00743089

Component

https://doi.org/10.3141/1588-12

http://scholar.google.com...tsky&publication_year=1997

Real-time route guidance is a promising approach to alleviating congestion on the nation's highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithms (GAs) is used to solve a dynamic traffic assignment model developed for a real-world routing scenario in Hampton Roads, Virginia. The results of the GA approach are presented and discussed, and the performance of the GA program is compared with an example of commercially available nonlinear programming (NLP) software. Among the main conclusions is that GAs offer tangible advantages when used to solve the dynamic traffic assignment problem. First, GAs allow the relaxation of many of the assumptions that were needed to solve the problem analytically by traditional techniques. GAs can also handle larger problems than some of the commercially available NLP software packages.

This paper appears in Transportation Research Record No. 1588, Intelligent Transportation Systems and Artificial Intelligence.

English

p. 95-103

1997

Transportation Research Record

0309061628

Figures (6) ; References (11) ; Tables (2)

Alternatives analysis; Artificial intelligence; Genetic algorithms; Nonlinear programming; Real time control; Real time data processing; Route guidance; Traffic assignment

Hampton Roads (Virginia)

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Nov 5 1997 12:00AM

• AUTOMATIC VEHICLE IDENTIFICATION TECHNOLOGY APPLICATIONS TO TOLL COLLECTION SERVICES
• COST-BENEFIT ANALYSIS ON DEPLOYMENT OF AUTOMATED HIGHWAY SYSTEMS
• CRITICAL REVIEW OF EXPERT SYSTEM VALIDATION IN TRANSPORTATION
• DEVELOPING A MARKET-SENSITIVE INTELLIGENT TRANSPORTATION SYSTEMS EDUCATIONAL PROGRAM
• EVALUATING EFFECTIVENESS OF REAL-TIME ADVANCED TRAVELER INFORMATION SYSTEMS USING A SMALL TEST VEHICLE FLEET
• FRAMEWORK FOR DEVELOPING A REGIONAL SYSTEM ARCHITECTURE FOR INTELLIGENT TRANSPORTATION SYSTEMS
• GUIDELINES FOR FUNDING OPERATIONS AND MAINTENANCE OF INTELLIGENT TRANSPORTATION SYSTEMS/ADVANCED TRAFFIC MANAGEMENT SYSTEMS
• INTELLIGENT TRANSPORTATION SYSTEMS AND SUSTAINABLE COMMUNITIES: FINDINGS OF A NATIONAL STUDY
• MACHINE LEARNING IN UPDATING PREDICTIVE MODELS OF PLANNING AND SCHEDULING TRANSPORTATION PROJECTS
• MACROSCOPIC MODELING OF FREEWAY TRAFFIC USING AN ARTIFICIAL NEURAL NETWORK
• PERSPECTIVES ON DRIVER PREFERENCES FOR DYNAMIC ROUTE GUIDANCE SYSTEMS
• PLANITS: STRUCTURING AND SUPPORTING THE INTELLIGENT TRANSPORTATION SYSTEMS PLANNING PROCESS
• PROMOTING ADVANCED TRAVELER INFORMATION SYSTEMS AMONG CELLULAR AND LAND-LINE PHONE USERS: SMARTRAVELER EXPERIENCE IN BOSTON
• REAL-TIME MULTIPLE-OBJECTIVE PATH SEARCH FOR IN-VEHICLE ROUTE GUIDANCE SYSTEMS
• TECHNICAL AND ECONOMIC VIABILITY OF AUTOMATED HIGHWAY SYSTEMS: PRELIMINARY ANALYSIS
• TRAFFIC ANALYSIS FOR HIGHWAY-TO-HIGHWAY INTERCHANGES ON AUTOMATED HIGHWAY SYSTEMS: CONGESTION IN ABSENCE OF DEDICATED RAMPS
• WILLINGNESS TO PAY FOR ADVANCED TRAVELER INFORMATION SYSTEMS: SMARTRAVELER CASE STUDY