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Title:

Modeling Freeway Incident Response Time: Mechanism-Based Approach

Accession Number:

01371099

Record Type:

Component

Abstract:

Freeway incident duration analysis and prediction is important for freeway congestion mitigation. The sooner an incident is responded to, the lower the negative impact from the incident is. Hence, response time is critical for incident management. Most previous studies on incident response time simply treated the incident response process as a black box and hence their findings were highly dependent on the proposed hypothesis and study sites. A more general analytical method is needed for response time analysis. To fill up the gap, we propose a mechanism based approach to model the incident response process and explore the contributing explanatory attributes in this paper. A typical incident response process is mathematically formulated based on the incident response truck (IRT)¡¯s activity. Response time is considered being comprised of both preparation delay and travel time to the incident site. Both components are modeled using probability distributions to take their stochastic features into account. The response time model is calibrated using the Washington State Incident Tracking System (WITS) data collected in 2009 and dual-loop detector data. Seven variables were found to significantly increase the response preparation delay (e.g. injury involved, heavy truck involved, and weekends) and eleven variables were found having a decreasing effect on preparation time (e.g. peak hour, HOV, and average annual daily traffic). The model has the potential to be used for incident response resource optimization and identification of measures for incident response time improvement.

Supplemental Notes:

This paper was sponsored by TRB committee AHB20 Freeway Operations

Monograph Accession #:

01362476

Report/Paper Numbers:

12-3530

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Hou, Lin
Lao, Yunteng
Wang, Yinhai
Zhang, Zuo
Zhang, Yi
Li, Zhiheng

Pagination:

22p

Publication Date:

2012

Conference:

Transportation Research Board 91st Annual Meeting

Location: Washington DC, United States
Date: 2012-1-22 to 2012-1-26
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control

Source Data:

Transportation Research Board Annual Meeting 2012 Paper #12-3530

Files:

TRIS, TRB

Created Date:

Feb 8 2012 5:17PM