A Cause Analysis of Residents' Dependence on Public Transportation Based on Association Rules
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摘要: 鉴别不同出行者对公共交通的依赖程度，并分析其形成的致因差异，有助于从规划设计、政策制定等维度针对性地改善公共交通服务质量。设计并实施了居民出行的行为调查（revealed preference，RP）线上问卷，在数据质量检验的基础上引入关联匹配技术，通过融合出行调查数据与公共交通出行交易数据实现了个体公共交通出行链提取。提出了公共交通依赖性度量指标与关键致因指标，构建了AGNES-Apriori模型开展公共交通依赖性分级与不同层级群体强关联规则挖掘，并据此提出了公共交通依赖性层级提升的“两阶段”框架及出行激励策略集。结果表明：①居民公共交通依赖性可被划分为低、较低、较高和高依赖性4个层级，不同层级对应的强关联规则间具有显著差异性；②关联规则包含的指标数量与3个参数值呈负相关关系，高依赖性强关联规则出现的概率为低依赖性的2.1倍；③家和目的地到站点总距离、收入、小汽车可用性等客观条件是影响居民公共交通依赖性的关键致因，而公共交通出行低自由度是导致居民公共交通依赖性降低的重要原因；④较低的客观条件指标值通常促使居民形成较高的公共交通依赖性；⑤小汽车低可用性变量主要出现在公共交通低、高依赖性群体对应的强关联规则中，而高依赖性群体随其小汽车可用性增强可能出现公共交通依赖性降低的趋势。
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关键词:
- 城市交通 /
- 公共交通依赖性 /
- 出行链 /
- AGNES-Apriori算法 /
- 关联规则
Abstract: Identifying the magnitude of travelers' dependence on public transit (PT) and analyzing the differences in its underlying causes can contribute to targeted improvements in the level of PT services from the perspectives of planning, design and policy making. In this study, an online revealed preference (RP) survey for residents' travel is designed and carried out. The data quality is examined, based on which the correlation matching technique is adopted to extract individual PT-trip chains by integrating travel survey data and PT transaction data. Measurement indicators and key causation indicators of PT dependence are proposed, and an AGNES-Apriori model is developed to classify travelers' PT dependence and strong association rules for different groups. Further, a two-stage framework and a set of travel incentive strategies to enhance travelers' PT dependence levels are proposed. The results show that ①residents'PT dependence can be classified into four categories (low, relatively low, relatively high, and high dependences), and significant differences are found among the different categories regarding the strong association rules; ②the number of indicators contained in association rules is negatively correlated with three parameters, and the probability of strong association rules with high dependence level is 2.1 times higher than that with low dependence level; ③objective factors such as total distance from home and destination to the PT stations, income, and car availability are identified as key indicators affecting residents' PT dependence, and the low freedom for traveling by PT is an important reason for the reduction of travelers' dependence on PT; ④the low values of the objective factors usually cause the travelers to form a relatively high PT dependence; ⑤the low availability of cars mainly related to the strong association rules corresponding to the low and high PT dependence groups, while the high dependence group may show the tendency of reducing PT dependence with increased car availability.-
Key words:
- urban transport /
- public transit dependence /
- trip chains /
- AGNES-Apriori algorithm /
- association rules
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图 4 参数与关联规则数量间关系
Figure 4. The relationships between the parameters and number of association rules
图 6 公共交通依赖性“两阶段”层级提升框架
Figure 6. The"two-stage"hierarchy improvement framework of public transport dependence
表 1 主要调查内容
Table 1. Main survey content
调查维度 调查指标 个体属性 小汽车可用性 自行车可用性 年龄 职业 收人 教育水平 出行环境 家和目的地到交通站点总距离 土地混合利用强度 出行特性 出行目的 出行距离 出行天数 出行次数 出行方式 出行心理 公共交通总体满意度 亲友对公共交通使用影响度 公共交通出行自由度 
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表 2 数据质量检验
Table 2. Data quality test
被检验项 Cronbach's α系数 KMO值 Sig. 出行环境 0.78 0.80 0.00 出行特性 0.81 0.80 0.00 出行心理 0.85 0.84 0.00
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表 3 个体公共交通出行链嵌套数据示例
Table 3. Examples of nested data of individual public transport travel chains
数据类型 记录信息 卡号 512****765028 性别 男 年龄/岁 30 职业 企业职员 教育程度 硕士及以上 收入/元 8 001~15 000 小汽车可用性 比较容易 自行车可用性 一般 出行日期 2018/7/1 2018/7/1 … 2018/7/31 出行模式 B-R R-B … R 上车线路 668路 14号线 14号线 上车时间 07:21 18:42 … 18:30 上车站点 通州杨庄南口 来广营 … 来广营 下车线路 14号线 668路 14号线 下车时间 08:22 19:38 … 19:05 下车站点 来广营 新华联锦园 … 大望路 出行距离/m 15 247 14 752 … 13 982
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表 4 公共交通依赖性关键致因指标离散化结果
Table 4. Discrete results of public transport dependence key causative indicators
指标类别 关键致因指标 划分节点 分类比例/% 标签 分类属性 客观条件因素 家和目的地到站点总距离/min
（Distance to transit，D）D12=14
D23=2721 D1 短 34 D2 中 45 D3 长 收入/元
（Income，I）I12=5 000
I23=15 00031 I1 低 51 I2 中 18 I3 高 小汽车可用性
（Car availability，C）C12=2
C23=422 C1 低 65 C2 中 13 C3 高 主观心理因素 公共交通总体满意度
（Overall satisfaction，S）S12=2
S23=47 S1 低 78 S2 中 15 S3 高 亲友对公共交通使用影响度
（Influence degree，E）E12=2
E23=413 E1 低 67 E2 中 20 E3 高 公共交通出行自由度
（Freedom degree，F）F12=2
F23=411 F1 低 69 F2 中 20 F3 高
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表 5 不同公共交通依赖性典型关联规则
Table 5. Typical association rules of different public transport dependence
序号 前件 后件 支持度 置信度 提升度 1 {家和目的地到站点总距离=D2，收入=I1，小汽车可用性=C1，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F1} {公共交通依赖性=低} 0.018 1.000 7.108 2 {家和目的地到站点总距离=D1，收入=I1，小汽车可用性=C2，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} 0.011 0.500 3.554 3 {家和目的地到站点总距离=D1，收入=I2，小汽车可用性=C2，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} {公共交通依赖性=较低} 0.023 0.750 2.466 4 {家和目的地到站点总距离=D2，收入=I3，小汽车可用性=C3，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} 0.019 0.833 2.740 5 {家和目的地到站点总距离=D3，收入=I1，小汽车可用性=C2，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} {公共交通依赖性=较高} 0.034 0.429 1.848 6 {家和目的地到站点总距离=D2，收入=I2，小汽车可用性=C2，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} 0.015 0.400 1.725 7 {家和目的地到站点总距离=D3，收入=I2，小汽车可用性=C2，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} {公共交通依赖性=高} 0.042 0.500 1.5471 8 {家和目的地到站点总距离=D3，收入=I2，小汽车可用性=C1，公共交通总体满意度=S2，亲友对公共交通使用影响度=E2，公共交通出行自由度=F2} 0.019 0.625 1.934
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表 6 公共交通依赖性层级提升激励政策集
Table 6. Incentive policy sets for promoting public transport dependence hierarchy
政策维度 政策方面 具体措施 政策指标 交通需求管理 交通管制 限号出行、限时通行、区域限行、摇号或排号购车、降低核心区停车位规模 小汽车可用性 经济惩罚 停车费用增加、特定区域拥堵收费、征收燃油税、提高燃油车购置税 小汽车可用性、收入 经济奖励 “碳普惠”奖励机制、公共交通票价折扣、公交乘车优惠换乘、错峰优惠 收入 交通供给管理 交通运行 增设公交专用道、增加公交优先交叉口、设置公交绿波带 公共交通出行方便与自由程度 交通运营 发展响应公交，BRT，定制公交，接驳公交和轻轨等多服务模式、智慧区域调度优化、车厢拥挤度控制、延时运营、发车间隔优化、智慧信息牌和出行APP等实时信息服务优化、增设地铁站出入口或直通廊道、提升公交线网覆盖率、缩短线路长度、充电桩与场站位置优化、改善乘车环境与设施舒适性 家和目的地到交通站点总时间、公共交通出行方便与自由程度 模式整合 优化枢纽站规划与功能、多模式一体化出行整合（如MaaS）、推广TOD发展模式、增加P+R换乘模式范围、公交与地铁衔接优化、加强共享单车、网约车与公共交通接驳合作、多方式付费功能整合 家和目的地到交通站点总时间、公共交通出行方便与自由程度
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[1] TU M T, LI W X, ORFIA O, et al. Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu[J]. Transportation Research Part D: Transport and Environment, 2021(93): 102776. [2] ZHAO X L, YAN X, YU A, et al. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models[J]. Travel Behaviour and Society, 2020(20): 22-35. [3] LUO M, MA Z L, ZHAO W J, et al. An ex-post evaluation of the public acceptance of a license plate-based restriction policy: A case study of Xi'an, China[J]. Transportation Research Part A: Policy and Practice, 2022(155): 259-282. [4] 戢晓峰, 杨春丽. 欠发达地区高铁出行意愿多群组结构方程模型[J]. 北京交通大学学报, 2022, 46(3): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT202203002.htmJI X F, YANG C L. Multi-group structural equation model of travel intention to take high-speed rail in underdeveloped areas[J]. Journal of Beijing Jiaotong University, 2022, 46(3): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BFJT202203002.htm [5] 姚恩建, 李翠萍, 郇宁, 等. 共享单车对通勤走廊出行结构的影响[J]. 华南理工大学学报(自然科学版), 2020, 48(7): 85-92, 142. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202007010.htmYAO E J, LI C P, HUAN N, et al., et al. Impact of shared bicycles on the configuration of travel mode in commuting corridor[J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(7): 85-92, 142. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202007010.htm [6] 云美萍, 刘广洋, 刘芳. 公交服务质量变化对出行方式选择行为的影响[J]. 中国公路学报, 2017, 30(7): 119-125. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201707015.htmYUN M P, LIU G Y, LIU F. Influence of change of public transportation service quality on travel mode choice behavior[J]. China Journal of Highway and Transport, 2017, 30(7): 119-125. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201707015.htm [7] 杨亚璪, 唐浩冬, 彭勇. 考虑偏好差异的后疫情时代居民出行方式选择行为研究[J]. 交通运输系统工程与信息, 2022, 22(3): 15-24. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203003.htmYANG Y Z, TANG H D, PENG Y. Residents' travel mode choice behavior in post-COVID-19 era considering preference differences[J] Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 15-24. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202203003.htm [8] ARROYO R, TOMÁS R, LIDÓN M, et al. Influence of values, attitudes towards transport modes and companions on travel behavior[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020(71): 8-22. [9] 李海杰, 苗蕾, 聂磊, 等. 基于关联规则和主成分分析的高铁旅客购票行为特征研究[J]. 铁道科学与工程学报, 2023, 20(6): 2013-2025. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202306007.htmLI H J, MIAO L, NIE L, et al. High-speed railway passenger ticketing behavior characteristics based on association rules and principal component analysis[J]. Journal of Railway Science and Engineering, 2023, 20(6): 2013-2025. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202306007.htm [10] 龙彦, 黄建玲, 赵晓华, 等. 基于多视图协同交互技术的换道图谱构建与分类[J]. 交通信息与安全, 2022, 40(1): 106-115. doi: 10.3963/j.jssn.1674-4861.2022.01.013LONG Y, HUANG J L, ZHAO X H, et al. Development and classification of lane-changing graph based on multi-view collaborative and interactive techniques[J]. Journal of Transport Information and Safety, 2022, 40(1): 106-115. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.01.013 [11] YANG Y, YUAN Z Z, SUN D Y, et al. Analysis of the factors influencing highway crash risk in different regional types based on improved Apriori algorithm[J]. Advances in Transportation Studies, 2019(49): 165-178. [12] YU W H. Discovering frequent movement paths from taxi trajectory data using spatially embedded networks and association rules[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(3): 855-866. [13] GUO X, WANG D Z W, WU J, et al. Mining commuting behavior of urban rail transit network by using association rules[J]. Physica A: Statistical Mechanics and its Applications, 2020(559): 125094. [14] 王颖志, 沈雅婕, 王立君. 基于改进兴趣度度量与Apriori算法的交通事故多发点成因分析[J]. 浙江大学学报: (理学版), 2021, 48(3): 349-355. https://www.cnki.com.cn/Article/CJFDTOTAL-HZDX202103011.htmWANG Y Z, SHEN Y J, WANG L J. The causes analysis of traffic accident black spots based on improved interest measurement and Apriori algorithm[J]. Journal of Zhejiang University(Science Edition), 2021, 48(3): 349-355(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZDX202103011.htm [15] 许研, 纪雪洪, 叶玫. 基于出行时空数据的分时租赁汽车与网约车出行场景比较研究[J]. 地球信息科学学报, 2021, 23(8): 1461-1472. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202108011.htmXU Y, JI X H, YE M. Travel scenes comparison of time-sharing and car-hailing based on traveling spatiotemporal data[J]. Journal of Geo-information Science, 2021, 23(8): 1461-1472. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202108011.htm [16] CAI Q R. Cause analysis of traffic accidents on urban roads based on an improved association rule mining algorithm[J]. IEEE Access, 2020(8): 75607-75615. [17] 苏芳, 袁勤. 城市公路交通事故关联规则分析[J]. 武汉理工大学学报: (信息与管理工程版), 2020, 42(4): 313-318, 331. https://www.cnki.com.cn/Article/CJFDTOTAL-WHQC202004005.htmSU F, YUAN Q. Analysis of association rules of urban road traffic accidents[J]. Journal of Wuhan University of Technology(IAME), 2020, 42(4): 313-318, 331. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WHQC202004005.htm [18] WU J W, LIAO H. Weather, travel mode choice, and impacts on subway ridership in Beijing[J]. Transportation Research Part A: Policy & Practice 2020(135): 264-279. [19] MARGARETH G, HURTUBIA R, ORTUZAR J D. The role of habit and the built environment in the willingness to commute by bicycle[J]. Travel Behaviour and Society, 2020(20): 62-73. [20] 张昕明, 弓棣, 谢秉磊, 等. 计划行为理论视角下基于出行行为的公交防疫策略影响效果研究[J]. 交通信息与安全, 2021, 39(6): 117-125. doi: 10.3963/j.jssn.1674-4861.2021.06.014ZHANG X M, GONG D, XIE B L, et al. A study of the effectiveness of epidemic prevention policies on public transit usage based on the theory of planned behaviors[J]. Journal of Transport Information and Safety, 2021, 39(6): 117-125. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.06.014 [21] 黎新华, 李俊辉, 黎景壮. 基于改进DTW-AGNES的网约车需求量时间序列聚类研究[J]. 重庆交通大学学报(自然科学版), 2019, 38(8): 13-19. https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201908003.htmLI X H, LI J H, LI J Z. Clustering research on time series of online car-hailing demand based on the improved DTW_AGNES[J]. Journal of Chongqing Jiaotong University(Natural Sciences), 2019, 38(8): 13-19. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CQJT201908003.htm [22] 胡松, 翁剑成, 周伟, 等. 基于扩展计划行为理论的公共交通出行依赖性影响[J]. 吉林大学学报(工学版), 2022, 52 (5): 1037-1044. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202205008.htmHU S, WENG J C, ZHOU W, et al. Influence of travelers' dependence on public transportation based on extended theory of planning behavior[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(5): 1037-1044. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202205008.htm [23] 徐宏, 陈焰, 支艳利, 等. 基于Apriori改进算法的旅游个性化推荐[J]. 微型电脑应用, 2018, 34(1): 74-79. https://www.cnki.com.cn/Article/CJFDTOTAL-WXDY201801022.htmXU H, CHEN Y, ZHI Y L, et al. Personalized travel search based on improved Apriori agorithm[J]. Microcomputer Applications, 2018, 34(1): 74-79(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WXDY201801022.htm [24] DAI J, LIU Z, LI R. Improving the subway attraction for the post-COVID-19 era: The role of fare-free public transport policy[J]. Transport Policy, 2021, 103(490): 21-30. [25] 吕楠, 滕爱, 张彬. "双碳"背景下深圳市公交票价优惠政策优化思路研究[J]. 城市公共交通, 2022(10): 43-48, 52. https://www.cnki.com.cn/Article/CJFDTOTAL-CSGJ202210015.htmLYU N, TENG A, ZHANG B. Research on bus discounting policy with purposes of carbon neutrality and peak emission: Case study in Shenzhen[J]. Urban Public Transport, 2022(10): 43-48, 52. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSGJ202210015.htm -
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