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Dest-ResNet: A Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction

Published: 15 October 2018 Publication History

Abstract

With the ever-increasing urbanization process, the traffic jam has become a common problem in the metropolises around the world, making the traffic speed prediction a crucial and fundamental task. This task is difficult due to the dynamic and intrinsic complexity of the traffic environment in urban cities, yet the emergence of crowd map query data sheds new light on it. In general, a burst of crowd map queries for the same destination in a short duration (called "hotspot'') could lead to traffic congestion. For example, queries of the Capital Gym burst on weekend evenings lead to traffic jams around the gym. However, unleashing the power of crowd map queries is challenging due to the innate spatiotemporal characteristics of the crowd queries. To bridge the gap, this paper firstly discovers hotspots underlying crowd map queries. These discovered hotspots address the spatiotemporal variations. Then Dest-ResNet (Deep spatiotemporal Residual Network) is proposed for hotspot traffic speed prediction. Dest-ResNet is a sequence learning framework that jointly deals with two sequences in different modalities, i.e., the traffic speed sequence and the query sequence. The main idea of Dest-ResNet is to learn to explain and amend the errors caused when the unimodal information is applied individually. In this way, Dest-ResNet addresses the temporal causal correlation between queries and the traffic speed. As a result, Dest-ResNet shows a 30% relative boost over the state-of-the-art methods on real-world datasets from Baidu Map.

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      cover image ACM Conferences
      MM '18: Proceedings of the 26th ACM international conference on Multimedia
      October 2018
      2167 pages
      ISBN:9781450356657
      DOI:10.1145/3240508
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      Published: 15 October 2018

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      Author Tags

      1. crowd map query
      2. lstm
      3. social media
      4. traffic speed prediction

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      Funding Sources

      • National Natural Science Foundation of China
      • National Basic Research Program of China
      • Key Program of Zhejiang Province, China
      • Chinese Knowledge Center for Engineering Sciences and Technology
      • Fundamental Research Funds for the Central Universities, China

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      MM '18
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      MM '18: ACM Multimedia Conference
      October 22 - 26, 2018
      Seoul, Republic of Korea

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      MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;
      Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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      • (2023)ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at ScaleProceedings of the ACM on Management of Data10.1145/35889411:1(1-28)Online publication date: 30-May-2023
      • (2023)MOHP-EC: A Multiobjective Hierarchical Prediction Framework for Urban Rail Transit Passenger FlowIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2023.324246515:4(86-105)Online publication date: Jul-2023
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