城市轨道交通车站双层选址模型
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摘要
为保证城市轨道交通车站选址的合理性,避免建成后分担率低的情况,建立基于既定线路和候选车站的双层选址模型.上层模型以客流量最大为目标,且在对候选车站进行客流量预测时,建立地理加权回归模型.下层模型考虑经济属性,将乘客出行成本和运营方成本作为城市轨道交通综合交通成本,以单位乘客综合交通成本最小为目标.通过对比多个启发式算法,确定基于模拟退火算法的求解流程.以哈尔滨地铁1号线为例,运用所建立模型对该线路的车站进行重新选址.结果表明:采用该模型所得到的车站选址方案其各车站每日上车乘客量之和为191 553人,较现有的177 010人,增长了14 543人,增长比例为8.2%;而新选址方案的牵引能耗成本为每日15 972元,较现有车站的17 501元,减少了1 529元,减少比例为8.7%。所建立的城市轨道交通车站双层选址模型能同时保证选址结果的社会效益和经济效益.
In order to ensure the rationality of urban rail transit stations location and avoid low sharing rate after construction, a bi-level model was established based on the selected route and candidate stations. The upper level aimed to achieve maximum ridership, and a GWR model was selected for ridership forecast at candidate stations. The lower level aimed to achieve minimum comprehensive cost per passenger, and the urban rail transit comprehensive transportation cost was defined to include both ridership cost and operating cost. Several heuristic algorithms were compared, and the simulated annealing algorithm was selected to solve the bi-level model. Harbin Metro Line 1 was taken as a case. The established model was used to optimize the location of the stations on this route. Results showed that there were 191 553 passengers per day at the optimized stations. Compared with the ridership of 177 010 at the current stations, there is an increase of 14 543 passengers with an increasing rate of 8.2%. The cost of energy consumption of the optimized stations was 15 972 yuan, which is 1 529 yuan less than that of the current stations with a decreasing rate of 8.7%. Therefore, the established bi-level model of urban rail transit station location can guarantee both social and economic benefits.
In order to ensure the rationality of urban rail transit stations location and avoid low sharing rate after construction, a bi-level model was established based on the selected route and candidate stations. The upper level aimed to achieve maximum ridership, and a GWR model was selected for ridership forecast at candidate stations. The lower level aimed to achieve minimum comprehensive cost per passenger, and the urban rail transit comprehensive transportation cost was defined to include both ridership cost and operating cost. Several heuristic algorithms were compared, and the simulated annealing algorithm was selected to solve the bi-level model. Harbin Metro Line 1 was taken as a case. The established model was used to optimize the location of the stations on this route. Results showed that there were 191 553 passengers per day at the optimized stations. Compared with the ridership of 177 010 at the current stations, there is an increase of 14 543 passengers with an increasing rate of 8.2%. The cost of energy consumption of the optimized stations was 15 972 yuan, which is 1 529 yuan less than that of the current stations with a decreasing rate of 8.7%. Therefore, the established bi-level model of urban rail transit station location can guarantee both social and economic benefits.
引文
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[16]尹航, 蒋涛, 张言伟, 等. 列车牵引计算分析及仿真[J]. 大连交通大学学报, 2013, 34(1): 80 YIN Hang, JIANG Tao, ZHANG Yanwei, et al. Analysis and simulation of train traction computation [J]. Journal of Dalian Jiaotong University, 2013, 34(1): 80
[2]SOHN K, SHIM H. Factors generating boardings atmetro stations in the Seoul metropolitan area [J]. Cities, 2010, 27(5): 358
[3]CEDER A, BUTCHER M, WANG L. Optimization of bus stop placement for routes on uneven topography [J]. Transportation Research Part B: Methodological, 2015, 74: 40
[4]王芳. 基于出行方式选择的轨道交通站间距确定[J]. 大连交通大学学报, 2011, 32(6): 36 WANG Fang. Spacing determination of rail transit stations based on travel mode choice [J]. Journal of Dalian Jiaotong University, 2011, 32(6): 36
[5] 贾俊芳, 黄荣松. 基于出行时间最少的市郊铁路合理站间距研究[J]. 交通信息与安全, 2013, 31(5): 74 JIA Junfang, HUANG Rongsong. Minimum travel time-based spacing between suburban railway stations [J]. Journal of Transport Information and Safety, 2013, 31(5): 74
[6]左忠义, 赵汉鲲. 城市轨道交通站间距优化研究[J]. 大连交通大学学报, 2014, 35(2): 25 ZUO Zhongyi, ZHAO Hankun. Study on optimal spacing of rail transit station [J]. Journal of Dalian Jiaotong University, 2014, 35(2): 25
[7]石瑜. 基于GIS的城市地铁站点布设研究[D]. 石家庄: 石家庄铁道大学,2014 SHI Yu. Research on the urban metro station layout based on GIS [D]. Shijiazhuang: Shijiazhuang Tiedao University, 2014
[8]ZHAO J, DENG W, SONG Y, et al. Analysis of metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models [J]. Transportation, 2014, 41(1): 133
[9]EWING R, CERVERO R. Travel and the built environment: a meta-analysis [J]. Journal of the American Planning Association, 2010, 76(3): 265
[10]李君, 叶霞飞. 城市轨道交通车站分布方法的研究[J]. 同济大学学报(自然科学版), 2004, 32(8): 1009 LI Jun, YE Xiafei. Studies on distribution of stations of urban mass transit [J]. Journal of Tongji University (Natural Science), 2004, 32(8): 1009
[11]秦萍, 陈颖翱, 徐晋涛,等. 北京居民出行行为分析:时间价值和交通需求弹性估算[J]. 经济地理, 2014, 34(11): 17 QIN Ping, CHEN Ying’ao, XU Jintao, et al. Travel behavior analysis for the residents in Beijing: value of time and travel demand elasticity estimates [J]. Economic Geography, 2014, 34(11): 17
[12]鲁钰雯. 寒地城市可步行性测度及优化研究[D]. 哈尔滨:哈尔滨工业大学, 2017 LU Yuwen.The walkability measurement and optimization study of winter cities [D]. Harbin :Harbin Institute of Technology, 2017
[13]徐威鸿. 城市轨道交通系统之复杂网络特征及乘客出行特征研究[D].天津:天津大学, 2014 XU Weihong. A research on the complex network characteristics of urban railway traffic system and the passenger traffic feature [D]. Tianjin: Tianjin University, 2014
[14]马超云, 毛保华, 梁肖, 等. 地铁列车节能运行惰行控制研究[J]. 交通信息与安全, 2010, 28(2): 37 MA Chaoyun, MAO Baohua, LIANG Xiao, et al. Coast control of urban train movement for energy efficiency [J]. Journal of Transport Information and Safety, 2010, 28(2): 37
[15]杨俭, 李发扬, 宋瑞刚, 等. 城市轨道交通车辆制动能量回收技术现状及研究进展[J]. 铁道学报, 2011, 33(2): 26 YANG Jian, LI Fayang, SONG Ruigang, et al. Review of the utilization of vehicular braking energy in urban railway transportation [J] Journal of the China Railway Society, 2011, 33(2): 26
[16]尹航, 蒋涛, 张言伟, 等. 列车牵引计算分析及仿真[J]. 大连交通大学学报, 2013, 34(1): 80 YIN Hang, JIANG Tao, ZHANG Yanwei, et al. Analysis and simulation of train traction computation [J]. Journal of Dalian Jiaotong University, 2013, 34(1): 80