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Collaborative Research: Non-Additive Network Routing and Assignment Models

协作研究：非加性网络路由和分配模型

基本信息

批准号：
1562291
负责人：
Stephen Boyles
金额：
$ 20.5万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562291&HistoricalAwards=false
关键词：
Collaborative Research Non Additive Network

项目摘要

Network assignment models are widely used to study the flow of commodities on infrastructure with a network structure (such as roadway drivers or freight shipments) and form the basis of transportation planning for urban areas. Currently, most network assignment models assume that the travel costs for a route is simply the sum of the travel costs of the roadway segments comprising that route. While true for some costs (like trip time), this assumption is invalid when accounting for costs due to unreliability, late arrival penalties, and risk preferences. This award will develop new network assignment models which can capture broader user objectives in route choice decision making, particularly related to reliability. Accurately representing such objectives in network optimization models will lead to better infrastructure planning and management strategies in large-scale urban areas, ultimately leading to reduced emissions and other related health benefits. The research will support participation and training of graduate and undergraduate students from minority and underrepresented groups in research. The results of this research will be incorporated into several undergraduate and graduate courses and high school lesson plans. This project will investigate efficient algorithms for different variants of non-additive shortest path, minimum cost flow, and traffic assignment problems, with potential applications in freight. These algorithms will exploit and advance recent progress in cutting plane and decomposition-based global optimization algorithms, and the underlying network structure. The project has three specific research aims: (i) Develop efficient solution algorithms for non-additive shortest path variants and their time-dependent extensions. (ii) Develop mathematical models and solution algorithms for non-additive network flow models. (iii) Develop analytical formulation and solution algorithm for the non-additive traffic assignment problem. The researched work draws on and integrates principles of network optimization, transportation network analysis, bi-criterion search, and global optimization. The key contribution of this work is the potential to exploit the synergies and integrate theoretically rigorous decomposition and cutting plane based algorithms developed for convex mixed integer nonlinear programs and existing network optimization algorithms.

网络分配模型被广泛用于研究具有网络结构的基础设施（如道路驾驶员或货物运输）上的商品流动，并构成城市交通规划的基础。目前，大多数网络分配模型都假设一条路线的旅行成本是组成该路线的路段的旅行成本的总和。虽然对于某些成本（如旅行时间）是正确的，但当考虑到由于不可靠性、迟到惩罚和风险偏好而导致的成本时，这种假设是无效的。该合同将开发新的网络分配模型，可以在路线选择决策中捕获更广泛的用户目标，特别是与可靠性相关的目标。在网络优化模型中准确地表示这些目标将有助于在大型城市地区制定更好的基础设施规划和管理战略，最终导致减少排放和其他相关的健康效益。这项研究将支持来自少数民族和代表性不足群体的研究生和本科生参与和培训研究。本研究的结果将被纳入几个本科和研究生课程和高中教案。该项目将研究非加性最短路径、最小成本流和交通分配问题的不同变体的有效算法，并在货运中具有潜在的应用。这些算法将利用和推进基于切割平面和分解的全局优化算法以及底层网络结构的最新进展。该项目有三个具体的研究目标：(i)开发非加性最短路径变体及其随时间扩展的有效解算法。（ii）为非加性网络流模型开发数学模型和求解算法。（iii）发展非加性交通分配问题的解析公式和求解算法。研究工作借鉴并整合了网络优化、交通网络分析、双准则搜索和全局优化的原理。这项工作的关键贡献是利用协同作用的潜力，并将为凸混合整数非线性规划开发的理论上严格的分解和基于切割平面的算法与现有的网络优化算法相结合。