Abstract
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.
Highlights
Traffic congestion arises when the traffic demand on the highway surpasses its usable capacity
Incident duration prediction models were developed by using Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) methods
Traffic incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States were collected over two years with a total of 352 incidents
Summary
Traffic congestion arises when the traffic demand on the highway surpasses its usable capacity. Non-recurrent traffic congestion is caused by unplanned events on the highway such as incidents, stranded vehicles, public manifestations, weather, and work zones. Since highway work zones associated with patching, flooring, lane marking, rubble removal, and weeding are followed by temporary reduction of capacity on the highway, and the congestion caused by them can be extremely high portion of the whole traffic congestion [1], [2]. Non-recurrent congestion is difficult to predict as a result of its random nature, the researches on impact and duration of the traffic incidents are still one of the main focuses for the traffic operators due to the serious social and economic losses generated [3]. The purpose of this study is to investigate various machine learning methods to predict the duration of the incident
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