Understanding Heterogeneous Passenger Route Choice in Municipal Rail Transit with Express and Local Trains: An Empirical Study in Shanghai

Wei Peng , Jing Teng , Hui Wang

Urban Rail Transit ›› 2024, Vol. 10 ›› Issue (2) : 122 -143.

PDF
Urban Rail Transit ›› 2024, Vol. 10 ›› Issue (2) : 122 -143. DOI: 10.1007/s40864-024-00214-8
Original Research Papers

Understanding Heterogeneous Passenger Route Choice in Municipal Rail Transit with Express and Local Trains: An Empirical Study in Shanghai

Author information +
History +
PDF

Abstract

The express/local mode of municipal rail transit provides passengers with multiple alternatives to achieve more efficient and superior travel, in contrast to the conventional all-stop operation mode. However, the various route choices (including direct express trains, direct local trains, or transfers) covering different passenger groups pose a significant challenge to passenger flow assignment. To understand route choice behavior, it is crucial to measure the passenger heterogeneity (variability in individual and trip attributes) in order to propose targeted solutions for operation schemes and service planning. This paper proposes a hybrid model by integrating structural equation modeling and the mixed logit model under express/local mode to estimate the impact of passenger heterogeneity on route choice. An empirical study with revealed preference and stated preference surveys carried out in Shanghai revealed how individual and trip attributes quantitatively impact the sensitivity of factors in route choice. The results show that age and trip purpose are more significant factors. Compared to the control group, the probability of express trains is reduced by 10.22% for the elderly and by 11.36% for non-commuters. Our findings can provide support for more reasonable operation schemes and more targeted services.

Keywords

Municipal rail transit / Route choice behavior / Express/local mode / Passenger sensitivity / Passenger heterogeneity

Cite this article

Download citation ▾
Wei Peng, Jing Teng, Hui Wang. Understanding Heterogeneous Passenger Route Choice in Municipal Rail Transit with Express and Local Trains: An Empirical Study in Shanghai. Urban Rail Transit, 2024, 10(2): 122-143 DOI:10.1007/s40864-024-00214-8

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Teng J, Hui W, Zhang C, Liu S. Evaluation of operating schemes on municipal rail transit with express/local mode. Transp Res Rec J Transp Res Board, 2021, 2675: 583-597

[2]

Cats O, Wang Q, Zhao Y. Identification and classification of public transport activity centres in Stockholm using passenger flows data. J Transp Geogr, 2015, 48: 10-22

[3]

Baek J, Sohn K. An investigation into passenger preference for express trains during peak hours. Transportation, 2016, 43: 623-641

[4]

Tang L, D’Ariano A, Xu X Scheduling local and express trains in suburban rail transit lines: mixed–integer nonlinear programming and adaptive genetic algorithm. Comput Oper Res, 2021, 135: 105436

[5]

Chen J, Pu Z, Guo X Multiperiod metro timetable optimization based on the complex network and dynamic travel demand. Phys A, 2023, 611: 128419

[6]

Tang L, Xu X. Optimization for operation scheme of express and local trains in suburban rail transit lines based on station classification and bi-level programming. J Rail Transp Plan Manag, 2022, 21: 100283

[7]

Di D, Yang D. Passenger flow analysis model about express/slow train in urban rail transportation corridor. J Tongji Univ Natl Sci, 2014, 42: 78-83.

[8]

Xie X, Zhang X, Chen J The discrete choice model of urban rail transit passengers’ route choice. J Transp Syst Eng Inf Technol, 2014, 14: 127-131.

[9]

Zhao X, Sun Q, Ding Y Passenger choice behavior for regional rail transit under express/local operation with overtaking. J Transp Syst Eng Inf Technol, 2016, 16: 104-109.

[10]

Bekhor S, Prato CG. Methodological transferability in route choice modeling. Transp Res Part B Methodol, 2009, 43: 422-437

[11]

Amirgholy M, Gonzales EJ. Efficient frontier of route choice for modeling the equilibrium under travel time variability with heterogeneous traveler preferences. Econ Transp, 2017, 11–12: 1-14

[12]

Leng J, Zhai J, Li Q, Zhao L. Construction of road network vulnerability evaluation index based on general travel cost. Phys A, 2018, 493: 421-429

[13]

Feng Y, Zhao J, Sun H Choices of intercity multimodal passenger travel modes. Phys A, 2022, 600: 127500

[14]

Teng J, Liu W-R. Development of a behavior-based passenger flow assignment model for urban rail transit in section interruption circumstance. Urban Rail Transit, 2015, 1: 35-46

[15]

Larsen OI, Sunde Ø. Waiting time and the role and value of information in scheduled transport. Res Transp Econ, 2008, 23: 41-52

[16]

Fernández E, Joaquín C. Transit assignment for congested public transport systems: an equilibrium model. Transp Sci, 1993, 2: 133-147.

[17]

Gao Y, Yang L, Gao Z. Energy consumption and travel time analysis for metro lines with express/local mode. Transp Res Part D Transp Environ, 2018, 60: 7-27

[18]

Li Z, Mao B, Bai Y, Chen Y. Integrated optimization of train stop planning and scheduling on metro lines with express/local mode. IEEE Access, 2019, 7: 88534-88546

[19]

Zhang H, Han B (2019) Optimizing Train plan of express-local modes for suburban rail transit. In: 2019 4th international conference on electromechanical control technology and transportation (ICECTT). pp 336–340

[20]

Si B, Mao B, Liu Z. Passenger flow assignment model and algorithm for urban railway traffic network under the condition of seamless transfer. J China Railw Soc, 2007, 29: 12-18.

[21]

Pel AJ, Bel NH, Pieters M. Including passengers’ response to crowding in the Dutch national train passenger assignment model. Transp Res Part A Policy Pract, 2014, 66: 111-126

[22]

Douglas N, Karpouzis G (2006) Estimating the passenger cost of train overcrowding

[23]

Lam S-H, Xie F. Transit path-choice models that use revealed preference and stated preference data. Transp Res Rec, 2002, 1799: 58-65

[24]

Guo Z, Wilson NHM. Assessing the cost of transfer inconvenience in public transport systems: A case study of the London underground. Transp Res Part A Policy Pract, 2011, 45: 91-104

[25]

Cheng Y, Ye X, Fujiyama T. How does interchange affect passengers’ route choices in urban rail transit? A case study of the Shanghai Metro. Transp Lett, 2022, 14: 416-426

[26]

Walker JL. Extended discrete choice models: integrated framework, flexible error structures, and latent variables, 2001, Cambridge: Massachusetts Institute of Technology

[27]

Prato CG, Bekhor S, Pronello C. Methodology for exploratory analysis of latent factors influencing drivers’ behavior. Transp Res Rec, 2005, 1926: 115-125

[28]

Prato CG, Bekhor S, Pronello C. Latent variables and route choice behavior. Transportation, 2012, 39: 299-319

[29]

Lu K, Han B, Zhou X. Smart urban transit systems: from integrated framework to interdisciplinary perspective. Urban Rail Transit, 2018, 4: 49-67

[30]

Kurauchi F, Schmöcker J-D, Fonzone A Estimating weights of times and transfers for hyperpath travelers. Transp Res Rec J Transp Res Board, 2012, 2284: 89-99

[31]

Liu W, Liu X. Route choice research in rail transit network based on passenger classification. J Transp Eng Inf, 2016, 14: 81-86.

[32]

Abou-Zeid M, Ben-Akiva M, Bierlaire M Attitudes and value of time heterogeneity. Applied transport economics–a management and policy perspective, 2010, Stockholm Sweden: De Boeck Publishing

[33]

Zhao P, Qu R, Song W. Passenger choice behavior of high-speed railway considering individual heterogeneity. J Beijing Jiaotong Univ, 2019, 43: 117

[34]

Liu J, Hao X. Travel mode choice in city based on random parameters logit model. J Transp Syst Eng Inf Technol, 2019, 19: 108

[35]

Cascetta E, Russo F, Viola FA, Vitetta A. A model of route perception in urban road networks. Transp Res Part B Methodol, 2002, 36: 577-592

[36]

Domencich TA, McFadden D. Urban travel demand–a behavioral analysis. Can J Econ, 1977, 10: 724

[37]

Koppelman FS, Sethi V. Incorporating variance and covariance heterogeneity in the generalized nested logit model: an application to modeling long distance travel choice behavior. Transp Res Part B Methodol, 2005, 39: 825-853

[38]

Mwale M, Luke R, Pisa N. Factors that affect travel behaviour in developing cities: a methodological review. Transp Res Interdiscip Perspect, 2022, 16: 100683

[39]

Bekhor S, Reznikova L. Application of cross-nested logit route choice model in stochastic user equilibrium traffic assignment. Transp Res Rec J Transp Res Board, 2007, 2003: 41-49

[40]

Zhou Z, Chen A, Bekhor S. C-logit stochastic user equilibrium model: formulations and solution algorithm. Transportmetrica, 2012, 8: 17-41

[41]

Ben-Akiva M, Bierlaire M. Hall RW. Discrete choice methods and their applications to short term travel decisions. Handbook of transportation science, 1999, Boston, MA: Springer 5-33

[42]

Krueger R, Bierlaire M, Daziano RA Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity. J Choice Model, 2021, 41(1–2): 100323

[43]

Tang L, Xu X. Study of suburban passengers’ route choice behavior under express/local train mode. J Wuhan Univ Technol, 2018, 42: 947-951.

[44]

Shang W, Han K, Ochieng W, Angeloudis P. Agent-based day-to-day traffic network model with information percolation. Transportmetr A Transp Sci, 2017, 13: 38-66

[45]

Pan H, Li J, Chen P. Study on the ownership of motorized and non-motorized vehicles in suburban metro station areas: a structural equation approach. Urban Rail Transit, 2016, 2: 47-58

[46]

Paetz F, Steiner WJ. Utility independence versus IIA property in independent probit models. J Choice Model, 2018, 26: 41-47

[47]

Teng J, Xue H. A study on intercity travel choice behavior based on traveler heterogeneity. Railw Transp Econ, 2020, 42: 60-66.

Funding

Research on suburban railway operation management system under the background of integration of Yangtze River Delta(2021F023)

AI Summary AI Mindmap
PDF

329

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/