Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model

Kai Zhang, Zixuan Chu, Jiping Xing, Honggang Zhang and Qixiu Cheng ()
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Kai Zhang: School of Transportation, Southeast University, Nanjing 211189, China
Zixuan Chu: School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou 215400, China
Jiping Xing: Xingmin Intelligent Transportation Systems (Group) Co., Ltd., Yantai 265716, China
Honggang Zhang: School of Transportation, Southeast University, Nanjing 211189, China
Qixiu Cheng: School of Transportation, Southeast University, Nanjing 211189, China

Mathematics, 2023, vol. 11, issue 19, 1-20

Abstract: Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

Keywords: congestion analysis; traffic flow prediction; spatial-temporal model; deep learning model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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