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Project Details

Title:Modeling Multi-class Truck Traffic Assignment Method with Different Traffic Restraint Constraints
Principal Investigators:Anthony Chen and Ziqi Song
University:Utah State University
Grant #:DTRT13-G-UTC38 (MAP21)
Project #:MPC-479
RiP #:01579596
RH Display ID:151119
Keywords:origin and destination, routes, traffic assignment, traffic flow, traffic restraint, travel demand, travel time, truck traffic, vehicle miles of travel


Truck traffic continues to grow as a result of increasing freight shipments transported by trucks, there is an increasing interest to model multiple vehicle classes separately, especially in addressing the impacts of truck traffic on congestion, infrastructure deterioration, safety, and environmental concerns in many urban cities. According to the Bureau of Transportation Statistics (BTS), freight shipments transported by trucks account for 71 percent by value in U.S. dollars and 76 percent by weight in tons of all commodity shipments (BTS, 2014). Hence, the purpose of this proposal is to develop advanced traffic assignment method and computation algorithm for addressing the asymmetric vehicle interactions, route overlapping, and traffic restraints in multi-class traffic assignment problems involving multiple types of trucks.

Project Word Files

NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050