Resident travel time choice and congestion pricing policy
-
摘要: 应用基于活动的出行需求预测方法, 分析了居民出行的时间分布规律与影响居民出行时间选择的因素, 分别建立了出发和到达时间选择模型, 用长春市居民出行调查数据对模型进行了标定和验证。用已建模型对比评价了两种拥挤收费政策, 证明了调整高峰时段小汽车出行费用策略不仅可以使居民的高峰时段出行减少大约18%, 而且可以抑制小汽车出行, 调整城市交通方式分配结构, 说明已建模型可以全面有效地进行交通需求管理政策的评价。Abstract: The distribution rules of resident travel time and the influence factors of resident travel time choice were analysed by activity-based travel demand forecasting method, two time choice models that could forecast the time of the start and end points of a trip were developed respectively, the models were estimated and validated with the survey data in Changchun city.It is proved that two strategies of increasing car costs in peak periods not only can reduce resident travel by 18% in peak time periods, but also restrain resident travel by car and adjust traffic mode structure in urban area.The results indicate that established activity-based time choice models can completely and effectively evaluate transportation management policy.
-
Key words:
- traffic planning /
- travel demand forecasting /
- travel time /
- congestion pricing /
- policy /
- disaggregated model
-
图 1 基于活动的出行需求预测模型结构
Figure 1. Structure of activity-based travel demand forecasting model
表 1 出发与到达时间分布
Table 1. Statistic of start and end times
时间段 出发次数 到达次数 早晨(EA) 0:00~6:59 5609 (15.1%) 2971 (8.0%) 早高峰(AM) 7:00~9:29 9249 (24.9%) 10945 (29.5%) 中午时段(MD) 9:30~15:59 13120 (35.3%) 12212 (32.9%) 晚高峰(PM) 16:00~18:59 8065 (21.7%) 9360 (25.2%) 晚上(LA) 19:00~24:00 1108 (3.0%) 1665 (4.5%) 总次数 37151 37153 
下载: 导出CSV
表 2 时间模型变量
Table 2. Variables of time choice models
影响因素类别 影响因素 变量 出行者特性 个人属性 性别 G 年龄 < 20岁 AA 20~40岁 AB 40~60岁 AC > 60岁 AD 家庭属性 家庭月总收入/元 0~500 IA 501~1000 IB 1001~2000 IC 2001~3000 ID 3001~5000 IE > 5000 IF 家中儿童情况 有否6岁以下儿童 Ch 家中车辆拥有情况 有无小汽车 C 有无摩托车 M 是否有2辆以上的自行车 B 出行特性 一阶活动(工作、生活、娱乐、在家活动) P 二阶活动(工作、生活、娱乐、没有二阶活动、二阶生活+娱乐) S 中途驻停(去时驻停、回时驻停、去回时都有或没有驻停) I 有否工作子往返 Su
下载: 导出CSV
表 3 时间选择模型参数估计结果
Table 3. Estimation results of time choice models
变量 出发时间模型 到达时间模型 EA AM MD PM EA AM MD PM V T V T V T V T V T V T V T V T Constant值 1.34 7.11 2.84 10.62 5.48 21.41 2.68 10.42 0.70 3.12 2.64 10.13 5.12 23.15 2.51 11.89 L -0.010 -0.500 0.020 1.800 -0.003 -0.300 0.010 1.400 -0.140 -5.380 0.030 1.620 -0.010 -1.500 0.001 0.130 G 0.21 3.13 0.18 2.78 0.33 5.19 0.13 1.97 0.41 6.06 0.13 2.25 0.32 5.83 0.17 3.17 AA - - - - - - - - - - -0.96 -7.60 -0.34 -4.23 -0.35 -4.42 AB -1.07 -12.64 0.12 1.42 -0.41 -6.06 -0.12 -1.74 -1.60 -19.06 -0.50 -4.37 -0.49 -7.73 -0.23 -3.66 AC -0.71 -8.35 0.37 4.39 - - - - -0.84 -10.82 -0.20 -1.81 - - - - IB - - - - - - - - 0.20 2.35 - - - - - - IC - - - - - - - - 0.16 2.21 - - - - - - Ch 0.24 1.85 0.46 3.80 0.32 2.65 0.40 3.28 0.33 2.67 0.42 4.11 0.35 3.39 0.46 4.49 C - - - - - - - - 2.56 5.13 -0.64 -1.51 - - - - M -0.19 -1.50 -0.21 -1.76 -0.16 -1.38 -0.23 -1.89 0.15 1.16 -0.14 -1.28 - - - - B - - - - - - - - 0.24 2.93 - - - - - - P 0.13 2.05 0.55 8.70 0.71 11.50 -0.49 -7.48 0.42 6.97 0.09 1.51 0.84 14.95 -0.47 -8.16 S 0.18 6.59 0.10 3.65 -0.19 -7.12 0.10 3.81 0.04 1.42 0.07 3.00 -0.26 -11.77 0.03 1.14 I - - -0.22 -5.29 -0.28 -7.05 -0.07 -1.59 - - -0.13 -3.71 -0.29 -8.79 -0.04 -1.12 Su -0.09 -2.56 -0.83 -13.45 -1.48 -24.18 -0.20 -3.63 -0.06 -1.56 -0.32 -5.75 -1.44 -25.71 -0.13 -3.47
下载: 导出CSV
表 4 出发时间变化
Table 4. Changes of start times
年龄 变量值(AA, AB, AC) L p1/p5 p2/p5 19 (1, 0, 0) 1.02 5.15 12.81 35 (0, 1, 0) 1.35 1.75 14.59 50 (0, 0, 1) 1.35 2.51 18.17
下载: 导出CSV
表 5 需求管理政策下的出发时间
Table 5. Travel times under management policies
出行方式 时间段 出发时间变化/% 小汽车出行费用增加10% 高峰时段小汽车出行费用增加100% 全方式 EA/LA 0.002 0.013 AM/LA -0.026 -0.133 MD/LA 0.004 0.024 PM/LA -0.015 -0.091 小汽车 EA/LA 0.112 1.000 AM/LA -1.563 -10.616 MD/LA 0.267 1.897 PM/LA -0.878 -7.265
下载: 导出CSV
-
[1] Juan Zhi-cai, Li Zhi-yao, Zong Fang. A review of activity-based travel demand forecasting method[J]. Journal of Highway and Transportation Research and Development, 2005, 22(6): 108-113. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ202007007.htm [2] 陆化普. 交通规划理论与方法[M]. 北京: 清华大学出版社, 1998. [3] 王济川, 郭志刚. Logistic回归模型——方法与应用[M]. 北京: 高等教育出版社, 2001. [4] Jin An. On methodology of parameter estimation in Logit model[J]. Journal of Transportation Systems Engineering and Information Technology, 2004, 4(1): 71-75. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX202107001.htm [5] Hu Da-wei, Xuan Deng-dian, Planning method of highway express traveler network system[J]. Journal of Chang'an University(Natural Science Edition), 2004, 24(2): 83-86. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201404003.htm [6] Ma Rong-guo, Liu Hong-ying, Liang Guo-hua. Evaluation indices on urban passenger transportation structure[J]. Journal of Traffic and Transportation Engineering, 2004, 4(1): 87-91. (in Chinese) http://transport.chd.edu.cn/article/id/200401022 [7] Zhang Fei-zhou, Yan Lei, Fan Yue-zu, et al. Research on dispatching methods of public traffic vehicles in intelligent transport system[J]. China Journal of Highway and Transport, 2003, 16(2): 82-85. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202102002.htm -
点击查看大图
计量
- 文章访问数: 339
- HTML全文浏览量: 117
- PDF下载量: 253
- 被引次数: 0
