西安建筑科技大学学报(自然科学版)

可交易电子路票方案为解决城市交通拥堵问题提供了新的思路,基于路段的路票扣除方案理论上可以实时反应拥堵情况,并在不同的交通方式之间发挥“惩罚”或“奖励”的不同机制.本文为研究不同出行者对两种机制的反应程度,针对于上海市居民出行数据,建立考虑多种通勤方式的多项logit模型,分析出对每种方式产生显著影响的变量,并针对不同路票价格下的不同通勤方式选择概率的变化量求出了各通勤方式中不同路票价格的弹性值,对比分析了路票交易政策针对不同出行方式时出行者的敏感程度.结论表明,全程驾车出行对票价一直处于富有弹性状态; 绕路一项对票价表现为缺乏弹性; 停车换乘方式对票价的弹性随着票价上升从缺乏弹性变为富有弹性; 全程公交出行方式中,出行需求对票价的弹性随着票价的提升先由缺乏弹性变为富有弹性再到缺乏弹性.

The TCS(tradable credit scheme)provides a new way to solve the problem of urban traffic congestion, the program that deduct the ticket amount based the road could reflection real-time road network congestion theoretically, and play a reward or penalty mechanism in the traffic demand management between different modes of transport. To study the degree of response of the different travelers to the two mechanisms, establish a Mlogit model that consider multiple commute approaches by the data of Shanghai. Analyze variables that have a significant effect on each approach, Calculate the elasticity of the traveler's change in the fare in the different travel modes. The result shows that travel by car is a flexible demand for the fare; the detour road is inelastic demand for the fare; The demand of commuting by P&R for the fare changes from inflexible to flexible as fare increases; With the increase of the fare, the elasticity of the demand of commuting by bus is a parabola for the fare.

引言
1 多通勤方式弹性模型建构
2 基于SP调查的路票交易下通勤方式选择数据收集
3 考虑多方式出行对于票价的弹性计算
4 结论

表1 选择肢列表
Tab.1 Select branch list

表2 出行者个人特性变量表<br/>Tab.2 Personal characteristics variable List

表2 出行者个人特性变量表
Tab.2 Personal characteristics variable List

表3 是否使用过停车换乘系统<br/>Tab.3 Whether to use P & R system

表3 是否使用过停车换乘系统
Tab.3 Whether to use P & R system

表4 对于停车换乘系统的服务评价<br/>Tab.4 Evaluation of the P & R system

表4 对于停车换乘系统的服务评价
Tab.4 Evaluation of the P & R system

表5 市中心停车是否困难<br/>Tab.5 Is it difficult to park downtown

表5 市中心停车是否困难
Tab.5 Is it difficult to park downtown

图1 样本各方式选择比例示意图<br/>Fig.1 Apportionment ratio of travel mode

图1 样本各方式选择比例示意图
Fig.1 Apportionment ratio of travel mode

表6 影响变量参数估计表<br/>Tab.6 Impact variable parameter estimation table

表6 影响变量参数估计表
Tab.6 Impact variable parameter estimation table

表7 样本变量平均值<br/>Tab.7 Average of variables

表7 样本变量平均值
Tab.7 Average of variables

表8 全程驾车弹性值列表<br/>Tab.8 The elasticity of travel by car

表8 全程驾车弹性值列表
Tab.8 The elasticity of travel by car

表9 绕路避费弹性值列表<br/>Tab.9 The elasticity of detour

表9 绕路避费弹性值列表
Tab.9 The elasticity of detour

表10 停车换乘弹性值列表<br/>Tab.10 The elasticity of travel by P & R

表10 停车换乘弹性值列表
Tab.10 The elasticity of travel by P & R

表11 全程公交弹性值列表<br/>Tab.11 The elasticity of travel by bus

表11 全程公交弹性值列表
Tab.11 The elasticity of travel by bus

图2 不同票价下全程驾车选择概率分布<br/>Fig.2 Selection probability of full driving under different fares

图2 不同票价下全程驾车选择概率分布
Fig.2 Selection probability of full driving under different fares

图3 不同票价下全程驾车弹性值分布<br/>Fig.3 the elasticity distribution of commuting by car

图3 不同票价下全程驾车弹性值分布
Fig.3 the elasticity distribution of commuting by car

图4 不同票价下绕路避费通勤概率分布<br/>Fig.4 Selection probability of detour under different fares

图4 不同票价下绕路避费通勤概率分布
Fig.4 Selection probability of detour under different fares

图5 绕路避费票价弹性值分布<br/>Fig.5 the elasticity distribution of detour

图5 绕路避费票价弹性值分布
Fig.5 the elasticity distribution of detour

图6 不同票价下停车换乘通勤选择概率分布<br/>Fig.6 Selection probability of commuting byP & R under different fares

图6 不同票价下停车换乘通勤选择概率分布
Fig.6 Selection probability of commuting byP & R under different fares

图7 停车换乘票价弹性值分布<br/>Fig.7 Elasticity distribution of commuting by P & R

图7 停车换乘票价弹性值分布
Fig.7 Elasticity distribution of commuting by P & R

图8 不同票价下全程公交通勤选择概率分布<br/>Fig.8 Selection probability of commutingby bus under different fares

图8 不同票价下全程公交通勤选择概率分布
Fig.8 Selection probability of commutingby bus under different fares

图9 全程公交票价弹性值分布<br/>Fig.9 Elasticity distribution of commuting by bus

图9 全程公交票价弹性值分布
Fig.9 Elasticity distribution of commuting by bus

[1] SCHRANK D L, LOMAX T J. 2009 urban mobility report [M]. Texas:Texas Transportation Institute, Texas A & M University, 2009.
[2] 高德地图交通大数据.2016年度中国主要城市交通分析报告[EB/OL].
[3] 赵辉.北京市交通拥堵特征及其影响因素分析[D]. 北京: 北京交通大学,2017.5.
[4] JONES P. Urban road pricing: public acceptability and barriers to implementation [M]. Camberley:Edward Elgar Publishing, 1998.263-284.
[5] YANG Hai, WANG Xiaolei, Mananing network mobility with tradable credits [J].Transportation Research Part B, 2011,45(3):580-594.
[6] WU Di, YIN Yafeng, LAWPHONGPANICH S, et al. Design of more equitable congestion pricing and tradable credit schemes for multimodal transportation networks [J]. Transportation Research, 2012, 46(9):1273-1287.
[7] TIAN Lijun, YANG Hai, HUANG Haijun, Tradable credit schemes for managing bottleneck congestion and modal split with heterogeneous users [J]. Transportation Research Part E: Logistics and Transportation Review, 2013, 54(3): 1-13.
[8] AZIZ H A, UKKUSURI S V. Tradable emissions credits for personal travel: a market-based approach to achieve air quality standards [J].International Journal of Advances in Engineering Sciences and Applied Mathematics, 2013,5(2-3):145-157.
[9] ZHANG Xiaoning, YANG Hai, HUANG Haijun. Improving travel efficiency by parking permits distribution and trading [J]. Transportation Research Part B: Methodological, 2011, 45(7): 1018-1034.
[10]NIE Yu, YIN Yafeng. Managing rush hour travel choices with tradable credit scheme [J]. Transportation Research Part B: Methodological 2013, 50(2):1-19.
[11]BAO Yue, GAO Ziyou, XU Meng, et al. Tradable credit scheme for mobility management considering travelers' loss aversion[J]. Transportation Research Part E: Logistics & Transportation Review, 2014, 68: 138-154.
[12]BAO Yue, GAO Ziyou, XU Meng. Traffic assignment under tradable credit scheme: an investigation considering travelers' framing and labelling of credits [J]. IEEE Intelligent Transportation Systems Magazine, 2016, 8(2): 74-89.
[13]XU Meng, GRANT-MULLER S. Trip mode and travel pattern impacts of a Tradable Credits Scheme: A case study of Beijing [J]. Transport Policy, 2016, 47:72-83.
[14]XU Meng, MUSSONE L, GRANT-MULLER S. Effects of a tradable credits scheme on mobility management: a household utility based approach incorporating travel money and travel time budgets [J]. Case Studies on Transport Policy, 2017.
[15]张蕊, 杨静, 雷熙文. 城市交通出行方式选择的随机弹性分析[J]. 交通运输系统工程与信息, 2012, 12(2):132-136.
[16]秦萍, 陈颖翱, 徐晋涛, 等.北京居民出行行为分析: 时间价值和交通需求弹性弹性估算[J].经济地理, 2014,34.(11): 17-22.
[17]云美萍, 张元, 周源, 等.出行弹性概念及其应用[J].同济大学学报(自然科学版),2013,41(9):1359-1365.
[18]包丹文, 邓卫, 顾仕珲. 停车收费对居民出行方式选择的影响分析[J]. 交通运输系统工程与信息,2010,10(3): 80-85.
[19]邵娟.路段容量和路票约束下的交通网络均衡模型[D].南京:东南大学,2015.
[20]关志宏.非集计模型: 交通行为分析的工具[M].北京:人民交通出版社, 2004.
[21]赵路敏. 停车收费对出行方式选择的影响研究[D].北京:北京交通大学, 2007.

备注

引言

1 多通勤方式弹性模型建构

2 基于SP调查的路票交易下通勤方式选择数据收集

3 考虑多方式出行对于票价的弹性计算

4 结论

期刊信息