| 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. |
| 关键词: 城市轨道交通 车站选址 双层模型 地理加权回归 模拟退火算法 |
| DOI：10.11918/j.issn.0367-6234.201806134 |
| 分类号:U491 |
| 文献标识码:A |
| 基金项目:吉林省教育厅“十三五”科学技术研究项目(JJKH20180609KJ) |
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| Bi-level model of urban rail transit stations location |
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CHENG Guozhu1,ZHOU Linfang2
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(1.School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China; 2.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China)
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| Abstract: |
| 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. |
| Key words: urban rail transit station location bi-level model GWR simulated annealing algorithm |
