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DOI: 10.4230/OASIcs.ATMOS.2024.18

Dynamic Traffic Assignment for Public Transport with Vehicle Capacities

Authors: Julian Patzner and Matthias Müller-Hannemann

Published in: OASIcs, Volume 123, 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024)

Abstract

Traffic assignment is a core component of many urban transport planning tools. It is used to determine how traffic is distributed over a transportation network. We study the task of computing traffic assignments for public transport: Given a public transit network, a timetable, vehicle capacities and a demand (i.e. a list of passengers, each with an associated origin, destination, and departure time), the goal is to predict the resulting passenger flow and the corresponding load of each vehicle. Microscopic stochastic simulation of individual passengers is a standard, but computationally expensive approach. Briem et al. (2017) have shown that a clever adaptation of the Connection Scan Algorithm (CSA) can lead to highly efficient traffic assignment algorithms, but ignores vehicle capacities, resulting in overcrowded vehicles. Taking their work as a starting point, we here propose a new and extended model that guarantees capacity-feasible assignments and incorporates dynamic network congestion effects such as crowded vehicles, denied boarding, and dwell time delays. Moreover, we also incorporate learning and adaptation of individual passengers based on their experience with the network. Applications include studying the evolution of perceived travel times as a result of adaptation, the impact of an increase in capacity, or network effects due to changes in the timetable such as the addition or the removal of a service or a whole line. The proposed framework has been experimentally evaluated with public transport networks of Göttingen and Stuttgart (Germany). The simulation proves to be highly efficient. On a standard PC the computation of a traffic assignment takes just a few seconds per simulation day.

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Julian Patzner and Matthias Müller-Hannemann. Dynamic Traffic Assignment for Public Transport with Vehicle Capacities. In 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024). Open Access Series in Informatics (OASIcs), Volume 123, pp. 18:1-18:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{patzner_et_al:OASIcs.ATMOS.2024.18,
  author =	{Patzner, Julian and M\"{u}ller-Hannemann, Matthias},
  title =	{{Dynamic Traffic Assignment for Public Transport with Vehicle Capacities}},
  booktitle =	{24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024)},
  pages =	{18:1--18:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-350-8},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{123},
  editor =	{Bouman, Paul C. and Kontogiannis, Spyros C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2024.18},
  URN =		{urn:nbn:de:0030-drops-212064},
  doi =		{10.4230/OASIcs.ATMOS.2024.18},
  annote =	{Keywords: Public transport, traffic assignment, vehicle capacities, crowding, stochastic simulation, learning}
}

Document

DOI: 10.4230/OASIcs.ATMOS.2022.13

Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport

Authors: Julian Patzner, Ralf Rückert, and Matthias Müller-Hannemann

Published in: OASIcs, Volume 106, 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022)

Abstract

Delays and disruptions are commonplace in public transportation. An important tool to limit the impact of severely delayed vehicles is the use of short turns, where a planned trip is shortened in order to be able to resume the following trip in the opposite direction as close to the schedule as possible. Short turns have different effects on passengers: some suffer additional delays and have to reschedule their route, while others can benefit from them. Dispatchers therefore need decision support in order to use short turns only if the overall delay of all affected passengers is positively influenced. In this paper, we study the planning of short turns based on passenger flows. We propose a simulation framework which can be used to decide upon single short turns in real time. An experimental study with a scientific model (LinTim) of an entire public transport system for the German city of Stuttgart including busses, trams, and local trains shows that we can solve these problems on average within few milliseconds. Based on features of the current delay scenario and the passenger flow we use machine learning to classify cases where short turns are beneficial. Depending on how many features are used, we reach a correct classification rate of more than 93% (full feature set) and 90% (partial feature set) using random forests. Since precise passenger flows are often not available in urban public transportation, our machine learning approach has the great advantage of working with significantly less detailed passenger information.

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Julian Patzner, Ralf Rückert, and Matthias Müller-Hannemann. Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport. In 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022). Open Access Series in Informatics (OASIcs), Volume 106, pp. 13:1-13:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{patzner_et_al:OASIcs.ATMOS.2022.13,
  author =	{Patzner, Julian and R\"{u}ckert, Ralf and M\"{u}ller-Hannemann, Matthias},
  title =	{{Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport}},
  booktitle =	{22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022)},
  pages =	{13:1--13:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-259-4},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{106},
  editor =	{D'Emidio, Mattia and Lindner, Niels},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2022.13},
  URN =		{urn:nbn:de:0030-drops-171171},
  doi =		{10.4230/OASIcs.ATMOS.2022.13},
  annote =	{Keywords: Public Transportation, Delays, Real-time Dispatching, Passenger Flows}
}

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Documents authored by Patzner, Julian

Dynamic Traffic Assignment for Public Transport with Vehicle Capacities

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Passenger-Aware Real-Time Planning of Short Turns to Reduce Delays in Public Transport

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