Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3458-3464.doi: 10.13229/j.cnki.jdxbgxb.20221192

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Short⁃term traffic flow prediction algorithm for road network based on deep asynchronous residual network

Hai-long GAO1(),Yi-bo XU2,De-zao HOU2(),Xue-song WANG3   

  1. 1.Key Laboratory of Technology on Intelligent Transportation Systems，Research Institute of Highway，Ministry of Transport，Beijing 100088，China
    2.Key Laboratory of Technology on Intelligent Transportation Systems，Research Institute of Highway，Ministry of Transport，Beijing 100088，China
    3.School of Transportation Engineering，Tongji University，Shanghai 201804，China
  • Received:2022-09-15 Online:2023-12-01 Published:2024-01-12
  • Contact: De-zao HOU E-mail:ghl19650815@163.com;hdz0328@163.com

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Abstract:

The whole road network was divided into sub-networks that meet the requirements of short-term traffic forecast. Three temporal and spatial characteristics are extracted from the short-term traffic volume of the road network， namely time sequence， periodic similarity and spatial sequence， and residual units were added to convolutional neural networks to construct a deep asynchronous network model to realize the short-term traffic flow forecast of the road network. The experimental results show that the root mean square error and average absolute error of the proposed algorithm are small， the trend of traffic forecast value is almost consistent with the trend of real traffic value， and the traffic flow forecast performance is good.

Key words: intelligent transportation, deep asynchronous residual network, predictability of traffic flow, space time characteristics, convolutional neural network, residual unit

CLC Number: 

  • TP399

Fig.1

Structure diagram of relationship between generalized spatial distance and its influencing factors"

Fig.2

Residual unit diagram"

Table 1

Data division of traffic flow forecast"

数据集训练数据测试数据
南京Taxi4952456
杭州Bike3469269

Fig.3

Comparison of root mean square error values of various algorithms"

Fig.4

Comparison of the mean absolute error values of various algorithms"

Fig.5

Comparison of TaxiNJ traffic prediction values and various algorithms"

Fig.6

Comparison of BikeHZ traffic forecast value and various algorithms"

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