Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model

Jinjun Tang,Chunyang Han,Lanlan Zheng,Fang Liu,Jianming Cai

doi:10.1155/2020/6401082

Abstract

Accurate prediction and reliable significant factor analysis of incident clearance time are two main objects of traffic incident management (TIM) system, as it could help to relieve traffic congestion caused by traffic incidents. This study applies the extreme gradient boosting machine algorithm (XGBoost) to predict incident clearance time on freeway and analyze the significant factors of clearance time. The XGBoost integrates the superiority of statistical and machine learning methods, which can flexibly deal with the nonlinear data in high-dimensional space and quantify the relative importance of the explanatory variables. The data collected from the Washington Incident Tracking System in 2011 are used in this research. To investigate the potential philosophy hidden in data,K-means is chosen to cluster the data into two clusters. The XGBoost is built for each cluster. Bayesian optimization is used to optimize the parameters of XGBoost, and the MAPE is considered as the predictive indicator to evaluate the prediction performance. A comparative study confirms that the XGBoost outperforms other models. In addition, response time, AADT (annual average daily traffic), incident type, and lane closure type are identified as the significant explanatory variables for clearance time.

Highlights

According to Lindley [1], traffic incidents result in about 60% of nonrecurrent traffic congestions. ese congestions may cause lots of adverse effects such as reducing the roadway capacity, increasing the likelihood of secondary incidents [2], and unfavorable social and economic phenomenon [3]
Samples in the dataset with similar characteristics can be clustered into the same class by using K-means [48]. e data we used in this research are expressed as {xi [xi1, xi2, . . . , xim], yi}, i 1, 2, 3, . . . , n and n represents the number of incidents, m is the number of explanatory variables, and the y denotes the actual clearance time. e detailed steps of the K-means algorithm are presented as follows: Step 1: assuming the number of clusters (K clusters) and choosing the cluster centers from the dataset randomly
XGBoost is applied to predict incident clearance time that occurred on the freeway and investigates the significant factors of clearance time by using the data collected from the Washington Incident Tracking System in 2011

Summary

Introduction

According to Lindley [1], traffic incidents result in about 60% of nonrecurrent traffic congestions. ese congestions may cause lots of adverse effects such as reducing the roadway capacity, increasing the likelihood of secondary incidents [2], and unfavorable social and economic phenomenon [3]. Ese congestions may cause lots of adverse effects such as reducing the roadway capacity, increasing the likelihood of secondary incidents [2], and unfavorable social and economic phenomenon [3]. When a traffic incident occurred, timely and reliable incident duration prediction plays an important role in the traffic authorities to design strategy for traffic guidance. Ese approaches can be mainly categorized into statistical approaches and machine learning approaches. Statistical methods have their own model assumptions and predefined underlying relationships between dependent and independent variables [6] which provide the explainable ability to statistical methods. Machine learning methods are based on a more flexible mapping process that

Objectives

Methods

Results

Conclusion