特大城市居民出行方式选择行为的混合Logit模型
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摘要
为缓解交通供需冲突、改善居民出行结构模式,基于随机效用最大化理论,以特大城市南京为例,明确了影响出行方式选择行为的多重变量;通过建立混合logit(Mixed logit,ML)数理模型分析了家庭特征、个人属性、出行信息、居民出行OD位置4者的相互作用机理,并分析解读了ML模型输出的统计回归结果。研究结果表明:当时间要求性不高时,慢行方式的竞争性能得以凸显,但随着出行时间的增加,这种优势效用会逐步减弱;而在市郊、长距离行程中,公共交通(含地铁)或小汽车出行方式对旅客吸引力更大。
In order to alleviate the conflicts of traffic supply and demand,improve the resident trip structure and mode,multiple variables impacting the travel mode choice were defined based on the random utility maximization theory with Nanjing City as a specific case.Mixed Logit(ML)model was established for analyzing and interpreting the influence and interaction mechanism of behavioral characteristics among household properties,individual attributes,trip information and the locations of trip ODs.The outcomes of ML model were analyzed by statistic regression.Results show that competitiveness of slow modes(non-motorized transport)become better if the time requirement is not high.However,with travel time increasing,the advantages op the slow modes are gradually weaken.Moreover,in suburbs and long distance trips,public transports(including metro)become more attractive to passengers. The results of statistic regression may help urban planners and transportation policy makers to integrate limited road resources systematically to ensure the resident travel comfortable,convenient and efficient.
In order to alleviate the conflicts of traffic supply and demand,improve the resident trip structure and mode,multiple variables impacting the travel mode choice were defined based on the random utility maximization theory with Nanjing City as a specific case.Mixed Logit(ML)model was established for analyzing and interpreting the influence and interaction mechanism of behavioral characteristics among household properties,individual attributes,trip information and the locations of trip ODs.The outcomes of ML model were analyzed by statistic regression.Results show that competitiveness of slow modes(non-motorized transport)become better if the time requirement is not high.However,with travel time increasing,the advantages op the slow modes are gradually weaken.Moreover,in suburbs and long distance trips,public transports(including metro)become more attractive to passengers. The results of statistic regression may help urban planners and transportation policy makers to integrate limited road resources systematically to ensure the resident travel comfortable,convenient and efficient.
引文
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[12]Kattan L,Barros A G D,Saleemi H.Travel behavior changes and responses to advanced traveler information in prolonged and large-scale network disruptions:a case study of west LRT line construction in the city of calgary[J].Transportation Research Part F:Traffic Psychology and Behaviour,2013,21(3):90-102.
[13]Martínez F,Aguila F,Hurtubia R.The constrained multinomial Logit:a semi-compensatory choice model[J].Transportation Research Part B:Methodological,2009,43(3):365-377.
[14]Mcfadden D,Train K.Mixed MNL models for discrete response[J].Journal of Applied Econometrics,2000,15(5):447-470.
[15]陈艳艳,刘小明,陈金川.城市交通需求管理及应用[M].北京:人民交通出版社,2009:152-192.
[2]Krygsman S,Dijst M,Arentze T.Multimodal public transport:an analysis of travel time elements and the interconnectivity ratio[J].Transport Policy,2004,11(3):265-275.
[3]Krygsman S,Arentze T,Timmermans H.Capturing tour mode and activity choice interdependencies:a co-evolutionary Logit modelling approach[J].Transportation Research Part A:policy and Practice,2007,41(10):913-933.
[4]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.
[5]Abane A M.Travel behaviour in Ghana:empirical observations from four metropolitan areas[J].Journal of Transport Geography,2011,19(2):313-322.
[6]Papadimitriou E,Lassarre S,Yannis G.Introducing human factors in pedestrian crossing behaviour models[J].Transportation Research Part F:Traffic Psychology and Behaviour,2016,36:69-82.
[7]陈坚,晏启鹏,杨飞,等.出行方式选择行为的SEM-Logit整合模型[J].华南理工大学学报:自然科学版,2013,41(2):51-58.Chen Jian,Yan Qi-peng,Yang Fei,et al.SEMLogit integration model of travel mode choice behaviors[J].Journal of South China University of Technology(Natural Science Edition),2013,41(2):51-58.
[8]叶茂,于淼,过秀成,等.历史城区居民出行活动模式选择影响因素分析[J].东南大学学报:自然科学版,2014,44(1):211-215.Ye Mao,Yu Miao,Guo Xiu-cheng,et al.Analysis of effects of contributing factors on choice of activity pattern in historic urban areas[J].Journal of Southeast University(Natural Science Edition),2014,44(1):211-215.
[9]景鹏,隽志才,查奇芬.考虑心理潜变量的出行方式选择行为模型[J].中国公路学报,2014,27(11):84-92.Jing Peng,Juan Zhi-cai,Zha Qi-fen.Incorporating psychological latent variables into travel mode choice model[J].China Journal of Highway and Transport,2014,27(11):84-92.
[10]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.
[11]Hensher D A,Greene W H.The mixed Logit model:the state of practice and warnings for the unwary[J].General Information,2002,30(2):133-176.
[12]Kattan L,Barros A G D,Saleemi H.Travel behavior changes and responses to advanced traveler information in prolonged and large-scale network disruptions:a case study of west LRT line construction in the city of calgary[J].Transportation Research Part F:Traffic Psychology and Behaviour,2013,21(3):90-102.
[13]Martínez F,Aguila F,Hurtubia R.The constrained multinomial Logit:a semi-compensatory choice model[J].Transportation Research Part B:Methodological,2009,43(3):365-377.
[14]Mcfadden D,Train K.Mixed MNL models for discrete response[J].Journal of Applied Econometrics,2000,15(5):447-470.
[15]陈艳艳,刘小明,陈金川.城市交通需求管理及应用[M].北京:人民交通出版社,2009:152-192.