Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Haoyi Xiong; Xun Zhou; David A. Bennett

doi:10.1080/13658816.2023.2178653

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Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Journal article

Open access

Peer reviewed

Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Haoyi Xiong, Xun Zhou and David A. Bennett

International journal of geographical information science : IJGIS, Vol.37(5), pp.1157-1179

2023

DOI: 10.1080/13658816.2023.2178653

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https://figshare.com/articles/journal_contribution/Detecting_spatiotemporal_propagation_patterns_of_traffic_congestion_from_fine-grained_vehicle_trajectory_data/22140290View

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Abstract

Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis.

network dynamics

Spatiotemporal representation of event propagation

traffic congestion

Details

Title: Subtitle: Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data
Creators: Haoyi Xiong - University of Iowa
Xun Zhou - University of Iowa
David A. Bennett - University of Iowa
Resource Type: Journal article
Publication Details: International journal of geographical information science : IJGIS, Vol.37(5), pp.1157-1179
DOI: 10.1080/13658816.2023.2178653
ISSN: 1365-8816
eISSN: 1365-8824
Publisher: Taylor & Francis
Grant note: name: U.S. Department of Transportation’s University Transportation Center Program, award: 31
Language: English
Electronic publication date: 02/22/2023
Date published: 2023
Academic Unit: Business Analytics; Geographical and Sustainability Sciences
Record Identifier: 9984368432502771

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