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清华大学学报(自然科学版)  2023, Vol. 63 Issue (9): 1428-1439    DOI: 10.16511/j.cnki.qhdxxb.2022.21.044
  车辆与交通 本期目录 | 过刊浏览 | 高级检索 |
城市轨道交通车站客流特征影响程度分析
马壮林1, 杨兴2, 胡大伟1, 谭晓伟3
1. 长安大学 运输工程学院, 西安 710064;
2. 山东浪潮新基建科技有限公司, 济南 250101;
3. 长安大学 汽车学院, 西安 710064
Influence degree analysis of ridership characteristics at urban rail transit stations
MA Zhuanglin1, YANG Xing2, HU Dawei1, TAN Xiaowei3
1. College of Transportation Engineering, Chang'an University, Xi'an 710064, China;
2. Inspur New Infrastructure Technology Co., Ltd., Jinan 250101, China;
3. School of Automobile, Chang'an University, Xi'an 710064, China
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摘要 城市轨道交通车站客流特征与其周边建成环境和社会经济因素密切相关, 且不同影响因素对客流特征的影响也存在时间和空间异质性。 以车站工作日日均客流量、 工作日特殊时段(如早高峰进站、 早高峰出站、 晚高峰进站和晚高峰出站)客流量为因变量, 从车站属性、 连接性和建成环境3个方面选择23个自变量, 采用多尺度地理加权回归(MGWR)模型构建客流特征分析模型, 分析不同时间尺度下轨道交通车站客流量的影响因素及其相互作用, 并以南京市轨道交通系统进行实例分析。 结果表明: 与普通最小二乘法(OLS)回归模型和地理加权回归(GWR)模型相比, MGWR模型更为可靠; 忽略早晚高峰客流影响的全天客流量预测模型拥有的显著自变量最多, 到市中心的距离对客流量有显著的负影响, 证明距离市中心越近的车站的客流量集聚性越明显; 周边居住、 生活类设施占比较高的车站对早高峰进站和晚高峰出站客流有很强的吸引作用, 而周边居住、 生活类设施占比不高的车站对早高峰出站和晚高峰进站客流有很强的吸引作用。 研究结果可以为城市规划部门促进城市轨道交通与城市建设的协同发展提供理论支撑。
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马壮林
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关键词 城市轨道交通车站客流特征多尺度加权地理回归模型影响程度    
Abstract:[Objective] The ridership characteristics of urban rail transit stations are closely related to the surrounding built environment and socio-economic factors, and the influence of different influencing factors on ridership characteristics also has temporal and spatial heterogeneity. Considering the complexity of influencing factors on station ridership, this paper uses the multiscale geographical weighted regression (MGWR) model to analyze the influencing factors of ridership at rail transit stations in different temporal scales.[Methods] This paper selects the station ridership on weekdays as the dependent variable, which is divided into five categories, including the average daily ridership, inbound ridership of morning peak hours, outbound ridership of morning peak hours, inbound ridership of evening peak hours, and outbound ridership of evening peak hours. A total of 23 independent variables are selected from three aspects: station attributes, connectivity, and the built environment. The variance inflation factor and Moran index are utilized to test the linear correlation and spatial autocorrelation between independent variables, respectively. The MGWR model is applied to construct the analysis model of ridership characteristics, and three indicators, including the residual sum of squares (RSS), adjusted R2, and the corrected Akaike information criterion (AICc), are employed to compare the performance of the ordinary least squares (OLS), geographically weighted regression (GWR), and MGWR models. The influencing factors and their interaction with rail transit station ridership in different temporal scales are developed. Finally, this method is applied to analyze the influence degree of ridership characteristics at Nanjing rail transit station.[Results] The following results are presented. 1) The MGWR model is more reliable than the OLS and GWR models. 2) The average daily ridership analysis model, which ignores the impact of morning and evening peak hour ridership, has the most significant independent variables. 3) The distance to the city center has a significant negative impact on station ridership, indicating that the agglomeration of station ridership is evident when the station is close to the city center. 4) The stations with a high proportion of residential and living facilities have a strong attraction to the morning peak inbound and evening peak outbound ridership, whereas those with a low proportion of residential and livings facilities have a strong attraction to the morning peak outbound and evening peak inbound ridership. Three significant local variables, namely tourism facility POI density, enterprise and office POI density, and the ratio of floor area on commercial lands to the total floor area, are available, and these local variables have different impacts on rail transit ridership at different temporal scales. Tourism facility POI density has negative spatially varying impacts on the average daily ridership, inbound ridership of morning peak hours, and outbound ridership of evening peak hours. Enterprise and office POI density has a negative spatially varying impact on inbound ridership of morning peak hours but has a positive spatially varying impact on outbound ridership of morning peak hours. The ratio of floor area on commercial lands to the total floor area has positive and negative spatially varying impacts on inbound ridership during evening peak hours. This finding implies that not all the commercial buildings around the rail transit stations are attractive to the inbound ridership during evening peak hours.[Conclusions] The MGWR model considering spatial autocorrelation can capture numerous influence scales of different variables and reduce the deviation of results. The developed method in this paper achieves the expected goal and depicts the interdependence between ridership and influencing factors from the station level.
Key wordsurban rail transit    characteristic of station ridership    multiscale weighted geographical regression model    influence degree
收稿日期: 2022-08-16      出版日期: 2023-08-19
基金资助:教育部人文社会科学研究青年基金项目(18YJCZH130); 陕西省自然科学基金项目(2021JZ-20); 长安大学中央高校基本科研业务费专项资金(300102229304)
作者简介: 马壮林(1980-),男,教授。E-mail:zhuanglinma@chd.edu.cn
引用本文:   
马壮林, 杨兴, 胡大伟, 谭晓伟. 城市轨道交通车站客流特征影响程度分析[J]. 清华大学学报(自然科学版), 2023, 63(9): 1428-1439.
MA Zhuanglin, YANG Xing, HU Dawei, TAN Xiaowei. Influence degree analysis of ridership characteristics at urban rail transit stations. Journal of Tsinghua University(Science and Technology), 2023, 63(9): 1428-1439.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.21.044  或          http://jst.tsinghuajournals.com/CN/Y2023/V63/I9/1428
  
  
  
  
  
  
  
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