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Quantifying incident-induced travel delays on freeways using traffic sensor data

Author: Yinhai WangPatikhom CheevarunothaiMark E HallenbeckWashington (State). Department of Transportation.Washington State Transportation Center.All authors
Publisher: [Olympia, Wash.] : Washington State Dept. of Transportation ; [Seattle, Wash.] : TransNow, [2008].
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
Traffic congestion is a major operational problem for freeways in Washington State. Recent studies have estimated that more than 50 percent of freeway congestion is caused by traffic incidents. To help the Washington State Department of Transportation (WSDOT) identify effective countermeasures against such congestion-inducing incidents, a thorough understanding of travel delays caused by incidents is essential. By  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Yinhai Wang; Patikhom Cheevarunothai; Mark E Hallenbeck; Washington (State). Department of Transportation.; Washington State Transportation Center.; Transportation Northwest (Organization); United States. Department of Transportation. Office of the Secretary.
OCLC Number: 243783398
Notes: "May 2008."
"TNW2008-07."
"TransNow budget no. 61-2393, agreement T4118, task 03, Incident-induced delay."
Description: x, 66 p. : ill. (some col.) ; 28 cm.
Responsibility: by Yinhai Wang, Patikhom Cheevarunothai, Mark Hallenbeck.

Abstract:

Traffic congestion is a major operational problem for freeways in Washington State. Recent studies have estimated that more than 50 percent of freeway congestion is caused by traffic incidents. To help the Washington State Department of Transportation (WSDOT) identify effective countermeasures against such congestion-inducing incidents, a thorough understanding of travel delays caused by incidents is essential. By using traffic data extracted from archived loop detector measurements and incident log data recorded by the WSDOT Incident Response (IR) team, this research project developed a new algorithm for quantifying travel delays produced by different incident categories. The algorithm applies a modified deterministic queuing theory to estimate incident-induced delay by using 1-minute aggregated loop detector data. Incident-induced delay refers to the difference between the total delay and the recurrent travel delay at the time and location influenced by the incident. The uniqueness of the delay calculation in this study is the use of a dynamic traffic-volume-based background profile, which is considered a more accurate representation of prevailing traffic conditions. According to the test results, the proposed algorithm can provide good estimates for incident-induced delay and capture the evolution of freeway traffic flow during incident duration. Because actual traffic data measured by loop detectors were used in this study to compute vehicle arrival and departure rates for delay calculations, the estimated incident-induced delay should be very close to the reality. Additionally, the proposed algorithm was implemented in the Advanced Roadway Incident Analyzer (ARIA) system. ARIA is a database-driven computer system that automates all the computational processes. More accurate incident delay information will help WSDOT improve its understanding of congestion-inducing incidents and select more effective countermeasures against incident-related traffic congestion on freeways.

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