order to form a working model of the system of dispatching control. A given system has been developed on the basis of the imitation of the cognitive activity of the human operator, thereby providing an opportunity to deepen the implementation of artificial intelligence systems on railroads. These innovations could help achieve the maximum level of safety in the transportation of dangerous goods while simultaneously and unconditionally reducing operating costs and gaining higher profits.
AS

Medical instructed real-time assistant for patient with glaucoma and diabetic conditions

Rehman, Ubaid Ur, Chang, Dong Jin, Jung, Younhea, Akhtar, Usman, Razzaq, Muhammad Asif, and Lee, Sungyoung

Applied Sciences 2020

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Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user’s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen’s Kappa (𝑘=0.848) that categorizes MIRA as ‘Almost Perfect’. Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.
MS

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

Razzaq, Muhammad Asif, Quero, Javier Medina, Cleland, Ian, Nugent, Chris, Akhtar, Usman, Bilal, Hafiz Syed Muhammad, Rehman, Ubaid Ur, and Lee, Sungyoung

Multimedia Systems 2020

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Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment, using a novel low-resolution Thermal Vision Sensor (TVS). Specifically, the research presents the development and implementation of a two-step framework, consisting of a Computer Vision-based method to detect and track multiple occupants combined with Convolutional Neural Network (CNN)-based HAR. The proposed algorithm uses frame difference over consecutive frames for occupant detection, a set of morphological operations to refine identified objects, and features are extracted before applying a Kalman filter for tracking. Laterally, a 19-layer CNN architecture is used for HAR and afterward the results from both methods are fused using time interval-based sliding window. This approach is evaluated through a series of experiments based on benchmark Thermal Infrared datasets (VOT-TIR2016) and multi-occupant data collected from TVS. Results demonstrate that the proposed framework is capable of detecting and tracking 88.46% of multi-occupants with a classification accuracy of 90.99% for HAR.
Computer

The intelligent medical platform: a novel dialogue-based platform for health-care services

Ali, Taqdir, Hussain, Jamil, Amin, Muhammad Bilal, Hussain, Musarrat, Akhtar, Usman, Khan, Wajahat Ali, Lee, Sungyoung, Kang, Byeong Ho, Hussain, Maqbool, Afzal, Muhammad, and others,

Computer 2020

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The proposed Intelligent Medical Platform is a dialoguebased medical decision-making system that provides medical coaching and recommendation services, based on incremental learning methodology. The prototype demonstrates 90% accuracy for knowledge acquisition, 80% satisfaction level of user interaction with the system, and 95% accuracy for system integration with the legacy system.
Suppressing the effects of induced traffic in urban road systems: Impact assessment with macrosimulation tools-results from the city of Krakow (Poland)

Drabicki, A., Szarata, A., and Kucharski, R.

2020
Low-dimensional model for bike-sharing demand forecasting that explicitly accounts for weather data

Cantelmo, Guido, Kucharski, Rafał, and Antoniou, Constantinos

In Transportation research board 99th, 2019, Washinton DC 2020
Evaluation of the transport impact on environmental pollution due to the use of cargo bikes

Vasiutina, Hanna, and Naumov, Vitalii

In International Conference on Reliability and Statistics in Transportation and Communication 2020

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The constant increase in transport demand inevitably causes environmental deterioration and has a negative impact on the environment, especially this problem is relevant for city inhabitants. Therefore, the qualitative estimation of road transport-related emissions is a crucial task for the development of environmental protection strategies. The use of environmentally friendly alternative means of transport is a constantly developing direction to improve the quality of life in urban areas. Restrictions on the use of transport with internal combustion engines are introduced in most European cities. These activities face strong resistance from the business environment: officials often find it quite difficult to justify the desirability of restrictions, because they do not have a tool to assess the effectiveness of similar activities. This also applies to the use of cargo bikes in the delivery of freight in urban areas. This paper proposes an integrative methodology for assessing the reduction of environmental pollution caused by the use of cargo bicycles. A case study of the Old Town district in Kraków, Poland, is presented to illustrate the developed methodology.
TR:B

Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations

Kucharski, Rafał, and Cats, Oded

Transportation Research Part B: Methodological 2020

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The premise of ride-sharing is that service providers can offer a discount, so that travellers are compensated for prolonged travel times and induced discomfort, while still increasing their revenues. While recently proposed real-time solutions support online operations, algorithms to perform strategic system-wide evaluations are crucially needed. We propose an exact, replicable and demand-, rather than supply-driven algorithm for matching trips into shared rides. We leverage on delimiting our search for attractive shared rides only, which, coupled with a directed shareability multi-graph representation and efficient graph searches with predetermined node sequence, narrows the (otherwise exploding) search-space effectively enough to derive an exact solution. The proposed utility-based formulation paves the way for model integration in travel demand models, allowing for a cross-scenario sensitivity analysis, including pricing strategies and regulation policies. We apply the proposed algorithm in a series of experiments for the case of Amsterdam, where we perform a system-wide analysis of the ride-sharing performance in terms of both algorithm computations of shareability under alternative demand, network and service settings as well as behavioural parameters. In the case of Amsterdam, 3000 travellers offered a 30% discount form 1900 rides achieving an average occupancy of 1.67 and yielding a 30% vehicle-hours reduction at the cost of halving service provider revenues and a 17% increase in passenger-hours. Benchmarking against time-window constrained approaches reveals that our algorithm reduces the search-space more effectively, while yielding solutions that are substantially more attractive for travellers.
TRR

Low-Dimensional Model for Bike-Sharing Demand Forecasting that Explicitly Accounts for Weather Data

Cantelmo, Guido, Kucharski, Rafał, and Antoniou, Constantinos

Transportation Research Record 2020

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With the increasing availability of big, transport-related datasets, detailed data-driven mobility analysis is becoming possible. Trips with their origins, destinations, and travel times are now collected in publicly available databases, allowing for detailed demand forecasting with methods exploiting big and accurate data. In this paper, we predict the demand pattern of New York City bikes with a low-dimensional approach utilizing three-level data clustering. We use historical demand data along with temperature and precipitation to first aggregate and then decompose data to obtain meaningful clusters. The core of this approach lies in the proposed clustering technique, which reduces the dimension of the problem and, differently from other machine learning techniques, requires limited assumptions on the model or its parameters. The proposed method allows, for the given temperature and precipitation method, to obtain expected vector of movement (mean number and direction of trips) for each zone. In this paper, we synthesize more than 17 million trips into daily and zonal vectors of movement, which combined with weather data allow forecasting of the trip demand. The method allows us to predict the demand with over 75% accuracy, as shown in series of experiments in which various settings and parameterizations are validated against 25% holdout data.
JAT

Lewis-Mogridge Points: A Nonarbitrary Method to Include Induced Traffic in Cost-Benefit Analyses

Kucharski, Rafał, Drabicki, Arkadiusz, Paszkowski, Jan, and Szarata, Andrzej

Journal of Advanced Transportation 2020

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We propose a new method to estimate benefits of road network improvements, which allows to include the induced demand without arbitrary assumptions. Instead of estimating induced demand (which is nontrivial and hardly possible in practice), we search for demand induction where initial benefits are mitigated to zero. Such approach allows to formulate a dual measure of benefit, covering both the potential benefits and the likelihood of consuming them by the induced traffic. We first estimate benefits of road network improvement assuming that traffic demand is fixed. Consequently, we find demand model configurations at which the benefits of the new investment become null, i.e., all the initial benefits are consumed by the traffic demand growth. We call such states of induced demand the Lewis–Mogridge points of the analysed improvement. We select the most probable of such points and use it to calculate the proposed novel indicator μ, for which the initial benefits (obtained under a fixed-demand assumption) are multiplied with a demand increase rate needed to consume them. We believe that such measure allows to include the critical phenomena of induced traffic and, at the same time, to overcome problems associated with reliable estimation of induced demand. As we illustrate with the case of two alternative road improvement schemes in Kraków, Poland, the proposed method allows to estimate maximal threshold of induced traffic and to select scenario more resilient to induced traffic.

2019

Transp. Res. Proc.

Designing urban areas using traffic simulations, artificial intelligence and acquiring feedback from stakeholders

Gora, Paweł

Transportation Research Procedia 2019

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TR:D

Impact of commuting, time budgets, and activity durations on modal disparity in accessibility to supermarkets

Niedzielski, Michał A., and Kucharski, Rafał

Transportation Research Part D: Transport and Environment 2019

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Urban spatial structure, transport mode and space-time constraints are known to affect accessibility to activities for a city’s residents. However, most studies measure accessibility from home only and report accessibility in terms of the travel time or number of activities. Such studies neglect the mobility and time budget constraints on people’s accessibility. Furthermore, modal accessibility disparity studies have not considered inequality between modes during the afternoon commute. We examine accessibility to non-work activities by automobile and public transport using spatiotemporal measures that determine the number of minutes people have available to participate in activities, given time budget and activity duration constraints, rather than simply the number of activities. We examine modal accessibility disparity in Warsaw, Poland. Our two major findings are the reverse of findings in previous studies: that disparity shifts toward public transport as the activity duration increases and the travel time decreases; and that it shifts toward public transport with increasing distance away from the city center.
TR:B

Simulation of rerouting phenomena in Dynamic Traffic Assignment with the Information Comply Model

Kucharski, Rafał, and Gentile, Guido

Transportation Research Part B: Methodological 2019

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We present the Information Comply Model (ICM) which extends the framework for macroscopic within-day DTA proposed by Gentile (2016) to represent the rerouting of drivers wrt a single traffic event. Rerouting is reproduced as a two-stage process: first, drivers become aware about the event and its consequences on traffic; second, drivers may decide to change path. At each arc, unaware drivers have a probability of being informed by multiple ATIS sources (radio, VMS, mobile apps), which depends not only on devise penetration rates, but also on users space and time coordinates wrt the position and interval of the event. At each node, aware drivers, who are somehow reluctant to change, may finally modify their path based on a random rerouting utility, which is composed of expected gains and avoided losses. ICM is thus capable of representing the evolution of rerouting phenomena in time and space when the information about a traffic event and its consequences on congestion spread among drivers and onto the network. This way, ICM extends the concept of dynamic user equilibrium to a case of imperfect information related to availability and awareness rather than to individual perception, as well as to a case of bounded rationality with prudent drivers. Besides the model architecture and specification, this paper provides a workable methodology which can be applied both off-line for transport planning and in real-time for traffic management on large size networks.
EJTIR

Multichannel queueing behaviour in urban bicycle traffic

Kucharski, Rafał, Drabicki, Arkadiusz, Żyłka, Klaudia, and Szarata, Andrzej

European Journal of Transport and Infrastructure Research 2019

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The objective of this paper is to propose a method to analyse and describe cyclists’ behaviour at signalized intersections with specific focus on the multichannel (multi-lane) queue phenomenon. As we observed, cyclists form queues without a fixed-lane and FIFO discipline, for which the classical, car-oriented analytical approach becomes insufficient. Cyclists’ multichannel queueing behaviour is common and characterized by substantial degree of variability, especially in case of shorter queues which emerge regularly at cycle crossings. Although cyclist behaviour has been widely studied by transportation research community, their queueing behaviour picture is still incomplete. Namely, there is no method addressed to analyse the full scope of these phenomena and to quantify their impact on the cyclist queue performance. To bridge this gap, we introduce the technique to observe multichannel queues and report relevant observations, which we then complement with a methodological framework to analyse obtained results and provide a complete multichannel queue description. We video-record cyclists as they enqueue to one of multiple channels, form the queue and smoothly merge into a single lane again as the queue discharges. We apply the method to analyse results from a pilot study of 160 cyclists forming 50 queues in the city of Krakow, Poland. The proposed method allows us to analyse and quantify the observed queue performance and its characteristics: the number of channels, their emergence process, channel and queue lengths, discharge process with FIFO violations, starting and discharging times. Findings from pilot study reveal that both queue length and discharge times strongly depend on queue formation process. The contribution of this paper is the method to describe multichannel cyclist queueing behaviour, enriching current picture of bicycle flow and cyclists’ behaviour. Since the method has been developed on relatively short queues (up to 10 cyclists), findings included in this paper primarily refer to such queue sizes. Nonetheless, the method is formulated in a generic way, applicable also for longer bicycle queues. Possible practical implications are new estimates for queue lengths and discharge times - useful for bicycle infrastructure design and traffic engineering purposes.
Dividing investments into sustainable development of railway transport system as linear programming problem

Naumov, Vitalii, and Vasiutina, Hanna

In 5th International Conference on Road and Rail Infrastructure 2019

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The problem of dividing the investments into development of railway transport systems is formulated as a linear programming problem. As the objective function for the optimization problem, we propose to use the sum of private performance indicators characterizing resource saving, the environmental component of functioning, the social component, and the quality of customer service. The vector of control variables in the proposed formulation is the set of investments shares for the considered directions of sustainable development, and the vector of the objective function coefficients contains functions of investments elasticity for the respective directions of the railway system sustainable development. As far as the objective coefficients depend on the number of productive resources (number of locomotives and handling mechanisms at the stations) and parameters of demand for transport services, computer simulations should be used for estimation of the objective function. The paper briefly describes the developed software for simulations of railway transport systems and solving the presented linear programming problem.
Method to Decompose Regional Travel Demand Model-Case Study of Kraków Region

Kucharski, Rafał, Kulpa, Tomasz, Mielczarek, Justyna, and Drabicki, Arkadiusz

In Directions of Development of Transport Networks and Traffic Engineering: 15th Scientific and Technical Conference" Transport Systems. Theory and Practice 2018" 2019

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In this paper we propose a new approach to model regional travel demand with a four-step model. Based on a comprehensive travel survey results for the Małopolska region in Poland (over 3mln inhabitants) we analysed the demand model with specific focus on regional trips. As a result, we introduce a modified model structure where long-distance trips are exposed. We breakdown the demand to four demand strata of various destination types to improve regional demand model quality. Such decomposition allows better representation of regional travel demand as compared to the classical four-step approach.
Modelowanie skutków zjawiska wzbudzenia (tłumienia) ruchu w analizach efektywności miejskich inwestycji drogowych

Drabicki, Arkadiusz, Szarata, Andrzej, and Kucharski, Rafał

2019
AS

A dynamic, cost-aware, optimized maintenance policy for interactive exploration of linked data

Akhtar, Usman, Sant’Anna, Anita, and Lee, Sungyoung

Applied Sciences 2019

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Vast amounts of data, especially in biomedical research, are being published as Linked Data. Being able to analyze these data sets is essential for creating new knowledge and better decision support solutions. Many of the current analytics solutions require continuous access to these data sets. However, accessing Linked Data at query time is prohibitive due to high latency in searching the content and the limited capacity of current tools to connect to these databases. To reduce this overhead cost, modern database systems maintain a cache of previously searched content. The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated. To overcome this challenge, we propose a Change-Aware Maintenance Policy (CAMP) for updating cached content. We propose a Change Metric that quantifies the evolution of a Linked Dataset and determines when to update cached content. We evaluate our approach on two datasets and show that CAMP can reduce maintenance costs, improve maintenance quality and increase cache hit rates compared to standard approaches.

2018

Change-aware scheduling for effectively updating linked open data caches

Akhtar, Usman, Razzaq, Muhammad Asif, Rehman, Ubaid Ur, Amin, Muhammad Bilal, Khan, Wajahat Ali, Huh, Eui-Nam, and Lee, Sungyoung

IEEE Access 2018

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The linked open data (LOD) cloud is a global information space with a wealth of structured facts, which are useful for a wide range of usage scenarios. The LOD cloud handles a large number of requests from applications consuming the data. However, the performance of retrieving data from LOD repositories is one of the major challenge. Overcome with this challenge, we argue that it is advantageous to maintain a local cache for efficient querying and processing. Due to the continuous evolution of the LOD cloud, local copies become outdated. In order to utilize the best resources, improvised scheduling is required to maintain the freshness of the local data cache. In this paper, we have proposed an approach to efficiently capture the changes and update the cache. Our proposed approach, called application-aware change prioritization (AACP), consists of a change metric that quantifies the changes in LOD, and a weight function that assigns importance to recent changes. We have also proposed a mechanism to update policies, called preference-aware source update (PASU), which incorporates the previous estimation of changes and establishes when the local data cache needs to be updated. In the experimental evaluation, several state-of-the-art strategies are compared against the proposed approach. The performance of each policy is measured by computing the precision and recall between the local data cache update using the policy under consideration and the data source, which is the ground truth. Both cases of a single update and iterative update are evaluated in this study. The proposed approach is reported to outperform all the other policies by achieving an F1-score of 88% and effectivity of 93.5%.
TMiR

Metoda aktualizacji modelu podróży z wykorzystaniem macierzy przemieszczeń telefonów komórkowych

Kucharski, Rafał, Mielczarek, Justyna, Drabicki, Arkadiusz, and Szarata, Andrzej

Transport Miejski i Regionalny 2018

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W artykule prezentujemy metodę aktualizacji modelu podróży z użyciem macierzy przemieszczeń telefonów komórkowych. Proponujemy rozwiązanie dla sytuacji, gdy model popytu przestaje być aktualny, a jednocześnie dostępne są duże zbiory danych o faktycznych przemieszczeniach. Jako że budowa modelu popytu jest pracochłonna i kosztowna, a duże zbiory danych coraz bardziej dostępne, wskazane jest opracowanie metody aktualizacji. Jest to jednak problematyczne. Po pierwsze szczegółowość podziału obszaru na rejony jest zazwyczaj większa niż ta dostępna dla macierzy komórkowych, po drugie model popytu zawiera pełny opis mobilności (ruchliwość, motywacje, modele wyboru celu, środka podróży i trasy), a macierze komórkowe zawierają jedynie wielkości podróży. W niniejszym artykule proponujemy metodę wykorzystującą zalety obydwu źródeł danych. W szczególności korzystamy z formuł generacji ruchu, zmiennych objaśniających i struktury motywacji ujawnionych w badaniach, na podstawie których sformułowano model podróży. Dopełniamy te dane dwustopniowym modelem wyboru celu podróży opracowanym na podstawie struktury przemieszczeń telefonów komórkowych. Uzyskane wielkości podróży kalibrujemy do wielkości ujawnionych w macierzach, a następnie potoki pasażerów i pojazdów kalibrujemy do wyników uzyskanych w pomiarach. Propozycja ta nie eliminuje wszystkich problemów i niejasności, ale niewątpliwe pozwala na pełniejsze wykorzystanie tego cennego źródła danych. Metodę ilustrujemy wynikami aktualizacji małopolskiego modelu ruchu z użyciem macierzy przemieszczeń między powiatami całej Polski klientów jednego z operatorów. W efekcie model podróży oparty na zdezaktualizowanych wynikach badań został zaktualizowany do aktualnej struktury przemieszczeń i wielkości podróży, ale i sam opis mobilności stał się dokładniejszy. Dzięki wydzieleniu na etapie generacji ruchu liczby podróży: a) do Krakowa, b) do Tarnowa lub Nowego Sącza, c) międzypowiatowych d) wewnątrzpowiatowych e) zewnątrzwojewódzkich udało się uzyskać obraz podróży regionalnych zgodny z rzeczywistym.
ATST

Trip volume seasonal variations at regional level – case study of Małopolska GSM OD matrices

Kucharski, R., Szarata, A., Mielczarek, J., and Drabicki, A.

Archives of Transport System Telematics 2018

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In this paper we analyze the big-data set of GSM origin-destination matrices collected between 360 districts (Powiat) in Poland to indirectly observe the trip volume fluctuations: weekly and seasonal. We utilized the dataset obtained from BTS position for all clients registered to a single provider. The data was collected over several days, which allows for a valuable temporal analysis of trip volumes. We analyze internal and external (inbound and outbound) trips in Małopolska region, intra-zonal trips (within district), inter-zonal (between districts of Małopolska). We discuss the general variability of observed trip volumes. We present the weekly fluctuations (working day, Friday, Saturday, Sunday) and the seasonal ones (winter, holiday, etc.). We verify the results obtained from GSM data with the traffic counts and their seasonal variations provided by national road administration (GDDKiA). Main contribution of the paper is presenting the observed fluctuations from the GSM data and comparing them with the classically collected data, as we demonstrate the results are comparable.
TMiR

Analiza symulacyjna postulowanych zmian w ciągu III oraz II obwodnicy Krakowa i ich oddziaływania na układ drogowy miasta pod kątem potencjalnego zjawiska ruchu wzbudzonego

Drabicki, Akradiusz, Szarata, Andrzej, and Kucharski, Rafał

Transport Miejski i Regionalny 2018
Analiza symulacyjna etapowej realizacji zachodnich odcinków III obwodnicy Krakowa i ich oddziaływania na układ transportowy

Drabicki, Arkadiusz, Szarata, Andrzej, and Kucharski, Rafał

Przegląd Komunikacyjny = Transportation Overview 2018

2017

Zjawisko bus bunching w transporcie zbiorowym i jego odwzorowanie symulacyjne

Drabicki, Arkadiusz, Kucharski, Rafał, and Szarata, Andrzej

2017
TMiR

Paradoksy przepustowości miejskiej sieci drogowej i sposoby ich odwzorowania w modelu czterostadiowym

Paszkowski, Jan, and Kucharski, Rafał

Transport miejski i regionalny 2017
Real-time traffic forecasting with recent DTA methods

Kucharski, R., Kostic, B., and Gentile, G.

2017

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In this paper we revisit the real-time traffic forecasting problem. We review recently proposed Dynamic Traffic Assignment (DTA) methods and verify how they can improve the practice of traffic forecasting. In particular, we analyze: 1) the Gradient projection DTA model of Gentile (2016), 2) Day-to-day model by Watling and Cantarella (2016), 3) the Marginal Computation (MaC) method by Corthout et al. (2014), 4) dynamic origin-destination (O-D) demand estimation methods (Kostic and Gentile, 2015) and 5) the event rerouting model (Kucharski and Gentile, 2014). We discuss how these methods can be applied to improve short-term forecasting and, most importantly, if they are efficient and mature enough for practical, real-time implementations. We formulate the real-time DTA forecasting problem which searches for the solution using all of the above DTA methods. The main contribution of this paper can be seen as a review and synthesis of recently proposed DTA methods, summarized with conceptual real-time forecasting framework.
Simulating the effects of real-time crowding information in public transport networks

Drabicki, A., Kucharski, R., Cats, O., and Fonzone, A.

2017
Training neural networks to approximate traffic simulation outcomes

Gora, Paweł, and Bardoński, Marek

In 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) 2017

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We present results of our research on training neural networks to approximate traffic simulation outcomes, such as total times of waiting on a red signal. We developed TensorTraffic software, based on a TensorFlow library, and trained neural networks on a dataset generated by simulating traffic on a realistic road network of Warsaw using Traffic Simulation Framework software. The goal of conducted experiments was to approximate the total times of waiting on a red signal on a region of Warsaw (Stara Ochota district), with the input to neural nets representing offsets of traffic signals on that region. In the presented research, we focused on investigating different neural network models and strategies of their training. We took into account different sizes of training sets, different numbers of neurons and layers, different parameters of dropout and learning rate, in order to reduce as much as possible time required to conduct experiments and apply this method in practice (e.g., time required to generate training and test sets for neural networks, time to train neural networks and time to make inferences on a new set), while preserving a sufficient accuracy of approximations, which may be especially important from a practical point of view Results show that it is possible to train neural networks able to approximate with a high accuracy (with an average error 1.18%) outcomes of traffic simulations. Moreover, TensorTraffic allows obtaining results of approximations a few orders of magnitude faster than by running simulations using microscopic traffic models and it is possible to achieve acceptable accuracy on a relatively small training set (consisting of 10240 elements). It means that the method can be potentially applied to different traffic analysis and transport planning tasks (e.g., to find suboptimal configurations of traffic signals) and time-consuming computer simulations applied nowadays for traffic analysis can be potentially replaced by neural nets supported by computations using graphical processing units (GPU). This innovation may significantly reduce time required to complete research and engineering tasks related to designing road infrastructure and analysing vehicular traffic, as well as enable developing better traffic management systems. As an example, we applied the method to the traffic signal setting problem and accelerated existing genetic algorithm, giving opportunity to evaluate much larger set of possible settings and find better traffic management strategies.
mlCAF: Multi-level cross-domain semantic context fusioning for behavior identification

Razzaq, Muhammad Asif, Villalonga, Claudia, Lee, Sungyoung, Akhtar, Usman, Ali, Maqbool, Kim, Eun-Soo, Khattak, Asad Masood, Seung, Hyonwoo, Hur, Taeho, Bang, Jaehun, and others,

Sensors 2017

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The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts.
AoT

Modelling the public transport capacity constraints impact on passenger path choices in transit assignment models

Drabicki, A., Kucharski, R., and Szarata, A.

Archives of Transport 2017
J. Hyperbolic Differ. Equ.

On measures of accretion and dissipation for solutions of the Camassa-Holm equation

Jamróz, Grzegorz

Journal of Hyperbolic Differential Equations 2017

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We investigate the measures of dissipation and accretion related to the weak solutions of the Camassa–Holm equation. Demonstrating certain novel properties of nonunique characteristics, we prove a new representation formula for these measures and conclude about their structural features, such as the fact that they are singular with respect to the Lebesgue measure. We apply these results to gain new insights into the structure of weak solutions, proving in particular that measures of accretion vanish for dissipative solutions of the Camassa–Holm equation.
J. Differ. Equ.

Transfer of energy in Camassa-Holm and related models by use of nonunique characteristics

Jamróz, Grzegorz

Journal of Differential Equations 2017

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We study the propagation of energy density in finite-energy weak solutions of the Camassa–Holm and related equations. Developing the methods based on generalized nonunique characteristics, we show that the parts of energy related to positive and negative slopes are one-sided weakly continuous and of bounded variation, which allows us to define certain measures of dissipation of both parts of energy. The result is a step towards the open problem of uniqueness of dissipative solutions of the Camassa–Holm equation.
JAT

Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows

Kucharski, Rafał, and Drabicki, Arkadiusz

Journal of Advanced Transportation 2017

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This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit from of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations.