Publications

@inproceedings{Luckner2024,
title = {Analysing Urban Transport Using Synthetic Journeys},
author = {Marcin Luckner and Przemysław Wrona and Maciej Grzenda and Agnieszka Łysak},
url = {http://dx.doi.org/10.1007/978-3-031-63783-4_10},
doi = {10.1007/978-3-031-63783-4_10},
issn = {16113349},
year = {2024},
date = {2024-01-01},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14838 LNCS},
pages = {118--132},
publisher = {Springer Nature Switzerland},
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.},
keywords = {public transport, synthetic journeys, Travel mode choice},
pubstate = {published},
tppubtype = {inproceedings}
}