Abstract
Travel mode choice models make it possible to learn under what conditions people decide to use different means of transport. Typically, such models are based on real trip records provided by respondents, e.g. city inhabitants. However, the question arises of how to scale the insights from an inevitably limited number of trips described in their travel diaries to entire cities.
To address the limited availability of real trip records, we propose the Urban Journey System integrating big data platforms, analytic engines, and synthetic data generators for urban transport analysis. First of all, the system makes it possible to generate random synthetic journeys linking origin and destination pairs by producing location pairs using an input probability distribution. For each synthetic journey, the system calculates candidate routes for different travel modes (car, public transport (PT), cycling, and walking). Next, the system calculates Level of Service (LOS) attributes such as travel duration, waiting time and distances involved, assuming both planned and real behaviour of the transport system. This allows us to compare travel parameters for planned and real transits.
We validate the system with spatial, schedule and GPS data from the City of Warsaw. We analyse LOS attributes and underlying vehicle trajectories over time to estimate spatio-temporal distributions of features such as travel duration, and number of transfers. We extend this analysis by referring to the travel mode choice model developed for the city.
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Notes
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Map data copyrighted by OpenStreetMap contributors and available from https://www.openstreetmap.org.
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While it is not the objective of this work to describe the process of model development, let us note that some further details on TMC modelling for the City of Warsaw that we rely on in this work can be found in [4].
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Acknowledgments
The research leading to these results has received funding from the EEA/Norway Grants 2014–2021 through the National Centre for Research and Development. CoMobility benefits from a 2.05 million€ grant from Iceland, Liechtenstein and Norway through the EEA Grants. The aim of the project is to provide a package of tools and methods for the co-creation of sustainable mobility in urban spaces.
Data made public by the City of Warsaw including schedules and GPS traces from the City Open Data portal (http://api.um.warszawa.pl) acquired and processed in the years 2022–2023 were used to develop public transport schedules. Further details on these data can be found on the Open Data portal.
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Luckner, M., Wrona, P., Grzenda, M., Łysak, A. (2024). Analysing Urban Transport Using Synthetic Journeys. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14838. Springer, Cham. https://doi.org/10.1007/978-3-031-63783-4_10
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