Travel Decision-making Behaviors of Urban Electric Bicycle Users Considering Psychological Latent Variables
-
摘要: 为探究电动自行车用户对不同城市电动自行车规范管理政策的行为响应机理，采用问卷调查方法收集用户的社会人口特征、出行特征、心理特征以及在不同政策下的决策，基于心理接受度等潜变量构建了多指标多因素模型, 得出了潜变量的拟合值，将潜变量作为解释变量引入到行为决策模型中，构成了混合选择模型来分析社会人口变量、出行特征变量和心理潜变量对电动自行车用户出行决策的影响。结果表明：①电动自行车用户的心理特征显著影响其出行决策行为，对政策接受度更高的用户会表现出更强的正面行为倾向；②经济因素是导致出行者继续使用超标违规电动自行车或违反政策的主要原因；③提供报废补贴能中和收入对决策的影响，促进低收入家庭购买符合标准的电动自行车；④政策的实施会促进电动自行车交通向小汽车交通的转移。Abstract: This paper aims to explore electric bicycle users'behavioral responses to different management policies of electric bicycles issued in 2018.A survey is conducted to collect socio-demographic characteristics, travel habits, psychological characteristics of electric bicycle users, and their decision-making against different policies.A multiple indicators and multiple causes model is constructed considering several latent variables such as policy acceptability to obtain the fitted value of the latent variables.Then a hybrid choice model taking the latent variables as explanatory variables is applied to analyze the impacts of social-demographic related variables, travel habit variables, and psychological latent variables on the travel decision of electric bicycle users.The results show that: ①The psychological characteristics of electric bicycle users significantly affect their travel decision-making, and travelers with higher policy acceptability tend to adopt positive behaviors.②Economic factors prompt the travelers to continue to use illegal electric bicycles or violate the policy.③Subsidies for scraping illegal electric bicycles can neutralize the impact ofeconomic factors on decision-making and can promote low-income families to purchase electric bicycles fitting the standard.④The implementation of the policy will promote the mode switching from electric-bicycle traffic to car traffic.
-
Key words:
- Urban traffic /
- Travel decision /
- Hybrid choice model /
- E-bike
-
表 1 问卷数据描述性统计
Table 1. Descriptive statistics for survey data
影响因素 变量 说明 百分比/% 个人属性 年龄/岁 Vage 中年及以上（> 30）0 46.2 年轻（< 30）1 53.8 性别 Vgender 男性0 52.6 女性1 47.4 受教育程度 Vedu 大专, 高中（职高）学历及以下0 43.4 本科及以上1 56.6 职业 Vocc 公司职员0 34.3 非公司职员1 65.7 是否拥有机动车驾驶证 Vadl 无0 27.0 有1 73.0 家庭属性 家庭年收入/（元/年） Vinc 收入低于10万0 67.6 收入高于10万1 32.4 家庭里小孩(< 12岁)的数量 Vchild 无0 41.0 有1 59.0 家庭是否拥有汽车 Vcaro 无0 33.5 有1 66.5 计划购买小汽车的情况 Vcarp 无0 64.4 有1 35.6 出行特征 近3年，涉及与电动自行车相关的事故数 Vcrash 无0 85.0 有1 15.0 电动自行车使用频率/（d/每周） Vebf 高频率（5~7）1 41.5 中频率（3~4）2 29.1 低频率（0~2）3 29.4 日均出行距离/km Vdis 短距离（< 5）1 44.9 中距离（5~10）2 43.4 长距离（> 10）3 11.7 早晚高峰的出行频率 Vpeak 经常0 52.4 偶尔1 47.6 
下载: 导出CSV
表 2 信效度检验结果
Table 2. Results of the reliability and validity test
潜变量 组合信度CR 平均提取方差AVE AVE叹的平方根 接受度1 0.866 0.764 0.874 接受度2 0.875 0.778 0.882 问题意识 0.877 0.642 0.801 主观规范 0.934 0.781 0.884
下载: 导出CSV
表 3 模型拟合指标结果
Table 3. Results of model fitting indices
评价指标 RMSFA CFI TLI SRMR 模型1 0.049 0.970 0.958 0.021 模型2 0.052 0.967 0.954 0.022
下载: 导出CSV
表 4 结构模型结果
Table 4. Results of the structure model
场景 影响变量 接受度 问题意识 主观规范 参数值 t值 参数值 t值 参数值 t值 年龄 0.134 3.823 性别 0.112 3.244 1 家庭年收人拥有汽车 0.152 4.282 0.108 2.989 0.125 3.736 汽车购买计划 —0.173 -5.204 -0.099 -2.851 年龄 0.089 2.407 性别 0.086 2.422 0.112 3.243 2 家庭年收人 0.074 2.052 0.125 3.735 拥有汽车 0.134 3.693 0.108 2.979 汽车购买计划 -0.097 -2.866 -0.099 -2.853
下载: 导出CSV
表 5 模型分析结果比较
Table 5. Comparison of analysis results of the model
场景 变量 以决策4为基础项 以决策1为基础项 决策1 决策2 决策3 决策2 决策3 决策4 参数 OR值 参数 OR值 参数 OR值 参数 OR值 参数 OR值 参数 OR值 常数项 1.24* 3.46 0.43 1.54 -1.30** 0.27 -0.81 0.44 -2.54*** 0.08 -1.24* 0.29 Vedu(1 vs 0) 0.74** 2.10 0.50** 1.65 -0.03 0.97 -0.24 0.79 -0.77*** 0.46 -0.74** 0.48 Vinc(1 vS 0) -1.11** 0.33 -0.55** 0.58 -0.38 0.69 0.56* 1.76 0.73** 2.09 1.11*** 3.05 Vchild(1 vs 0) 0.18 1.20 0.32 1.38 0.64** 1.90 0.14 1.15 0.46* 1.59 -0.18 0.84 Vcaro(l vs 0) -0.13 0.88 -0.49** 0.61 -0.14 0.87 -0.36 0.70 -0.01 0.99 0.13 1.14 1 Vcarp(l vs 0 0.21 1.23 0.63** 1.87 0.39* 1.48 0.42 1.52 0.18 1.20 -0.21 0.81 Vebf(1 vs 3) 1.44** 4.24 1.28*** 3.59 1.28*** 3.60 -0.17 0.85 -0.16 0.85 -1.44*** 0.24 Vebf(2 vs 3) 0.36 1.43 0.43 1.54 0.54** 1.72 0.08 1.08 0.19 1.21 -0.36 0.70 ZACC -0.17* 0.84 0.07 1.08 0.35*** 1.41 0.25*** 1.28 0.52*** 1.68 0.17* 1.19 ZPA -0.15** 0.86 -0.08* 0.93 -0.05 0.95 0.07* 1.08 0.10** 1.10 0.15*** 1.17 常数项 0.51 1.67 0.31 1.36 -1.59*** 0.20 -0.20 0.82 -2.09*** 0.12 -0.51 0.60 Vedu(1 vs 0) 0.68** 1.97 0.50** 1.64 0.38* 1.46 -0.18 0.84 -0.30 0.74 -0.68** 0.51 Vinc(1 vs 0) -1.06*** 0.35 -0.49** 0.61 -0.49** 0.61 0.56 1.76 0.56* 1.76 1.06*** 2.86 Vebf(1 vs 3) 1.09*** 2.98 1.20*** 3.33 0.93*** 2.54 0.11 1.12 -0.16 0.85 -1.09*** 0.34 2 Vebf (2 vs 3) 0.62 1.85 0.76*** 2.14 0.49** 1.63 0.15 1.16 -0.13 0.88 -0.62 0.54 ZACC -0.23*** 0.80 -0.05 0.96 0.19*** 1.20 0.18** 1.20 0.41*** 1.51 0.23*** 1.25 ZSN -0.04 0.96 -0.03 0.97 0.06 1.06 0.01 1.01 0.10** 1.10 0.04 1.04 注：1. *，**，***分别表示p < 0.10，p < 0.05，p < 0.01。
2.决策1为继续使用两轮电动车，但不会注册临时牌照；决策2为注册临时牌照；决策3为购买符合国家标准的电动自行车；决策4为使用其他的交通方式出行。
3.场景1：AIC=1 983.991，SC=2 127.389，-2 Log L=1 923.991；场景2：AIC=1 963.950，SC=2 064.328，-2 Log L=1 921.9。
下载: 导出CSV
-
[1] 高纯. 信号交叉口电动自行车闯红灯行为影响因素研究[D]. 北京: 中国人民公安大学, 2020.GAO Chun. Research of the influencing factors of red-Light running behavior of e-bike[D]. Beijing: People's Public Security University of China, 2020. (in Chinese). [2] 柏璐. 城市道路电动自行车交通特性与安全影响研究[D]. 南京: 东南大学, 2017.BAI Lu. Research on traffic characteristics and safety effect of electric bicycle in urban street[D]. Nanjing: Southeast University, 2017. (in Chinese). [3] WEINERT J, MA C, CHERRY C. The transition to electric bikes in China: History and key reasons for rapid growth[J]. Transportation, 2007, 34(3): 301-318. doi: 10.1007/s11116-007-9118-8 [4] NORDFJRN T, RUNDMO T. Environmental norms, transport priorities and resistance to change associated with acceptance of push measures in transport[J]. Transport Policy, 2015(44): 1-8. http://www.sciencedirect.com/science/article/pii/S0967070X15300317 [5] SUN Xianglong, FENG Shumin, LU Jian. Psychological factors influencing the public acceptability of congestion pricing in China[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2016(41): 104-112. [6] WANG Lanlan, XU Jintao, QIN Ping. Will a driving restriction policy reduce car trips?The case study of Beijing, China[J]. Transportation Research Part A: Policy and Practice, 2014(67): 279-290. http://www.sciencedirect.com/science/article/pii/S0965856414001797 [7] LIU Zhiyong, LI Ruimin, WANG Xiaokun, et al. Noncompliance behavior against vehicle restriction policy: A case study of Langfang, China[J]. Transportation Research Part A: Policy and Practice, 2020(132): 1020-1033. http://www.sciencedirect.com/science/article/pii/S0965856419305580 [8] GUO Yuntao, WANG Jian, PEETA S, et al. Personal and societal impacts of motorcycle ban policy on motorcyclists'home-to-work morning commute in China[J]. Travel Behaviour and Society, 2020(19): 137-150. http://www.sciencedirect.com/science/article/pii/S2214367X19303151 [9] JIA Ning, ZHANG Yidan, HE Zhengbing, et al. Commuters'acceptance of and behavior reactions to license plate restriction policy: A case study of Tianjin, China[J]. Transportation Research Part D: Transport and environment, 2017(52): 428-440. http://www.sciencedirect.com/science/article/pii/S1361920915301917 [10] COOLS M, BRIJS K, TORMANS H, et al. The socio-cognitive links between road pricing acceptability and changes in travel-behavior[J]. Transportation Research Part A: Policy and Practice, 2011, 45(8): 779-788. doi: 10.1016/j.tra.2011.06.006 [11] ROSE G. E-bikes and urban transportation: emerging issues and unresolved questions[J]. Transportation, 2012, 39(1): 81-96. doi: 10.1007/s11116-011-9328-y [12] GUO Yanyong, LI Zhibin, WU Yao, et al. Evaluating factors affecting electric bike users'registration of license plate in China using Bayesian approach[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018(59): 212-221. http://www.sciencedirect.com/science/article/pii/S1369847816304673 [13] 张鹏辉. 常规公交与城市轨道交通出行方式转移行为研究[D]. 长安: 长安大学, 2012.ZHANG Penghui. Study of travel mode shift between regular public bus and urban rail transit[D]. Chang'an: Chang'an University, 2012. (in Chinese). [14] BHAT C R, DUBEY S K. A new estimation approach to integrate latent psychological constructs in choice modeling[J]. Transportation Research Part B: Methodological, 2014(67): 68-85. http://www.sciencedirect.com/science/article/pii/S0191261514000678 [15] VIJ A, WALKER J L. How, when and why integrated choice and latent variable models are latently useful[J]. Transportation Research Part B: Methodological, 2016(90): 192-217. http://www.sciencedirect.com/science/article/pii/S019126151630234X [16] SCHWARTZ S H. Normative influe-nces on altruism[J]. Advances in Experimental Social Psychology, 1977(10): 221-279. http://www.sciencedirect.com/science/article/pii/S0065260108603585 [17] AJZEN I. The theory of planned behavior[J]. Organizational Behavior & Human Decision Processes, 1991, 50(2): 179-211. [18] 陈月霞, 陈龙, 查奇芬等. 基于低碳心理潜变量Logit模型的出行方式预测模型[J]. 公路交通科技, 2017, 34(9): 100-108+137. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201709015.htmCHEN Yuexia, CHEN Long, CHA Qifen, et al. A travel mode forecasting model based on low-carbon psycho-logical latent variable logit model[J]. Journal of Highway and Transportation Research and Development, 2017, 34(9): 100-108+137. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK201709015.htm [19] WEI Longyu, XIN Feifei, AN Kang, et al. Comparison study on travel characteristics between two kinds of electric bike[J]. Procedia-Social and Behavioral Sciences, 2013(96): 1603-1610. http://www.sciencedirect.com/science/article/pii/S1877042813023082 [20] CHERRY C, YANG Hongtai, JONES L, et al. Dynamics of electric bike ownership and use in Kunming, China[J]. Transport Policy, 2016(45): 127-135. http://www.sciencedirect.com/science/article/pii/S0967070X15300524 -
点击查看大图
图(4) / 表(5)
计量
- 文章访问数: 271
- HTML全文浏览量: 59
- PDF下载量: 24
- 被引次数: 0