Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (8): 2214-2222.doi: 10.13229/j.cnki.jdxbgxb.20221386

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TrafficPro: a framework to predict link speeds on signalized urban traffic network

Xiao-yue WEN1(),Guo-min QIAN2,3,Hua-hua KONG2,Yue-jie MIU2,Dian-hai WANG1()   

  1. 1.Intelligent Transportation Research Institute,Zhejiang University,Hangzhou 310058,China
    2.Enjoyor Technology Co. ,Ltd. ,Hangzhou 310023,China
    3.College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2022-10-31 Online:2024-08-01 Published:2024-08-30
  • Contact: Dian-hai WANG E-mail:wenxiaoyue@163.com;wangdianhai@zju.edu.cn

Abstract:

When the traditional deep learning-based models predict link speeds for the entire urban traffic network, they do not consider the proactive feature (signal control information) of traffic flow, and therefore achieve low prediction accuracy. In order to tackle this issue, this paper proposed a link speed prediction framework, based on generative adversarial network and graph neural network. By adopting a proactive and a reactive prediction module, the generator of this framework is able to encode traffic flow and signal control information at the entire network level. The discriminator is then used to increase the generalizability of the prediction outcome. By comparing its performance with traditional time-series and deep learning-based models in real-world traffic circumstances, it is found that the proposed framework achieved less prediction error (3%-5% RMSE drop) than the SOTA model (ASTGCN).

Key words: transportation planning and management, signalized urban traffic network, traffic speed prediction, generative adversarial network

CLC Number: 

  • U491

Fig.1

Details of TrafficPro framework"

Fig.2

Case study area"

Table 1

Example of speed, signal and signal code on one road"

路段ID时刻平均速度/(km?h-1

信控数据

/s

信控独热

编码向量

001

2020-01-01

08:00:00

28.230[0,1,0,…,0]
001

2020-01-01

08:05:00

31.530[0,1,0,…,0]
001

2020-01-01

08:10:00

30.630[0,1,0,…,0]
001

2020-01-01

08:15:00

24.530[0,1,0,…,0]
001

2020-01-01

08:20:00

22.325[1,0,0,…,0]
001

2020-01-01

08:25:00

23.925[1,0,0,…,0]
001

2020-01-01

08:30:00

21.725[1,0,0,…,0]

Table 2

Prediction errors for nine models (3 steps)"

模型RMSEMAEMAPE/%
HA8.877.6121.8
SVR8.637.3216.5
ARIMA6.025.1814.8
GCN6.946.4611.6
GRU6.485.3411.2
T-GCN5.604.4210.7
ST-GCN5.273.889.5
ASTGCN4.753.439.1
TrafficPro(G)4.213.118.9
TrafficPro4.052.998.6

Table 3

Prediction errors for nine models (6 steps)"

模型RMSEMAEMAPE/%
HA11.49.1425.7
SVR10.58.7219.8
ARIMA7.116.7917.5
GCN8.547.7813.2
GRU6.885.8712.4
T-GCN5.914.8611.5
ST-GCN5.454.419.8
ASTGCN5.063.679.6
TrafficPro(G)4.543.409.4
TrafficPro4.403.218.9

Table 4

Prediction errors for nine models (12 steps)"

模型RMSEMAEMAPE/%
HA16.512.033.9
SVR14.311.623.5
ARIMA9.749.2720.1
GCN9.819.1915.4
GRU8.537.2714.0
T-GCN6.525.6212.8
ST-GCN5.864.9910.9
ASTGCN5.263.8310.3
TrafficPro(G)4.843.529.8
TrafficPro4.593.399.3

Fig.3

RMSE errors in different hours"

Fig.4

Comparisons between real speed and predicted speed on different roads"

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