Allocation optimization of bicycle-sharing stations at scenic spots
来源期刊：中南大学学报(英文版)2014年第8期
论文作者：GUO Tang-yi(郭唐仪) 张平 SHAO Fei(邵飞) LIU Ying-shun(刘英舜)
文章页码：3396 - 3403
Key words：bicycle-sharing; allocation optimization; scenic spot; cluster
Abstract: Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference (RP/SP) combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The survey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O(243) to O(432). The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.
GUO Tang-yi(郭唐仪)1, ZHANG Ping(张平)2, SHAO Fei(邵飞)3, LIU Ying-shun(刘英舜)1
(1. School of Automation, Nanjing University of Science & Technology, Nanjing 210094, China;
2. Department of Scientific Research, PLA University of Science and Technology, Nanjing 210007, China;
3. Engineering Institute, PLA University of Science and Technology, Nanjing 210007, China)
Abstract:Bicycle-sharing system is considered as a green option to provide a better connection between scenic spots and nearby metro/bus stations. Allocating and optimizing the layout of bicycle-sharing system inside the scenic spot and around its influencing area are focused on. It is found that the terrain, land use, nearby transport network and scenery point distribution have significant impact on the allocation of bicycle-sharing system. While the candidate bicycle-sharing stations installed at the inner scenic points, entrances/exits and metro stations are fixed, the ones installed at bus-stations and other passenger concentration buildings are adjustable. Aiming at minimizing the total cycling distance and overlapping rate, an optimization model is proposed and solved based on the idea of cluster concept and greedy heuristic. A revealed preference/stated preference (RP/SP) combined survey was conducted at Xuanwu Lake in Nanjing, China, to get an insight into the touring trip characteristics and bicycle-sharing tendency. The results reveal that 39.81% visitors accept a cycling distance of 1-3 km and 62.50% respondents think that the bicycle-sharing system should charge an appropriate fee. The survey indicates that there is high possibility to carry out a bicycle-sharing system at Xuanwu Lake. Optimizing the allocation problem cluster by cluster rather than using an exhaustive search method significantly reduces the computing amount from O(243) to O(432). The 500 m-radius-coverage rate for the alternative optimized by 500 m-radius-cluster and 800 m-radius-cluster is 89.2% and 68.5%, respectively. The final layout scheme will provide decision makers engineering guidelines and theoretical support.
Key words:bicycle-sharing; allocation optimization; scenic spot; cluster
