Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Qinge Xie, Tiancheng Guo, Yang Chen, Yu Xiao, Xin Wang, Ben Zhao

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.
AlkuperäiskieliEnglanti
OtsikkoCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
KustantajaACM
Sivut1665-1674
Sivumäärä10
ISBN (elektroninen)9781450368599
DOI - pysyväislinkit
TilaJulkaistu - 19 lokakuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM International Conference on Information and Knowledge Management - Virtual, Online, Irlanti
Kesto: 19 lokakuuta 202023 lokakuuta 2020
Konferenssinumero: 29

Conference

ConferenceACM International Conference on Information and Knowledge Management
LyhennettäCIKM
MaaIrlanti
KaupunkiVirtual, Online
Ajanjakso19/10/202023/10/2020

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    29 syyskuuta 201912 lokakuuta 2019

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