中文摘要：

分享自行车的系统被看作一种绿选择提供在舞台的点和附近的 metro/bus 车站之间的一个更好的连接。在舞台的点内并且在它的影响区域附近分配并且优化分享自行车的系统的布局上被集中。地面，陆地使用，附近的运输网络和风景点分发在分享自行车的系统的分配上有重要影响，这被发现。当分享自行车的车站在内部舞台的点，入口 / 出口和 metro 车站安装了的候选人被修理时，在公共汽车车站和另外的旅客集中大楼安装的是可调节的。瞄准最小化全部的骑车距离和重叠的率，一个优化模型基于簇概念的想法被建议并且解决并且贪婪启发式。揭示偏爱 / 状态偏爱(RP/SP ) 联合了调查在南京在克苏安伍·莱克被进行，中国，把卓见装入旅游旅行特征和分享自行车的趋势。结果表明 39.81% 来宾接受 13 km 的骑车的距离， 62.50% 回答者认为分享自行车的系统应该控告适当费用。调查显示有高可能性在克苏安伍·莱克执行一个分享自行车的系统。显著地由簇而非用一个彻底的搜索方法优化分配问题簇从 O 减少计算数量(2 < 啜 class= “ a-plus-plus ” > 43 ) 到 O (43 < 啜 class= “ a-plus-plus ” > 2 ) 。为 500 m-radius-cluster 和 800 m-radius-cluster 优化的选择的 500 m-radius-coverage 率分别地是 89.2% 和 68.5% 。最后的布局计划将提供设计指南和理论支持的决定制造者。

英文摘要：

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 sttrvey 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（2^43） to O（43 2）. 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.