Travel time prediction with LSTM neural network

Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings (IEEE Conference on Intelligent Transportation Systems) pp. 1053 - 1058
Main Authors: Duan, Yanjie, L.V., Yisheng, Wang, Fei-Yue
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2016
Subjects:
Logic gates
Machine learning
Predictive models
Real-time systems
Recurrent neural networks
Roads
ISSN:2153-0017
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural network model, for travel time prediction. By employing the travel time data provided by Highways England, we construct 66 series prediction LSTM neural networks for the 66 links in the data set. Through model training and validation, we obtain the optimal structure within the setting range for each link. Then we predict multi-step ahead travel times for each link on the test set. Evaluation results show that the 1-step ahead travel time prediction error is relatively small, the median of mean relative error for the 66 links in the experiments is 7.0% on the test set. Deep learning models considering sequence relation are promising in traffic series data prediction.
ISSN:2153-0017
DOI:10.1109/ITSC.2016.7795686