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Why Don’t More Commuters Consider Buses for Their Work Trip?—A Geographically Weighted Segmented Logistic Regression Modelling Approach

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Abstract

Empirical data from several cities worldwide show that low consideration rates of bus transit constitute an important reason for declining transit mode share. This trend raises a critical question: why do not more commuters consider buses for travel? The aim of this paper is to investigate the key deterrents to bus transit consideration among commuters. Consideration refers to the decision of an individual to include a given mode in his/her choice set. Along this line, this study focuses on two important directions. The first is to gain an understanding of how the key factors and their influence on consideration propensity differ across market segments based on personal vehicle availability and accessibility. The second is to quantify the spatial heterogeneity in the influence of those key factors across geographical locations. A new geographically weighted segmented logistic regression (GWSLR) model is proposed to address these research issues. The model is developed using household survey data from a sample of work-commuters from Chennai city, India. The findings reveal that neglecting either segmentation or spatial heterogeneity cannot only result in inaccurate model predictions and inferences, but also lead to sub-optimal policy interventions. Results show that factors influencing consideration differ significantly across segments based on captivity and accessibility. Choice users within walking distance to bus stops can benefit from direct bus service, whereas those beyond walking distance prioritise first-mile connectivity. Results also highlight significant variations in the influence of different factors across locations. Improved walkability in central business districts (CBD) could increase consideration, while non-CBD areas should focus on first-mile connectivity. Finally, this study illustrates that customised policy interventions for specific segments and locations can be more effective in enhancing bus consideration than segment-agnostic or geographically uniform policies.

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References

  • Albuquerque, P. H. M., Medina, F. A. S., & Da Silva, A. R. (2017). Geographically Weighted Logistic Regression Applied to Credit Scoring Models. Revista Contabilidade e Financas, 28(73), 93–112.

    Article  Google Scholar 

  • Anwar, A. H. M. M., & Yang, J. (2017). Examining the Effects of Transport Policy on Modal Shift from Private Car to Public Bus. Procedia Engineering, 180, 1413–1422.

    Article  Google Scholar 

  • Beimborn, E. A., Greenwald, M. J., & Jin, X. (2003). Accessibility, connectivity, and captivity: Impacts on transit choice. Transportation Research Record, 1835, 1–9.

    Article  Google Scholar 

  • Cascetta, E., & Papola, A. (2001). Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand. Transportation Research Part C, 9(4), 249–263.

    Article  Google Scholar 

  • Castro, M., Martínez, F., & Munizaga, M. A. (2012). Estimation of a constrained multinomial logit model. Transportation, 40(3), 563–581.

    Article  Google Scholar 

  • Cheng, L., Chen, X., Lam, W. H., Yang, S., & Lei, D. (2017). Public transit market research of low-income commuters using attitude-based market segmentation approach: Case study of Fushun, China. Transportation research record, 2671(1), 10–19.

  • Chennai Metropolitan Development Authority. (2019). Comprehensive Mobility Plan for CMA Final Report. Retrieved from https://www.cmdachennai.gov.in/pdfs/ComprehensiveMobilityPlan-CMA.pdf (Accessed: 15 November 2023).

  • Devaraj, A., Ambi Ramakrishnan, G., Nair, G. S., Srinivasan, K. K., Bhat, C. R., Pinjari, A. R., Ramadurai, G., & Pendyala, R. M. (2020). Joint model of application-based ride hailing adoption, intensity of use, and intermediate public transport consideration among workers in Chennai City. Transportation Research Record, 2674(4), 152–164.

    Article  Google Scholar 

  • Fotheringham, A. S., & Brunsdon, C. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Wiley.

    Google Scholar 

  • Hu, Y., Sobhani, A., & Ettema, D. (2022). Exploring commute mode choice in dual-earner households in a small Chinese city. Transportation Research Part D: Transport and Environment, 102, 103148.

  • Inverness & Nairn Transport Forum (2006). Rural community transport survey analysis of survey findings. Lèirsinn Research Centre, Sabhal Mòr Ostaig.

  • Kunhikrishnan, P., & Srinivasan, K. K. (2019). Choice set variability and contextual heterogeneity in work trip mode choice in Chennai city. Transportation Letters, 11(4), 174–189.

  • Manski, C. F. (1977). The structure of random utility models. Theory and Decision, 8(3), 229–254.

    Article  Google Scholar 

  • Mayfield, H. J., Lowry, J. H., Watson, C. H., Kama, M., Nilles, E. J., & Lau, C. L. (2018). Use of geographically weighted logistic regression to quantify spatial variation in the environmental and socio-demographic drivers of leptospirosis in Fiji: A modelling study. The Lancet Planetary Health, 2(5), e223–e232.

    Article  Google Scholar 

  • Nkeki, F. N., & Asikhia, M. O. (2019). Geographically weighted logistic regression approach to explore the spatial variability in travel behaviour and built environment interactions: Accounting simultaneously for demographic and socio-economic characteristics. Applied Geography, 108, 47–63.

    Article  Google Scholar 

  • Puan, O. C., Hassan, Y. A. H., Mashros, N., Idham, M. K., Hassan, N. A., Warid, M. N. M., Hainin, M. R. (2019). Transportation mode choice binary logit model: A case study for Johor Bahru city. IOP Conference Series: Materials Science and Engineering, 527(1). https://doi.org/10.1088/1757-899X/527/1/012066

  • Ramakrishnan, G. A., Srinivasan, K. K., Pynda, S. P. (2020). Joint Models for Consideration of Public Transit and Mode Choice for Work Commute. Transportation in Developing Economies, 6(1). https://doi.org/10.1007/s40890-020-00101-4

  • Registrar General and Census Commissioner of India. (2011). District Census Handbook, Chennai. Retrieved from https://censusindia.gov.in/census.website/data/handbooks

  • Rybarczyk, G., & Shaker, R. R. (2021). Predicting bicycle-on-board transit choice in a university environment. Sustainability (switzerland), 13(2), 1–16.

    Google Scholar 

  • Sana, B., Outwater, M., Ferdous, N., Woodford, B., & Lobb, J. (2011). Characteristics of premium transit services that affect choice of mode. The National Academies Press.

    Google Scholar 

  • Selvakumar, M., Abishek Reddy, M., Sathish, V., & Venkatesh, R. (2018). Potential influence of metro on bus: a case study. Journal of The Institution of Engineers (India): Series A, 99(2), 379–384. https://doi.org/10.1007/s40030-018-0290-y

    Article  Google Scholar 

  • Shortridge, A. (2007). Practical limits of Moran’s autocorrelation index for raster class maps. Computers, Environment and Urban Systems, 31(3), 362–371.

    Article  Google Scholar 

  • Sinha, S., Swamy, H. S., & Modi, K. (2020). User perceptions of public transport service quality. Transportation Research Procedia, 48, 3310–3323.

    Article  Google Scholar 

  • Srinivasan, K. K., Pradhan, G. N., & Maheswara Naidu, G. (2007). Commute mode choice in a developing country: Role of subjective factors and variations in responsiveness across captive, semi-captive, and choice segments. Transportation Research Record, 2038, 53–61.

    Article  Google Scholar 

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Acknowledgements

We would like to express our gratitude for the support received from the IMPRINT (IMP/2018/001850, DST) project, the Government of India for sponsoring the MHRD scholarship, and CMRL in facilitating this study.

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  1. Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India

    Payel Roy, Karthik K. Srinivasan & Ganesh Ambi Ramakrishnan

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  1. Payel Roy
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  2. Karthik K. Srinivasan
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  3. Ganesh Ambi Ramakrishnan
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Correspondence to Payel Roy.

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Roy, P., Srinivasan, K.K. & Ramakrishnan, G.A. Why Don’t More Commuters Consider Buses for Their Work Trip?—A Geographically Weighted Segmented Logistic Regression Modelling Approach. Appl. Spatial Analysis (2024). https://doi.org/10.1007/s12061-024-09576-9

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