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Title:

Detection of High-risk Segments of Traffic Incidents on Freeway Networks by Multi-Kernel Density Estimation and Spatial Analysis

Accession Number:

01764337

Record Type:

Component

Abstract:

Traffic incidents on freeways cause a considerable loss of life and property. Some traffic operation organizations provide freeway safety services to improve the roadway’s safety condition by assisting in the detection and clearance of incidents. To offer assistance in time, the traffic operation centers generally use patrol vehicles to cover the freeway networks. However, the risk of an incident occurring may differ considerably among road segments in the networks. By giving these high-risk segments more resources, the efficiency of the freeway safety service may increase. Therefore, it is essential to recognize the road segments having higher incident risks among the whole freeway network. This research aims at providing a method of detecting the road segments with a higher risk of traffic incidents. The risk will be considered from both spatial and temporal factors by implementing the multi-kernel density estimation method. The statistics of spatial analysis will be applied to evaluate the detection results.

Supplemental Notes:

This paper was sponsored by TRB committee AED60 Standing Committee on Statistical Methods.

Report/Paper Numbers:

TRBAM-21-04272

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Zhang, Binya
He, Qinglian

Pagination:

14p

Publication Date:

2021

Conference:

Transportation Research Board 100th Annual Meeting

Location: Washington DC, United States
Date: 2021-1-5 to 2021-1-29
Sponsors: Transportation Research Board; Transportation Research Board

Media Type:

Digital/other

Features:

Figures; Maps; References; Tables

Identifier Terms:

CHART (Program : Maryland)

Geographic Terms:

Maryland

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors

Source Data:

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-04272

Files:

TRIS, TRB, ATRI

Created Date:

Dec 23 2020 11:26AM