Abstract
In public transport, crowding is one of the variables that is likely to influence the decisions of the choice makers. Crowding has become a subject of concern in metropolitan areas, triggering passenger travel behavior, such as shifting from public to private modes of transport, changing routes or departure times, etc. Hence, there is a need to understand the effect of crowding in public transport and its influence on the behavior of travelers. Therefore, this review investigates essential factors (e.g., crowding representation, crowding measurement, modeling framework, etc.) after reviewing the 40 screened studies on the valuation of crowding in public transport. The paper’s findings show that the passenger perception towards crowding is different for varying levels of crowding, modes of transport, study areas, data types, different modeling frameworks, and the underlying distribution of the attribute parameters. A meta-analysis is performed to show the influence of explanatory variables affecting the value of the time multiplier. A net-salary-based city classification is used to make the results transferable. Lastly, this work provides a direction for the selection of the crowding representation, measure, and valuation for future studies. Further, several research gaps are identified for the model formulation, valuation, crowding at different locations, non-linearity, etc.
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Notes
The past studies have given the multiplier values for different crowding levels/densities. The range plots are drawn by retrieving the data from these studies.
The PPP rates are extracted from https://data.oecd.org/conversion/purchasing-power-parities-ppp.htm on Nov. 23, 2022.
For this, average annual US Consumer Price Index (CPI) data is used. This information is extracted from https://www.calculator.net/inflation-calculator.html on Nov. 23, 2022.
References
Aghabayk K, Esmailpour J, Shiwakoti N (2021) Effects of COVID-19 on rail passengers’ crowding perceptions. Transport Res Part A Policy Pract 154:186–202. https://doi.org/10.1016/j.tra.2021.10.011
Bansal P, Hurtubia R, Tirachini A, Daziano RA (2019) Flexible estimates of heterogeneity in crowding valuation in the New York City subway. J Choice Model 31:124–140. https://doi.org/10.1016/j.jocm.2019.04.004
Basnak P, Giesen R, Muñoz JC (2022) Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile. Transport Res Part A Policy Pract 159:140–156. https://doi.org/10.1016/j.tra.2022.03.011
Basu D, Hunt JD (2012) Valuing of attributes influencing the attractiveness of suburban train service in Mumbai city: a stated preference approach. Transport Res Part A Policy Pract 46(9):1465–1476. https://doi.org/10.1016/j.tra.2012.05.010
Batarce M, Muñoz JC, de Dios Ortúzar J et al. (2015) Use of mixed stated and revealed preference data for crowding valuation on public transport in Santiago, Chile. Transport Res Rec J Transport Res Board 2535(1):73–78. https://doi.org/10.3141/2535-08
Batarce M, Muñoz JC, de Dios Ortúzar J (2016) Valuing crowding in public transport: implications for cost-benefit analysis. Transport Res Part A Policy Pract 91:358–378. https://doi.org/10.1016/j.tra.2016.06.025
Batley R, Bates J, Bliemer M et al. (2017) New appraisal values of travel time saving and reliability in great Britain. Transportation 46(3):583–621. https://doi.org/10.1007/s11116-017-9798-7
Björklund G, Swärdh JE (2017) Estimating policy values for in-vehicle comfort and crowding reduction in local public transport. Transport Res Part A Policy Pract 106:453–472. https://doi.org/10.1016/j.tra.2017.10.016
Cats O, West J, Eliasson J (2016) A dynamic stochastic model for evaluating congestion and crowding effects in transit systems. Transport Res Part B Methodol 89:43–57. https://doi.org/10.1016/j.trb.2016.04.001
Çelebi D, İmre Ş (2020) Measuring crowding-related comfort in public transport. Transport Plan Technol 43(7):735–750. https://doi.org/10.1080/03081060.2020.1805546
Cho SH, Park HC (2021) Exploring the behaviour change of crowding impedance on public transit due to COVID-19 pandemic: before and after comparison. Transport Lett 13(5–6):367–374. https://doi.org/10.1080/19427867.2021.1897937
Gao K, Sun L, Tu H, Li H (2018) Heterogeneity in valuation of travel time reliability and in-vehicle crowding for mode choices in multimodal networks. J Transport Eng Part A Syst 144(10):04018,061. https://doi.org/10.1061/jtepbs.0000187
Harzing AW (2020) Publish or perish. https://harzing.com/resources/publish-or-perish. Accessed 2020
Haywood L, Koning M (2015) The distribution of crowding costs in public transport: new evidence from Paris. Transport Res Part A Policy Pract 77:182–201. https://doi.org/10.1016/j.tra.2015.04.005
Hensher DA, Rose JM, Collins AT (2011) Identifying commuter preferences for existing modes and a proposed Metro in Sydney, Australia with special reference to crowding. Public Transport 3(2):109–147. https://doi.org/10.1007/s12469-010-0035-4
Hensher DA, Rose JM, Leong W et al. (2013) Choosing public transport—incorporating richer behavioural elements in modal choice models. Transport Rev 33(1):92–106. https://doi.org/10.1080/01441647.2012.760671
Hörcher D, Graham DJ, Anderson RJ (2017) Crowding cost estimation with large scale smart card and vehicle location data. Transport Res Part B Methodol 95:105–125. https://doi.org/10.1016/j.trb.2016.10.015
Kim KM, Hong SP, Ko SJ, Kim D (2015) Does crowding affect the path choice of metro passengers? Transport Res Part A Policy Pract 77:292–304. https://doi.org/10.1016/j.tra.2015.04.023
Kroes E, Kouwenhoven M, Debrincat L, Pauget N (2014) Value of crowding on public transport in île-de-France, France. Transport Res Rec J Transport Res Board 2417(1):37–45. https://doi.org/10.3141/2417-05
Li Z, Hensher DA (2011) Crowding and public transport: a review of willingness to pay evidence and its relevance in project appraisal. Transport Policy 18(6):880–887. https://doi.org/10.1016/j.tranpol.2011.06.003
Li H, Gao K, Tu H et al. (2016) Perception of mode-specific travel time reliability and crowding in multimodal trips. Transport Res Rec J Transport Res Board 2566(1):22–30. https://doi.org/10.3141/2566-03
Li H, Gao K, Tu H (2017) Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: implications for demand estimation. Transport Res Part A Policy Pract 103:250–263. https://doi.org/10.1016/j.tra.2017.06.009
Liu J, Wen H (2016) Public transport crowding valuation: evidence from college students in Guangzhou. J Public Transport 19(3):78–97. https://doi.org/10.5038/2375-0901.19.3.5
Lu H, Fowkes T, Wardman M (2008) Amending the incentive for strategic bias in stated preference studies. Transport Res Rec J Transport Res Board 2049(1):128–135. https://doi.org/10.3141/2049-15
Márquez L, A JVA, Poveda JC, (2019) In-vehicle crowding: integrating tangible attributes, attitudes, and perceptions in a choice context between BRT and metro. Transport Res Part A Policy Pract 130:452–465. https://doi.org/10.1016/j.tra.2019.09.061
Moher D, Liberati A, Tetzlaff J et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097
Prud’homme R, Koning M, Lenormand L et al. (2012) Public transport congestion costs: the case of the Paris subway. Transport Policy 21:101–109. https://doi.org/10.1016/j.tranpol.2011.11.002
Qin F (2014) Investigating the in-vehicle crowding cost functions for public transit modes. Math Probl Eng 2014:1–13. https://doi.org/10.1155/2014/502708
Sadeghi S, Daziano R, Yoon SY, Anderson AK (2022) Crowding and perceived travel time in public transit: virtual reality compared with stated choice surveys. Transport Res Rec J Transport Res Board 2677(5):296–307. https://doi.org/10.1177/03611981221130346
Sahu PK, Sharma G, Guharoy A (2018) Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai. Public Transport 10(3):379–398. https://doi.org/10.1007/s12469-018-0190-6
Shao M, Xie C, Li T, Sun L (2021) Influence of in-vehicle crowding on passenger travel time value: insights from bus transit in Shanghai. Int J Transport Sci Technol, China. https://doi.org/10.1016/j.ijtst.2021.09.001
Shelat S, Cats O, van Cranenburgh S (2022) Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands. Transport Res Part A Policy Pract 159:357–371. https://doi.org/10.1016/j.tra.2022.03.027
Shelat S, van de Wiel T, Molin E et al. (2022) Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks. PLOS ONE 17(3):e0264805. https://doi.org/10.1371/journal.pone.0264805
Shin H, Kim DK, Kho SY, Cho SH (2020) Valuation of metro crowding considering heterogeneity of route choice behaviors. Transport Res Rec J Transport Res Board 2675(2):162–173. https://doi.org/10.1177/0361198120948862
Tang Y, Jiang Y, Yang H, Nielsen OA (2020) Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems. Transport Res Part B Methodol 138:247–267. https://doi.org/10.1016/j.trb.2020.05.006
Tirachini A, Hensher DA, Rose JM (2013) Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transport Res Part A Policy Pract 53:36–52. https://doi.org/10.1016/j.tra.2013.06.005
Tirachini A, Hensher DA, Rose JM (2014) Multimodal pricing and optimal design of urban public transport: the interplay between traffic congestion and bus crowding. Transport Res Part B Methodol 61:33–54. https://doi.org/10.1016/j.trb.2014.01.003
Tirachini A, Sun L, Erath A, Chakirov A (2016) Valuation of sitting and standing in metro trains using revealed preferences. Transport Policy 47:94–104. https://doi.org/10.1016/j.tranpol.2015.12.004
Tirachini A, Hurtubia R, Dekker T, Daziano RA (2017) Estimation of crowding discomfort in public transport: results from Santiago de Chile. Transport Res Part A Policy Pract 103:311–326. https://doi.org/10.1016/j.tra.2017.06.008
Wardman M, Murphy P (2015) Passengers’ valuations of train seating layout, position and occupancy. Transport Res Part A Policy Pract 74:222–238. https://doi.org/10.1016/j.tra.2015.01.007
Wardman M, Whelan G (2011) Twenty years of rail crowding valuation studies: evidence and lessons from British experience. Transport Rev 31(3):379–398. https://doi.org/10.1080/01441647.2010.519127
Yap M, Cats O (2021) Taking the path less travelled: Valuation of denied boarding in crowded public transport systems. Transport Res Part A Policy Pract 147:1–13. https://doi.org/10.1016/j.tra.2021.02.007
Yap M, Cats O, van Arem B (2018) Crowding valuation in urban tram and bus transportation based on smart card data. Transportmetrica A Transport Sci 16(1):23–42. https://doi.org/10.1080/23249935.2018.1537319
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List of screened studies
List of screened studies
Models MNL—multi-nomial logit, ML—mixed logit, LC—latent class, EC—error component
Data SP—stated preference, RP—revealed preference
Passive data AVL—automated vehicle location, APC—automatic passenger count
Crowding representation (CR) L—Linguistic, 2DD—2D diagram, P—pictorial
Crowding description LF—load factor, SD—standing density, SO—sitting occupancy
Crowding valuation TM—time multiplier, MVpH—monetary value per hour, MVpH—monetary value per trip, WTM—wait time multiplier
NA—not available
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Fedujwar, R., Agarwal, A. A systematic review on crowding valuation in public transport. Public Transp (2024). https://doi.org/10.1007/s12469-024-00363-w
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DOI: https://doi.org/10.1007/s12469-024-00363-w