|Table of Contents|

Nested logit model of combined selection for travel mode and departure time(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2012年02期
Page:
76-83
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Nested logit model of combined selection for travel mode and departure time
Author(s):
YANG Li-ya1; SHAO Chun-fu2; HAGHANI A3
1.School of Public Administration and Policy,Renmin University of China,Beijing 100872,China;2.School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China;3.Department of Civil and Environment Engineering,University of Maryland,Park 20742,Maryland,USA
Keywords:
traffic demand management;  travel mode;  departure time;  combined selection;  NL model;  MNL model;  utility variable;  peak charge
PACS:
U491.1
DOI:
-
Abstract:
Based on the maximum random utility theory,traveler characteristic,travel characteristic and the service level of travel mode were taken as utility variables,travel mode and departure time were taken as alternative parts,and two nested logit(NL) models were built,one structure with departure time located in lower layer and another structure with travel mode located in lower layer.The sample data of resident travel in Beijing City were analyzed,and the travel behavior changes of car travelers were simulated when different car travel costs were charged in morning peak period.Calculation result shows that compared with traditional MNL model,there is better statistic characteristic in NL model.After adjustment,the goodness of fit increases from 0.338 to 0.404.In the two NL models,the structure with departure time located in lower layer has stronger adaptability on sample data than the structure with travel mode located in lower layer.While car travel cost in morning peak period is 5 yuan,72.6% of car travelers will still insist on original travel mode and departure time,22.4% of car travelers will still insist on original travel mode,but will change departure times,4.8% of car travelers will turn to public transit,but will still insist on original departure time,and only 0.2% of car travelers will change travel mode and departure time simultaneously.While car travel cost in morning peak period is 10 yuan,51.7% of car travelers will still insist on original travel mode and departure time,40.4% of car traveler will still insist on original travel mode,but will change departure times,and only 7.9% of car travelers will turn to public transit,but will still insist on original departure time.While car travel cost in morning peak period is 20 yuan,27.5% of car travelers will still insist on original travel mode and departure time,60.6% of car travelers will still insist on original travel mode,but will change departure times,and only 11.9% of car travelers will turn to public transit,but will still insist on original departure time.6 tabs,3 figs,22 refs.

References:

[1] PALMA A D, ROCHAT D. Mode choice for trips to work in Geneva: an empirical analysis[J]. Journal of Transport Geography, 2000, 8(1): 43-51.
[2] BHARAT P, BHATTE O I. Errors in variables in multinomial choice modeling: a simulation study applied to a multinomial logit model of travel mode choice[J]. Transport Policy, 2011, 18(2): 326-335.
[3] LIMTANAKOOL N, DIJST M, SCHWANEN T. The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium and longer-distance trips[J]. Journal of Transport Geography, 2006, 14(5): 327-341.
[4] 许 铁,高林杰,景 鹏,等.基于PSO-SVM的居民出行方式预测模型[J].交通运输系统工程与信息,2011,11(5):155-161. XU Tie, GAO Lin-jie, JING Peng, et al. Prediction model of residents' trip mode based on PSO-SVM[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(5): 155-161.(in Chinese)
[5] 姚荣涵.基于最大信息熵的居民出行分布模型研究[D].长春:吉林大学,2004. YAO Rong-han. Study on inhabitant trip distraibution model based on maximum information entropy[D]. Changchun: Jilin University, 2004.(in Chinese)
[6] 刘炳恩,隽志才,李艳玲,等.居民出行方式选择非集计模型的建立[J].公路交通科技,2008,25(5):116-120. LIU Bing-en, JUAN Zhi-cai, LI Yan-ling, et al. Development of a multinomial logit model for travel mode choice of residents[J]. Journal of Highway and Transportation Research and Development, 2008, 25(5): 116-120.(in Chinese)
[7] LEMP J D, KOCKELMEN K M, DAMIEN P. The continuous cross-nested logit model: formulation and application for departure time choice[J]. Transportation Research Part B: Methodological, 2010, 44(5): 646-661.
[8] SALEH W, FARRELL S. Implications of congestion charging for departure time choice: work and non-work schedule flexibility[J]. Transportation Research Part A: Policy and Practice, 2005, 39(9): 773-791.
[9] OZBAY K, YANMAZ-TUZEL O. Valuation of travel time and departure time choice in the presence of time-of-day pricing[J]. Transportation Research Part A: Policy and Practice, 2008, 42(4): 577-590.
[10] 宗 芳,隽志才,张慧永.基于活动的日活动计划模型[J].吉林大学学报:工学版,2007,37(6):1294-1299. ZONG Fang, JUAN Zhi-cai, ZHANG Hui-yong. Activity-based full day activity pattern model[J]. Journal of Jilin University: Engineering and Technology Edition, 2007, 37(6): 1294-1299.(in Chinese)
[11] BHAT C R. Analysis of travel mode and departure time choice for urban shopping trips[J]. Transportation Research Part B: Methodological, 1998, 32(6): 361-371.
[12] DE JONG G, DALY A, PIETERS M, et al. A model for time of day and mode choice using error components logit[J]. Transportation Research Part E: Logistics and Transportation Review, 2003, 39(3): 245-268.
[13] WEN C H, KOPPELMAN F S. The generalized nested logit model[J]. Transportation Research Part B: Methodological, 2001, 35(7): 627-671.
[14] BEKHOR S, PRASHKER J N. GEV-based destination choice models that account for unobserved similarities among alternatives[J]. Transportation Research Part B: Methodological, 2008, 42(3): 243-262.
[15] PINJARI A R. Generalized extreme value-based errorstruc-tures for multiple discrete-continuous choice models[J]. Transportation Research Part B: Methodological, 2011, 45(5): 474-489.
[16] BIERLAIRE M. A theoretical analysis of the cross-nested logit model[J]. Annals of Operation Research, 2006, 144(1): 287-300.
[17] PAPOLA A. Some developments on the cross-nested logit model[J]. Transportation Research Part B: Methodological, 2004, 38(9): 833-851.
[18] 胡 华,腾 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.(in Chinese)
[19] 刘浩学,冯忠祥,赵炜华,等.中国农村人口活动出行距离分布模型[J].长安大学学报:自然科学版,2010,30(6):68-71. LIU Hao-xue, FENG Zhong-xiang, ZHAO Wei-hua, et al. Trip distance distribution mode of Chinese rural population[J]. Journal of Chang'an University: Natural Science Edition, 2010, 30(6): 68-71.(in Chinese)
[20] 曲大义,于仲臣,庄劲松,等.苏州市居民出行特征分析及交通发展对策研究[J].东南大学学报:自然科学版,2001,31(3):118-123. QU Da-yi, YU Zhong-chen, ZHUANG Jin-song, et al. Analysis on the resident trip characteristics and study on the transport development policies in Suzhou[J]. Journal of Southeast University: Natural Science Edition, 2001, 31(3): 118-123.(in Chinese)
[21] 冯忠祥,刘浩学,张景峰.农村人口出行方式选择模型[J].交通运输工程学报,2010,10(3):77-83. FENG Zhong-xiang, LIU Hao-xue, ZHANG Jing-feng. Selection model of trip modes for rural population[J]. Journal of Traffic and Transportation Engineering, 2010, 10(3): 77-83.(in Chinese)
[22] LAHIRI K, GAO Jian. Bayesian analysis of nested logit model by Markov chain Monte Carlo[J]. Journal of Econometrics, 2002, 111(1): 103-133.

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Last Update: 2012-04-30