|Table of Contents|

Speed prediction by online map-based GCN-LSTM neural network(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年04期
Page:
183-196
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Speed prediction by online map-based GCN-LSTM neural network
Author(s):
CHEN Hua-wei1;  SHAO Yi-ming1; 2;  AO Gu-chang1; 2;  ZHANG Hui-ling1; 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)
Keywords:
traffic engineering;  speed prediction;  GCN-LSTM neural network;  online map speed;  deep learning;  spatio-temporal feature
PACS:
U491.1
DOI:
10.19818/j.cnki.1671-1637.2021.04.014
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.

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Last Update: 2021-09-01