arXiv subjects

Rafał Kucharski

Publications and source records attributed to Rafał Kucharski.

23 records · Page 2Linked to original sources

arXiv2022

Exploring shareability networks of probabilistic ride-pooling problems

Travellers sharing rides in ride-pooling systems form various kinds of networks. While the notions of the so-called shareability graphs, has been in the core of many ride-pooling algorithms, so far they have not been explicitly analysed. Here, we introduce and examine four kinds of networks resulting from ride-pooling problems. We use 147 NYC taxi requests from 2016 pooled into attractive shared-rides with our utility based ExMAS algorithm and explore resulting shareability networks. To cover unknown individual properties of pooling travellers, we run 1000 replications of probabilistic pooling process, resulting in richer representation of weighted graphs instrumental to reveal complex network structures. Our findings reveal substantial differences between network structures and topologies. Properties of ride-pooling networks may be further analysed to better understand and improve ride-pooling systems.

physics.soc-ph↗

arXiv2021

Ride-pooling potential under alternative spatial demand patterns

Shared rides are often considered to be a promising travel alternative that could efficiently pool people together while offering a door-to-door service. Notwithstanding, even though demand distribution patterns are expected to greatly affect the potential for ride-pooling, their impact remains unknown. In this study we explore the shareability of various demand patterns. We devise a set of experiments tailored to identify the most promising demand patterns for introducing ride-pooling services by varying the number of centers, the dispersion of destinations around each of these centers and the trip length distribution. When matching trips into rides, we do not only ensure their mutual compatibility in time and space but also that shared rides are only composed by travellers who find the ride-pooling offer to be more attractive than the private ride-hailing alternative given the trade-offs between travel time, fare and discomfort. We measure the shareability potential using a series of metrics related to the extent to which passenger demand can be assigned to shared rides. Our findings indicate that introducing a ride-pooling service can reduce vehicle-hours by 18-59% under a fixed demand level and depending on the concentration of travel destinations around the center and the trip length distribution. System efficiency correlates positively with the former and negatively with the latter. A shift from a monocentric to a polycentric demand pattern is found to have a limited impact on the prospects of shared rides.

physics.soc-ph↗

arXiv2021

Beyond the Dichotomy: How Ride-hailing Competes with and Complements Public Transport

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 that 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.

physics.soc-ph↗

arXiv2021

Modelling virus spreading in ride-pooling networks

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.

physics.soc-ph↗

arXiv2020

MaaSSim -- agent-based two-sided mobility platform simulator

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape, bringing disruptive changes to transportation systems worldwide. This calls for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the dynamics of platform-driven urban mobility systems. In this work, we present MaaSSim, an agent-based simulator reproducing the transport system used by two kind 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, allows demand to be matched with supply. Agents are decision makers, specifically, travellers may decide which mode they use or reject an incoming offer. Similarly, drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, representing agents' taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is an open-source library available at a public repository github.com/RafalKucharskiPK/MaaSSim, along with a set of tutorials and reproducible use-case scenarios.

cs.MA↗