Travel Time Prediction of Urban Road Based on Deep Learning

Authors

Weiwei Zhang, 1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China;;2. National Road Traffic Management Engineering Technology Research Center, Beijing 100084, China;;
Ruimin Li, 1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China;;2. National Road Traffic Management Engineering Technology Research Center, Beijing 100084, China;;
Zhongjiao Xie, 3. Traffic Management Bureau of Shandong Public Security Bureau, Ji'nan 250031, China;

Abstract

Abstract: Travel time prediction of urban road is a significant support for urban intelligent transportation system. Four types of LSTM neural network architecture were selected to predict the urban road travel time. The number of nodes in the LSTM hidden layer was fixed to determine the optimal input length of the model. The input length of the model was fixed and the predictive performance of the four LSTM models under different hidden layer nodes and considering spatial correlation were tested respectively. The performance of spatial LSTM model was compared with four traditional models, for example, BP neural network. The results show that the LSTM model with spatial correlation has better fitting and training ability than the four traditional models.

Recommended Citation

Zhang, Weiwei; Li, Ruimin; and Xie, Zhongjiao (2020) "Travel Time Prediction of Urban Road Based on Deep Learning," Journal of System Simulation: Vol. 29: Iss. 10, Article 11.
DOI: 10.16182/j.issn1004731x.joss.201710011
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss10/11

First Page

2309

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201710011

Last Page

2316

CLC

TP391

Recommended Citation

Zhang Weiwei, Li Ruimin, Xie Zhongjiao. Travel Time Prediction of Urban Road Based on Deep Learning[J]. Journal of System Simulation, 2017, 29(10): 2309-2316.

Corresponding Author

Ruimin Li,

DOI

10.16182/j.issn1004731x.joss.201710011