| dc.description.abstract | Trucks are one common kind of vehicles for land transportation. In order to reduce costs and save time, many truck drivers would expand the one-time amount of cargo by overloads. Overweight trucks, however, incur numerous negative costs shared with other road users. The overloads not only cause damage to the road structure and pavements, but also increase costs of road maintenance. Overweight trucks emit much more exhaust that pollutes the environment without paying the price. Therefore, the authority of traffic administration has to aggressively capture overweight trucks at law. To capture overweight trucks, Weigh-In-Motion of trucks is required. As the cost of WIM (Weigh-In-Motion) is high, site selection becomes particularly important on a limited budget.
This study applies mathematical programming method as well as network flow techniques and simulates the interaction between the authority of road administration and overweight trucks in the use of Bi-Level mathematical programming method, in which overweight trucks would avoid the check point after arranging the site of WIM. In reality, the site of WIM could not be constantly changed to cater to every route overweight trucks take. Accordingly, if we can optimize the location by the program and use the result as final decision, the authority of road administration can install WIM on the most effective road. This study aims to minimize the damage due to overweight trucks from the authority’s point of view. This study utilizes Bi-Level mathematical programming method to establish the interaction between the authority of road administration and overweight trucks. To simulate multi-phased upper and lower layer solution, this study also develop a heuristic solution algorithm in coordination with mathematical programming software package CPLEX; to evaluate the practical performance of this algorithm, this study imitates Nevada network and compares the solution given by k-shortest path method with the heuristic solution algorithm, and ultimately performs sensitive and scenario analysis for different parameters. The result shows that the proposed algorithm provided by this study is efficient and can improve the shortcomings of k-shortest path method.
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