吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (6): 1763-1770.doi: 10.13229/j.cnki.jdxbgxb201506005

• • 上一篇    下一篇

多方式诱导下通勤出行链交通方式组合选择行为模型

赵丹1， 邵春福2， 王军利1， 李娟2， 王博彬2   

  1. 1. 中国人民公安大学 交通管理工程系, 北京 102623;
    2.北京交通大学 城市交通复杂系统理论与技术教育部重点实验室, 北京 100044
  • 收稿日期:2014-04-24 出版日期:2015-11-01 发布日期:2015-11-01
  • 作者简介:赵丹(1983-),女,博士研究生,讲师.研究方向:城市交通规划与管理.E-mail:zhaodanbjtu@163.com
  • 基金资助:
    国家自然科学基金项目(51178032); “973”国家重点基础研究发展计划项目(2012CB725403); 国家“十二五”科技支撑计划项目(2013BAG18B01); 2013年度教育部人文社会科学研究青年基金项目(13YJCZH082); 中国人民公安大学基本科研业务费项目(2014JKF01130)

Modelling combined mode choice behavior of commute trip chain under multi-modal guidance

ZHAO Dan1, SHAO Chun-fu2, WANG Jun-li1, LI Juan2, WANG Bo-bin2   

  1. 1.Traffic Management Engineering Department, People's Public Security University of China, Beijing 102623, China;
    2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2014-04-24 Online:2015-11-01 Published:2015-11-01

RICH HTML

0   

PDF (PC)

722

摘要： 从出行链的角度出发研究多方式诱导信息对通勤出行方式选择的影响。通过RP&SP调查获取通勤者的实际和意向出行链数据,建立了综合考虑尺度系数差异、非显化异质性效应和参照依赖效应的mixed logit模型,并设计仿真方法求解。结果表明,尺度系数差异解释了RP数据与SP数据融合时隐含的方差差异;异质性和参照依赖效应的引入可以度量偏好差异较大的随机变量的影响程度,反映随机变量的偏好分布;综合考虑3种因素的mixed logit模型比多项logit模型和普通mixed logit模型的精度高,解释能力更强。参数标定结果表明:多方式诱导信息服务对引导小汽车通勤出行链转向其他交通方式有积极作用,有利于从源头上缓解道路交通拥堵。

关键词: 交通运输系统工程, 交通方式组合, mixed logit模型, 出行链, 融合数据, 交通信息, 尺度系数

Abstract: To study the influence of multi-modal guidance information on the travel mode of commuters from the viewpoint of trip chain, A Revealed Preference (RP) and Stated Preference (SP) survey was carried out to get the revealed and stated trip chain data. Then a mixed logit model, which takes scale parameter difference, heterogeneity and reference dependence effect into account, was established. A simulation method was proposed to complete estimation. The estimation results show that the introduction of scale parameter difference is helpful to explain the unobvious variance differences between RP data and SP data when they are combined together; the adoption of heterogeneity and reference dependence effects are not only conductive to measure the impacts of some variables, whose random preferences vary obviously across individuals, more accurately, but also benefits to reflect the preference distribution of random variables. The accuracy and predictive capacity of the mixed logit model considering the three aspects are better than the multinomial logit model and the ordinary mixed logit model. It is also manifested that multi-modal guidance information service contributes to encourage commuters to shift from car trip-chain to other modes, and helps to ease traffic congestion at source.

Key words: engineering of communication and transportation system, combined travel mode, mixed logit model, trip chain, combined data, traffic information, scale parameter

中图分类号: 

  • U491
[1] McGuckin N, Nakamoto Y. Trips, chains, and tours-using an operational definition[R]. Proceedings of National Household Travel Survey Conference Transportation, Washington D C, 2004:1-15.
[2] Prillwitz J, Barr S. Moving towards sustainability? Mobility styles, attitudes and individual travel behavior[J]. Journal of Transport Geography,2011,19(6):1590-1600.
[3] 胡华,滕靖,高云峰,等. 多模式公交信息服务条件下的出行方式选择行为研究[J]. 中国公路学报,2009,22(2):87-92.
Hu Hua, Teng Jing, Gao Yun-feng, et al. Research on travel mode choice behavior under integrated multi-modal transit information service[J]. China Journal of Highway and Transport,2009,22(2): 87-92.
[4] Ben-Akiva M, Morikawa T. Estimation of switching models from revealed references and stated intentions[J]. Transportation Research Part A,1990,24(6):485-495.
[5] 张峻屹, Fujiwara A, Soe T. 基于SP调查的缅甸仰光市居民收入对其出行方式选择的影响研究[J]. 交通运输系统工程与信息,2008,8(4):49-62.
Zhang Jun-yi, Fujiwara A, Soe T. Capturing travelers'stated mode choice preferences under the influence of income in yangon city, myanmar[J]. Journal of Transportation System Engineering and Information Technology,2008,8(4):49-62.
[6] Hensher D A, Bradley M. Using stated response data to enrich revealed preference discrete choice models[J]. Marketing Letters,1993,4(2):139-152.
[7] 罗清玉,孙宝凤,吴文静,等. 基于Mixed logit模型的拥挤收费下交通方式分担率预测[J]. 吉林大学学报:工学版,2010,40(5):1230-1234.
Luo Qing-yu, Sun Bao-feng, Wu Wen-jing,et al. Predicting traffic mode split under congestion pricing based on mixed logit model[J]. Journal of Jilin University (Engineering and Technology Edition),2010,40(5): 1230-1234.
[8] 张天然,杨东援,赵娅丽,等. RP/SP融合数据的Mixed Logit和Nested Logit模型估计对比[J]. 同济大学学报:自然科学版,2008,36(8):1073-1078.
Zhang Tian-ran,Yang Dong-yuan,Zhao Ya-li,et al. Comparative study of RP/SP combined data estimation between mixed logit and nested logit[J]. Journal of Tongji University (Natural Science),2008,36(8):1073-1078.
[9] Börjesson M. Joint RP-SP data in a mixed logit analysis of trip timing decisions[J]. Transportation Research Part E,2008, 44(6):1025-1038.
[10] Hensher D, Rose J, Greene W. Combining RP and SP data: biases in using the nested logit ‘trick’-contrasts with flexible mixed logit incorporating panel and scale effects[J]. Journal of Transport Geography,2008,16(2):126-133.
[11] 郑柯,郑勇. 基于极值分布的离散选择模型尺度参数[J]. 交通运输工程学报,2009,9(1): 92-95.
Zheng Ke, Zheng Yong. Scale parameter of discrete choice model based on extreme value distribution[J]. Journal of Traffic and Transportation Engineering,2009,9(1):92-95.
[12] 周伟,赵胜川. 基于Mixed Logit模型的路线选择行为量化分析[J]. 吉林大学学报:工学版,2013,43(2):304-309.
Zhou Wei,Zhao Sheng-chuan. Quantitative analysis of traveler route choice behavior based on Mixed Logit model[J]. Journal of Jilin University (Engineering and Technology Edition),2013,43(2): 304-309.
[13] Bhat C R, Castelar S. A unified mixed logit framework for modeling revealed and stated preferences: formulation and application to congestion pricing analysis in San Francisco Bay area[J]. Transportation Research Part B,2002,36(7): 593-616.
[14] Mcfadden D, Train K. Mixed MNL models for discrete response[J]. Journal of Applied Econometrics,2000,15(5): 447-470.
[15] 赵丹,邵春福,孙壮志,等. 基于RP/SP融合数据的市郊铁路出行行为分析模型[J]. 交通运输系统工程与信息,2010,10(6):156-162.
Zhao Dan,Shao Chun-fu,Sun Zhuang-zhi,et al. Travel behavior analysis model of suburban railway based on combination of RP and SP data[J]. Journal of Transportation System Engineering and Information Technology,2010,10(6):156-162.
[16] Brownstone D, Bunch D, Train K. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles[J]. Transportation Research Part B,2000,34(5):315-338.
[17] 王劲峰,姜成晟,李连发. 空间抽样与统计推断[M]. 北京:科学出版社,2009.
[18] 关宏志. 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.
[1] 陈永恒,刘芳宏,曹宁博. 信控交叉口行人与提前右转机动车冲突影响因素[J]. 吉林大学学报(工学版), 2018, 48(6): 1669-1676.
[2] 常山,宋瑞,何世伟,黎浩东,殷玮川. 共享单车故障车辆回收模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1677-1684.
[3] 曲大义,杨晶茹,邴其春,王五林,周警春. 基于干线车流排队特性的相位差优化模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1685-1693.
[4] 宗芳, 齐厚成, 唐明, 吕建宇, 于萍. 基于GPS数据的日出行模式-出行目的识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1374-1379.
[5] 刘翔宇, 杨庆芳, 隗海林. 基于随机游走算法的交通诱导小区划分方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1380-1386.
[6] 钟伟, 隽志才, 孙宝凤. 不完全网络的城乡公交一体化枢纽层级选址模型[J]. 吉林大学学报(工学版), 2018, 48(5): 1387-1397.
[7] 刘兆惠, 王超, 吕文红, 管欣. 基于非线性动力学分析的车辆运行状态参数数据特征辨识[J]. 吉林大学学报(工学版), 2018, 48(5): 1405-1410.
[8] 宗芳, 路峰瑞, 唐明, 吕建宇, 吴挺. 习惯和路况对小汽车出行路径选择的影响[J]. 吉林大学学报(工学版), 2018, 48(4): 1023-1028.
[9] 栾鑫, 邓卫, 程琳, 陈新元. 特大城市居民出行方式选择行为的混合Logit模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[10] 陈永恒, 刘鑫山, 熊帅, 汪昆维, 谌垚, 杨少辉. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报(工学版), 2018, 48(3): 677-687.
[11] 王占中, 卢月, 刘晓峰, 赵利英. 基于改进和声搜索算法的越库车辆排序[J]. 吉林大学学报(工学版), 2018, 48(3): 688-693.
[12] 李志慧, 胡永利, 赵永华, 马佳磊, 李海涛, 钟涛, 杨少辉. 基于车载的运动行人区域估计方法[J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[13] 陈松, 李显生, 任园园. 公交车钩形转弯交叉口自适应信号控制方法[J]. 吉林大学学报(工学版), 2018, 48(2): 423-429.
[14] 苏书杰, 何露. 步行交通规划交叉路口行人瞬时动态拥塞疏散模型[J]. 吉林大学学报(工学版), 2018, 48(2): 440-447.
[15] 孟品超, 李学源, 贾洪飞, 李延忠. 基于滑动平均法的轨道交通短时客流实时预测[J]. 吉林大学学报(工学版), 2018, 48(2): 448-453.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发