Knowledge-Based System for Estimating Incident Clearance Duration for Maryland I-95. Issue 14 (December 2018)

• Record Type: Journal Article
• Authors: Won, Minsu; Kim, Hyeonmi; Chang, Gang-Len
• Is Part Of: Transportation research record. Volume 2672:Issue 14(2018)
• Page Start: 61
• Page End: 72
• Publication Date: 2018-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/0361198118792119
• Languages: English
• ISSNs: 0361-1981

Abstract

For incident response operations to be appreciated by the general public, it is essential that responsible highway agencies are capable of providing the estimated clearance duration of a detected incident at a level sufficiently reliable for motorists to make proper decisions such as selecting a detour route. Depending on the estimated clearance duration, the incident response center can then implement proper strategies to interact with motorists, ranging from providing incident information only to executing mandatory detouring operations. This study presents a knowledge-based system, based on detailed incident reports collected by the Maryland-CHART (Coordinated Highway Action Response Team) program between years 2012 and 2016, for such needs. The proposed system features the use of interval-based estimates derived from knowledge of historical data, with different confidence levels for each estimated incident clearance duration, and its rule-based structure for convenient updates with new data and available expertise from field operators. As some key variables associated with incident duration often only become available as the clearance operations progress, the developed system with its sequential nature allows users to dynamically revise the estimated duration when additional data have been reported. The preliminary evaluation results have shown the promise of the developed system which, with its invaluable historical information, can circumvent the many data quality and availability issues which have long plagued the applicability of some state-of-the-art models on this subject.