Citation

Tang J, Zheng L, Han C, Yin W, Zhang Y, Zou Y, Huang H. Anal. Meth. Accid. Res. 2020; 27: e100123.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.amar.2020.100123

PMID

unavailable

Abstract

Accurate clearance time prediction for road incident would be helpful to evaluate the incident impacting range and provide route guiding strategy according to the predicted results, and thus reduce the travel delays caused by incidents. Currently, a number of approaches have been developed for predicting incident clearance time and investigating the effects of influential factors. Statistical and machine learning methods are the two major methodological approaches. This study aims to make a methodology review for these methods by comprehensively examining their performance in incident clearance time prediction, especially, when omitted variables present significant impacts on selected variables. Specifically, we consider four widely used statistical models: Accelerated Failure Time (AFT) model, Quantile Regression (QR) model, Finite Mixture (FM) model, and Random Parameters Hazard-Based Duration (RPHD) model, and four machine learning models: K-Nearest Neighbor (KNN) model, Support Vector Machine (SVM) model, Back Propagation Neural Network (BPNN) model, and Random Forest (RF) model as candidates. Moreover, the abilities of these methods in uncovering the underlying causality (explaining the causal effects of significant influential factors on clearance time) are also investigated. Incident clearance time data was collected on freeway road sections in Seattle, Washington State from 2009 to 2011. The conclusions can be summarized as follows: 1) the RF model and RPHD model outperform the other three models in data fitting and model prediction in their respective methodological categories; 2) three "heterogeneity" methods including RPHD, FM and QR outperform machine learning methods in model prediction as measured by MAPE; 3) machine learning methods perform stably in model prediction relative to the statistical methods; 4) incident type and lane closure type present significant effects on incident clearance time in all eight selected models.

Language: en

Keywords

Clearance time prediction; Influence factor analysis; Machine learning methods; Road incidents; Statistical methods