An Analysis of Park and Ride Choice Behavior around Rail Stations Based on Cross-Nested Logit Model
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摘要: 研究轨道站点停车换乘选择行为，分析出行者选择停车换乘出行的影响因素，可进一步改善设施运营状况与优化设施配置。选取南京市轨道站点作为研究对象，通过采集轨道站点停车换乘设施的使用情况，从个人属性、出行属性及停车换乘意向3个方面对设施使用者的停车换乘选择行为进行调查。分析停车换乘选择行为的影响因素并选取9项特征变量，考虑出行者换乘方式、时间、距离，选择换乘便捷度和换乘次数进一步分析出行者停车换乘选择行为。分别以换乘便捷度和换乘次数为上层巢构建交叉巢式Logit模型，对比分析不同情况下出行者选择停车换乘出行的影响因素差异，揭示特征变量对站点选择和出行方式选择的影响。研究结果表明：换乘便捷度和换乘次数为CNL模型上层巢的参数结果存在差异，以换乘便捷度为CNL模型上层巢的结果中，收入、出行目的和停车时间的参数绝对值较大，分别为0.467，0.359，0.454；以换乘次数为CNL模型上层巢的结果中，绝对值较大的则是收入、出行目的和出行次数，分别为0.550，0.579，0.642，对比发现收入和出行目的对出行者选择停车换乘的影响较大。由模型中下层选择枝对上层巢的隶属度估计值可以发现，换乘便捷度适度增加或换乘次数适度减少的情形下“，停车换乘（P&R）”出行方式的选择枝隶属度最高，分别为0.399，0.464，出行者会更倾向于选择停车换乘。以换乘便捷度和换乘次数为上层巢构建的CNL模型较巢式Logit模型和多项Logit模型精度提升约10%，在不同换乘便捷度和换乘次数情况下出行者对不同影响因素具有差异化敏感程度。
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关键词:
- 城市轨道交通 /
- 停车换乘 /
- 选择行为 /
- 交叉巢式Logit模型
Abstract: This study aims to optimize the configuration and operation of park and ride (P&R) facilities around rail stations by investigating traveler choice behavior at rail stations in Nanjing. The data on P&R facility usage was collected, and a survey focusing on three primary aspects was conducted: personal characteristics, travel characteristics, and P&R intentions. Utilizing this data, nine key variables influencing P&R choice behavior were identified. The study incorporates factors such as transfer mode, time, and distance to examine the nuances of traveler choices. Cross-nested Logit (CNL) models with transfer convenience and times as the primary nests were developed to analyze these behaviors under varying conditions. The analysis reveals that income and travel purpose significantly impact P&R choice, with the magnitude of these effects varying between models prioritizing transfer convenience versus those emphasizing transfer times. When transfer convenience is the upper nest of the CNL model, parameters for income, travel purpose, and parking duration exhibit relatively significant absolute values, namely 0.467, 0.359, and 0.454 respectively. Conversely, when transfer frequency serves as the upper nest of the CNL model, income, travel purpose, and trip frequency demonstrate relatively substantial absolute values, namely 0.550, 0.579, and 0.642 respectively. The membership probabilities within the CNL models indicate that travelers are more likely to opt for P&R when transfer convenience moderately increases or transfer frequency moderately decreases, with the highest membership degrees being 0.399 and 0.464, respectively. This suggests a preference for balanced transfer conditions. Furthermore, the CNL models demonstrate an approximately 10% improvement in prediction accuracy over nested and multiple Logit models, underscoring their efficacy in capturing travelers'sensitivities to different transfer scenarios.-
Key words:
- urban rail transit /
- park and ride /
- choice behavior /
- cross-nested Logit model
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图 3 站点选择与出行方式组合选择的CNL模型结构示意图
Figure 3. Structure diagram of CNL for combination selection of stations and travel modes
图 4 以换乘便捷度为CNL模型上层结构示意图
Figure 4. Superstructure diagram of CNL based on transfer convenience
图 5 以换乘次数为CNL模型上层结构示意图
Figure 5. Superstructure diagram of CNL based on transfer times
表 1 南京市轨道站点P&R设施使用情况
Table 1. P&R facility usage around Nanjing rail stations
P&R设施 设施位置 停车泊位数/个 停车换乘数/(pcu/d) 步行距离/(m) 费用/(元/d) 泰冯路 3号线、S8号线 154 72 < 100 36 柳洲东路 3号线 179 171 < 100 20 经天路 2号线 780 731 100 14 金马路 2号线、4号线 72 14 170 13 马群 2号线、S6号线 653 80 100 32 竹山路 1号线 81 24 < 100 45 诚信大道 3号线 96 10 350 14 
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表 2 停车换乘选择行为影响因素
Table 2. Set of influencing factors of P&R choice behavior
类型 影响因素 个人层面 性别 年龄 职业 收入 出行层面 家庭拥有小汽车情况 出行目的 出行次数 停车时间 停车费用 换乘次数 换乘便捷度
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表 3 出行者特征变量情况汇总表
Table 3. Summary of traveler feature variables
类型 名称 变量 含义 社会经济属性 性别 分类变量 男性，女性 年龄 连续变量 实际值 职业 分类变量 学生，公司职员，政府及事业单位员工，私营及个体劳动者，专业人士（教师、医生、律师等），服务业人员，自由职业者（作家、艺术家等），其他 月收入/（元） 分类变量 ＜3 000，3 000~＜5 000，5 000~＜10 000，10 000~＜20 000，≥20 000 小汽车拥有量 连续变量 实际值 出行属性 出行目的 分类变量 通勤（上班、上学、商务出行），休闲娱乐，探亲访友，生活必要出行（看病、买菜等），其他 出行次数/（周/次） 分类变量 ＜1，1~＜3，4~＜6，≥6 停车时间/（h） 分类变量 ＜2，2~＜4，4~＜8，8~＜12，≥12 停车费用（元/次） 分类变量 10，15，20
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表 4 以换乘便捷度为CNL模型上层结构的参数结果表
Table 4. Parameter results of the upper structure of CNL model with transfer convenience
特征变量 参数 数值 t检验 常数项 βi －0.079 8 3.43 性别变量 β1 －0.15 －1.49 年龄变量 β2 0.267 2.59 职业变量 β3 －0.283 －1.86 收入变量 β4 0.467 15.4 小汽车拥有量变量 β5 －0.16 1.92 出行目的变量 β6 0.359 －11.07 出行次数变量 β7 0.288 2.74 停车时间变量 β8 －0.454 15.25 停车费用变量 β9 －0.027 9 －4.47 样本数 N 917 拟合优度 ρ2 0.362
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表 5 以换乘便捷度为巢的隶属度估计值表
Table 5. Membership degree estimation with transfer convenience as nest
隶属度 估计值 t检验 α1A 0.247 －3.18 α2A 0.242 －6.14 α3A 0.399 －4.85 α1B 0.445 －13.62 α2B 0.328 －11.53 α3B 0.346 －14.29 α1C 0.307 －8.76 α2C 0.429 －12.37 α3C 0.253 －9.31
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表 6 以换乘次数为CNL模型上层结构的参数结果表
Table 6. Parameter results of the upper structure of CNL model with transfer times
特征变量 参数 数值 t检验 常数项 βi －0.090 6 4.59 性别变量 βi －0.134 －1.47 年龄变量 β1 0.215 3.62 职业变量 β2 －0.177 －1.73 收入变量 β3 0.55 －13.24 小汽车拥有量变量 β4 －0.119 1.27 出行目的变量 β5 0.579 －14.81 出行次数变量 β6 0.642 －10.38 停车时间变量 β7 －0.182 9.17 停车费用变量 β8 －0.046 9 －4.32 样本数 N 917 拟合优度 ρ2 0.337
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表 7 以换乘次数为巢的隶属度估计值表
Table 7. Membership degree estimation with transfer times as nest
隶属度 估计值 t检验 α1D 0.263 －5.95 α2D 0.335 －14.3 α3D 0.464 －9.82 α1E 0.4 －11.73 α2E 0.278 4.52 α3E 0.294 8.74 α1F 0.336 －12.46 α2F 0.385 －7.91 α3F 0.241 6.48
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