Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach - Journal of Transport Geography 116 (2024) 103828

Aug 5, 2026

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This study investigates how the built environment (BE) shapes commuter mode choice within a dense Global South megacity setting. Using 10,150 home-based commuting trips from Dhaka, Bangladesh, three machine learning classifiers were tested to predict chosen travel modes. Random Forest delivered the strongest predictive performance. The model further quantifies the relative importance of BE factors, revealing nonlinear relationships and interaction effects that affect private car use. Results support policy directions combining transit-oriented development, travel demand management, and integrated land-use transportation planning to enable low-carbon urban mobility.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Sustainable development and private car growth

Built environment as a determinant of travel mode choices

Data and methods

Study context: Dhaka, Bangladesh

Machine learning classifiers for prediction

Results and analysis

Best-performing model and predictive performance

Relative importance of built environment factors

Nonlinear thresholds and interaction effects

Implications for planning and policy

Transit-oriented development and integrated land-use transport

Journal of Transport Geography 116 (2024) 103828 Contents lists available at ScienceDirect Journal of Transport Geography journal [homepage:](homepage: www.elsevier.com/locate/jtrangeo)[ www.elsevier.com/locate/jtrangeo](homepage: www.elsevier.com/locate/jtrangeo) | Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach F.R. Ashika, f, A.I.Z. Sreezon b, M.H. Rahman c, N.M. Zafrid, S.M. Labibe, *
a Department of Geography, McGill University, 805 Sherbrooke St W, Montreal, Quebec H3A 0B9, Canada b School of Computer Science, Queensland University of Technology (QUT), Australia
c Community and Regional Planning, The University of Texas at Austin, 310 Inner Campus Drive B7500, Austin, TX 78705, United States d Department of Urban and Regional Planning, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh e Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, 3584, CB, Utrecht, the Netherlands f Accident Research Institute, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Travel behavior Urban form Nonlinear effect Interaction effect Machine learning
Sustainable transportation systems | | In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we identified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predicting commute mode choice, identified nonlinear relationships between the BE factors and mode choice, and examined the interaction effects of these factors on mode selection. Our results reveal that, compared to socio-demographic factors, the BE substantially influence commuter travel behavior. The BE characteristics have a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant return of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting. These findings hold substantial implications for urban environmental policy, emphasizing the need for transit-oriented development, travel demand management, and integrated land-use transportation planning to foster low-carbon transportation systems in cities like Dhaka. | 1. Introduction Despite concerted efforts to advance sustainable development, the prevalence of private car usage in urban areas continues to grow, exacerbating greenhouse gas emissions and increasing energy consumption. Consequently, this trend undermines the pursuit of sustainable mobility objectives. Developed nations engage in urban planning strategies that involve modifying the built environment of cities (e.g., compact city development, smart growth, and Transit-Oriented Development) in order to reduce private car use, thereby mitigating urban challenges and ensuring sustainable mobility (Ewing and Cervero, 2010). The built environment (BE) is widely acknowledged as a significant contextual determinant in promoting the adoption of public transportation, walking, and cycling over private car, hence offering a potential remedy for reducing emission (Ashik et al., 2023; Wu et al., 2019) and enhancing physical and mental well-being (Handy et al., 2002; Panter et al., 2019). In the field of travel behavior research, it is commonly hypothesiz

FAQs

What key patterns did the study find about built environment effects?

Built environment factors showed nonlinear relationships with mode choice, including threshold-like limits that can reduce private car use, and interaction effects suggest optimal combinations of BE interventions.

Aug 5, 2026

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