DeepTSP: Deep traffic state prediction model based on large-scale empirical data
Artikel i vetenskaplig tidskrift, 2021

Real-time traffic state (e.g., speed) prediction is an essential component for traffic control and management in an urban road network. How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict the traffic state of large-scale traffic systems. In this study, we first summarize the three challenges faced by large-scale traffic state prediction, i.e., scale, granularity, and sparsity. Based on the domain knowledge of traffic engineering, the propagation of traffic states along the road network is theoretically analyzed, which are elaborated in aspects of the temporal and spatial propagation of traffic state, traffic state experience replay, and multi-source data fusion. A deep learning architecture, termed as Deep Traffic State Prediction (DeepTSP), is therefore proposed to address the current challenges in traffic state prediction. Experiments demonstrate that the proposed DeepTSP model can effectively predict large-scale traffic states.

Large-scale traffic prediction

Spatio-temporal data

Traffic state propagation

Författare

Yang Liu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Southeast University

Cheng Lyu

Southeast University

Yuan Zhang

Southeast University

Zhiyuan Liu

Southeast University

Wenwu Yu

Southeast University

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Communications in Transportation Research

27724247 (eISSN)

Vol. 1 100012

Ämneskategorier

Transportteknik och logistik

Datavetenskap (datalogi)

Datorsystem

DOI

10.1016/j.commtr.2021.100012

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2024-01-03