Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system
Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system
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DOI：
10.1111/mice.13007
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发表时间：
2023-04
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影响因子：
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通讯作者：
Liangze Yang;S. C. Wong;H. Ho;Chi-Wang Shu;Mengping Zhang
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文献类型：
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作者：
Liangze Yang;S. C. Wong;H. Ho;Chi-Wang Shu;Mengping Zhang
近几十年来，汽车排放对城市环境的影响引起了越来越多的关注，人们已经认识到，汽车排放影响人们对住房地点的选择。此外，住房分配模式决定了人们的出行行为，从而影响汽车排放。本研究将单一中央商务区（CBD）城市的机动车尾气排放纳入双层优化模型中，考虑住房分配问题。在下层子程序中，在固定的住房分配下，使用具有组合出发时间和路线选择的预测动态连续体用户最优（PDUO-C）模型来研究城市的交通流。在上层子程序中，定义并最小化健康成本，以确定额外住房单元的最优分配，从而更新住房分配。采用模拟退火算法求解住房分配问题。结果表明，额外的住房位置的分布取决于距离CBD的距离和方向。敏感性分析显示了各种因素的影响（例如，预算和住房供应成本）对优化的健康成本和旅行需求模式的影响。
In recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern.