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Journal of Intelligent and Connected Vehicles 2021, 4(2): 80-91 https://doi.org/10.1108/JICV-03-2021-0004
Research paper | Open Access | Issue | Published: 25 August 2021

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Show Author's Information Weiwei Zhu1,  Jinglin Wu2,  Ting Fu3( ),  Junhua Wang3,  Jie Zhang3,  Qiangqiang Shangguan3
Shanghai Municipal Road Transport Development Center, Shanghai, China
Institute of Urban Risk Management, Tongji University, Shanghai, China
College of Transportation Engineering, Tongji University, Shanghai, China
Keywords:
Deep learning, Long short-term memory, Prediction of traffic incident duration, Multi-layer perception
Cite this article:
Zhu W, Wu J, Fu T, et al. Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP. Journal of Intelligent and Connected Vehicles, 2021, 4(2): 80-91. https://doi.org/10.1108/JICV-03-2021-0004
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Abstract Full text About this article
Purpose

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

Design/methodology/approach

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Findings

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

Research limitations/implications

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

Practical implications

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

Originality/value

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.


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Abstract
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About this article

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Show Author's information Weiwei Zhu1, Jinglin Wu2, Ting Fu3( ), Junhua Wang3, Jie Zhang3, Qiangqiang Shangguan3
Shanghai Municipal Road Transport Development Center, Shanghai, China
Institute of Urban Risk Management, Tongji University, Shanghai, China
College of Transportation Engineering, Tongji University, Shanghai, China

Abstract

Purpose

Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps.

Design/methodology/approach

This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.

Findings

Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction.

Research limitations/implications

The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations.

Practical implications

The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.

Originality/value

This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Keywords: Deep learning, Long short-term memory, Prediction of traffic incident duration, Multi-layer perception

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Publication history
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Publication history

Received: 21 March 2021
Revised: 06 July 2021
Accepted: 10 July 2021
Published: 25 August 2021
Issue date: September 2021

Copyright

© 2021 Weiwei Zhu, Jinglin Wu, Ting Fu, Junhua Wang, Jie Zhang and Qiangqiang Shangguan. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

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