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Evasive Flow Capturing Problem: Optimal Allocation of Weigh-in-Motion Stations, Tollbooths, and Security Checkpoints

Evasive Flow Capturing Problem: Optimal Allocation of Weigh-in-Motion Stations, Tollbooths, and Security Checkpoints
规避流量捕获问题:动态称重站、收费站和安全检查站的优化分配
批准号:
1335416
负责人:
Paul Schonfeld
金额:
$22.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-09-01 至 2016-08-31

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中文摘要
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英文摘要
The goal of this research project is to introduce a new flow capturing framework, "Evasive Flow Capturing Problem" (EFCP), and develop mathematical solution techniques for optimizing locations of facilities that targeted drivers wish to avoid. The flow capturing problem consists of locating facilities in order to maximize the number of users who should encounter at least one of these facilities along their predetermined travel paths. Existing models for solving this problem commonly assume that if a facility is located along (or relatively close to) a predetermined path, the transportation flow along this path is considered captured. However, this assumption is untenable when locating facilities that drivers try to avoid, such as facilities used to intercept violators, toll evaders, intruders or hazardous cargoes. The project's methods will be applied in realistic case studies for optimally locating truck weight enforcement facilities as well as security and safety checkpoints.If successful, this research is expected to improve current practices of transportation agencies in locating weight-enforcing facilities, which often consist of simply prioritizing the most damaged roads. The methods for locating weight-enforcing facilities could speed up decision making processes of public agencies and provide solutions that greatly reduce 1) road maintenance costs, 2) environmental damage and 3) accident costs due to overweight commercial vehicles. The methods for optimal location of security and safety checkpoints could considerably improve homeland security and the risks of transporting hazardous materials. The computer implementations of the proposed work (e.g. codes for the solution techniques and input data) will be made available online for future extensions and will be used in graduate optimization courses that serve women and under-represented minorities. The developed methods and results will also be disseminated through technical conferences, journal papers, and presentations to transportation agencies.
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EAGER: Optimization with Data Acquisition in Transportation Engineering
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