Speed prediction by online map-based GCN-LSTM neural network

CHEN Hua-wei; SHAO Yi-ming; AO Gu-chang; ZHANG Hui-ling

doi:10.19818/j.cnki.1671-1637.2021.04.014

Volume 21 Issue 4

Sep. 2021

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Article Navigation > Journal of Traffic and Transportation Engineering > 2021 > 21(4): 183-196

CHEN Hua-wei, SHAO Yi-ming, AO Gu-chang, ZHANG Hui-ling. Speed prediction by online map-based GCN-LSTM neural network[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 183-196. doi: 10.19818/j.cnki.1671-1637.2021.04.014

Citation:

CHEN Hua-wei, SHAO Yi-ming, AO Gu-chang, ZHANG Hui-ling. Speed prediction by online map-based GCN-LSTM neural network[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 183-196. doi: 10.19818/j.cnki.1671-1637.2021.04.014

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Speed prediction by online map-based GCN-LSTM neural network

doi: 10.19818/j.cnki.1671-1637.2021.04.014

CHEN Hua-wei^1
,,
SHAO Yi-ming^{1, 2
,},
AO Gu-chang^{1, 2},
ZHANG Hui-ling^{1, 2}

1.
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
2.
Chongqing Key Lab of Traffic System and Safety in Mountain Cities, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 51508061

Natural Science Foundation of Chongqing cstc2019jcyj-msxmX0786

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Author Bio:
CHEN Hua-wei(1993-), male, doctoral student, 15215135375@163.com

SHAO Yi-ming(1955-), male, professor, PhD, sym@cqjtu.edu.cn
Received Date: 2021-02-19
Available Online: 2021-09-16
Publish Date: 2021-08-01

Abstract

Abstract

Online map speeds of roads were collected by calling the path-planning application programming interface of the online map to completely extract the spatio-temporal features of the road speed from road network speed and then achieve high-precision road speed prediction. The spatial features were extracted using a graph convolutional network (GCN), and the temporal features were extracted using a long short-term memory (LSTM) neural network. An online map-based GCN-LSTM neural network was established, the spatio-temporal features of the road speed were extracted, and the road speed was predicted. The performance of the online map-based GCN-LSTM neural network was assessed, and the advantages of the online map-based GCN-LSTM neural network and the substitutability of the detector-based speed prediction model were evaluated. By using the local road network as an example, the performance of the model was analyzed, and the performances of different online map-based models and similar models with different data sources were compared. Analysis results show that the mean absolute errors(MAEs) of the GCN-LSTM neural network are lower than 5, the root mean square errors (RMSEs) are lower than 6, and the mean absolute percentage errors (MAPEs) are lower than 30% in the training and testing sets. Hence, the training and testing errors are low, indicating good comprehensive performance. The MAPE of the GCN-LSTM neural network of the roads follows a Gumbel distribution, whose mean ranges between 19%±4%, and the 85% quantile ranges between 34%±5%. Hence, both indexes are low, indicating good individual performance. Among the online map-based speed prediction models, the MAE, RMSE, MAPE, mean, and 85% quantile of the MAPE fitting curve of the GCN-LSTM neural network have the lowest values. Hence, its comprehensive and individual performances are the best, and it exhibits advantages in online map-based speed prediction. Among the similar models, the MAE, RMSE, MAPE, mean, and 85% quantile of the MAPE fitting curve of the GCN-LSTM neural network have the lowest values. Hence, its comprehensive and individual performances are the best. Furthermore, the reliability of online map-based speed prediction is high, so that it can be used as a substitute for detector-based speed prediction. 4 tabs, 13 figs, 30 refs.
- traffic engineering,
- speed prediction,
- GCN-LSTM neural network,
- online map speed,
- deep learning,
- spatio-temporal feature

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References(30)

References

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Speed prediction by online map-based GCN-LSTM neural network

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