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Inferring Traffic Cascading Patterns

Published: 07 November 2017 Publication History

Abstract

There is an underlying cascading behavior over road networks. Traffic cascading patterns are of great importance to easing traffic and improving urban planning. However, what we can observe is individual traffic conditions on different road segments at discrete time intervals, rather than explicit interactions or propagation (e.g., A→B) between road segments. Additionally, the traffic from multiple sources and the geospatial correlations between road segments make it more challenging to infer the patterns. In this paper, we first model the three-fold influences existing in traffic propagation and then propose a data-driven approach, which finds the cascading patterns through maximizing the likelihood of observed traffic data. As this is equivalent to a submodular function maximization problem, we solve it by using an approximate algorithm with provable near-optimal performance guarantees based on its submodularity. Extensive experiments on real-world datasets demonstrate the advantages of our approach in both effectiveness and efficiency.

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cover image ACM Conferences
SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2017
677 pages
ISBN:9781450354905
DOI:10.1145/3139958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 November 2017

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Author Tags

  1. Spatio-temporal Data Mining
  2. Urban Computing

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SIGSPATIAL '17 Paper Acceptance Rate 39 of 193 submissions, 20%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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  • (2024)Multilevel Visual Analysis of Aggregate Geo-NetworksIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.322995330:7(3135-3150)Online publication date: Jul-2024
  • (2024)Early Detection of Driving Maneuvers for Proactive Congestion Prevention2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494436(135-142)Online publication date: 11-Mar-2024
  • (2024)Multi-Dimensional Threshold Model With Correlation: Emergence of Global CascadesICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622572(533-538)Online publication date: 9-Jun-2024
  • (2023)Discovering Causes of Traffic Congestion via Deep Transfer ClusteringACM Transactions on Intelligent Systems and Technology10.1145/360481014:5(1-24)Online publication date: 11-Aug-2023
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  • (2023)Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313562135:12(12323-12336)Online publication date: 1-Dec-2023
  • (2023)Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory dataInternational Journal of Geographical Information Science10.1080/13658816.2023.217865337:5(1157-1179)Online publication date: 22-Feb-2023
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