Optimizing bus services with variable directional and temporal demand using genetic algorithm

查看全文 作　　者：[1,2]瞿何舟;CHIEN Steven I-[3,4]Jy;[1]刘晓波;[1]张培桐;BLADIKAS [5]Athanassios

机构地区：[1]School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China;[2]Sichuan Province Key Laboratory of Comprehensive Transportation,Chengdu 610031,China;[3]College of Automobile,Chang'an University,Xi'an 710064,China;[4]John A.Reif Jr.Department of Civil and Environmental Engineering,New Jersey Institute of Technology,Newark 07102-1982,USA;[5]Mechanical&Industrial Engineering,New Jersey Institute of Technology,Newark 07102-1982,USA

出　　处：《Journal of Central South University》索引2016年第23卷第7期,共13页

基　　金：Project(B01B1203)supported by Sichuan Province Key Laboratory of Comprehensive Transportation,China;Project(SWJTU09BR141)supported by the Southwest Jiaotong University,China 摘　　要：As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services. 关键词：组合优化问题公交服务遗传算法时间可变出行方式城市地区数学模型

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Optimizing bus services with variable directional and temporal demand using genetic algorithm

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