Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

Cong, Haozhe; Chen, Cong; Lin, Pei-Sung; Zhang, Guohui; Milton, John; Yang, Zhi

doi:10.1177/0361198118796938

Cited by 19 publications

(13 citation statements)

References 28 publications

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“…Different statistical methods and machine learning methods have been applied in traffic incident duration prediction, including tree-based method (Lin et al , 2016; Weng et al , 2015), Bayesian classifier (Cong et al , 2018; Ozbay and Noyan, 2006; Zou et al , 2021), hazard-based method (Haule et al , 2019; Li et al , 2017; Li et al , 2015) and ANN (Lee et al , 2017). Among these methods, the accelerated failure time (AFT) model is the most widely used hazard-based method (Li et al , 2017; Li et al , 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Zhu¹,

et al. 2021

JICV

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Zhu¹,

et al. 2021

JICV

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“…Also, household income could have an association with PEV ownership, among other factors. The widely used approaches for developing the nodearc structure of the BN model are (1) using the expert knowledge base and (2) an analytical approach learning the structure from the available data set (19). Model development based on expert knowledge tends to introduce expert subjectivity.…”

Section: Methodsmentioning

confidence: 99%

Stated Preference Analysis of Automated Vehicles among California Residents Using Probabilistic Inferences

Salum

Kutela²,

Kitali

et al. 2021

Transportation Research Record: Journal of the Transportation R

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As technology advances, improvements in the way people travel also occur. In 2017, the National Renewable Energy Laboratory (NREL) conducted a travel survey in California to understand residents’ perception of several aspects of mobility. This study used the data collected by NREL to understand various factors associated with safety perceptions and acquisition of automated vehicles among California residents. Bayesian networks (BNs) were used to learn the probabilistic interrelationships between aspects of automated vehicles. The predicted probabilities for safety concerns about vehicles with full driving automation (FAVs), purchase of vehicles with auto-drive assistance, and purchase of FAVs were determined after learning the BN structure and parameters from the data. The study found a strong relationship between the acquisition of automated vehicles and vehicles with auto-drive assistance. The BN model predicted that residents who are interested in purchasing vehicles with auto-drive assistance also have about 95% likelihood of purchasing FAVs. Moreover, ridesharing, number of vehicles in the household, housing type, and plug-in electric vehicle (PEV) ownership are among other factors playing a great role in the acquisition and safety perception of automated vehicles. Residents who are currently participating in ridesharing and living in apartments are more likely to purchase vehicles with auto-drive assistance. Residents who either own a PEV or have three or more vehicles are more likely to have safety concerns about FAVs. Additionally, residents who do not have safety concerns about FAVs have about a 45% likelihood of purchasing them. These results could provide valuable user opinion information for vehicle developers and other stakeholders.

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“…H. Cong, et al [7], J. Tang, et al [8], and X. Li, et al [9] developed models to predict the duration of the incident. A. Khattak, et al, [10]…”

Section: Related Workmentioning

confidence: 99%

“…The incidents duration is generally defined as the time between incident occurrence and clearance of the roadway, which means it's the time elapsed from the occurrence of the incident until all proof of the incident has been removed from the incident scene. This time can be divided into three phases [3], [7]:…”

Section: Incident Durationmentioning

confidence: 99%

Predicting Incident Duration Based on Machine Learning Methods

2021

IJCCCE

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Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption of energy and air pollution. Therefore, it is essential to accurately estimate the duration of the incident to mitigate these effects. Traffic management center incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States collected during the period from January 2015 to January 2017, with a total of 352 incident records were used to develop incident duration estimation models. This paper investigated different machine learning (ML) methods for traffic incidents duration prediction. The attempted ML techniques include Support Vector Machine (SVM), Random Forest (RF), and Neural Network Multi-Layer Perceptron (MLP). Root mean squared error (RMSE) and Mean absolute error (MAE) were used to evaluate the performance of these models. The results showed that the performance of the models was comparable with SVM models slightly outperforms the RF, and MLP models in terms of MAE index, where MAE was 14.23 min for the best-performing SVM models. Whereas, in terms of the RMSE index, RF models slightly outperformed the other two models given RMSE of 18.91 min for the best-performing RF model. Index Terms— Incident Duration, Neural Network Multi-Layer Perceptron, Random Forest, Support Vector Machine.

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Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

Cited by 19 publications

References 28 publications

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

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

Stated Preference Analysis of Automated Vehicles among California Residents Using Probabilistic Inferences

Predicting Incident Duration Based on Machine Learning Methods

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