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NeurIPS2022_traffic4cast_proceedings.pdf (3.43 MB)

Traffic4cast at NeurIPS 2022 – Predict dynamics along graph edges from sparse node data: Whole city traffic and ETA from stationary vehicle detectors

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conference contribution
posted on 2024-01-10, 17:14 authored by Moritz Neun, Christian Eichenberger, Henry Martin, Markus Spanring, Rahul Siripurapu, Daniel Springer, Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou, Martin Lumiste, Andrei Ilie, Xinhua Wu, Cheng Lyu, Qing-Long Lu, Vishal Mahajan, Yichao Lu, Jiezhang Li, Junjun Li, Yue-Jiao Gong, Florian Grötschla, Joël Mathys, Ye WeiYe Wei, Haitao HeHaitao He, Hui FangHui Fang, Kevin Malm, Fei Tang, Michael Kopp, David Kreil, Sepp Hochreiter
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future – super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.

History

School

  • Science
  • Architecture, Building and Civil Engineering

Department

  • Computer Science

Published in

Proceedings of Machine Learning Research (PMLR)

Volume

220

Pages

251 - 278

Source

NeurIPS 2022 Competition Track Program

Publisher

Proceedings of Machine Learning Research (PMLR)

Version

  • VoR (Version of Record)

Rights holder

© M. Neun et al.

Publisher statement

This is a conference proceeding published openly by Proceedings of Machine Learning Research (PMLR).

Publication date

2023-01-01

Copyright date

2023

ISSN

2640-3498

Language

  • en

Location

Virtual conference

Depositor

Dr Hui Fang. Deposit date: 24 December 2023

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