Abstract
In middle and low-countries, traffic flow is normally dominated by motorcycles. This transport mode is recognised to significantly contribute to traffic congestion and accident. Given public transport (PT) has demonstrated to be a sustainable transport development strategy, it has been received less investment in these nations. In Da Nang, a city in the central of Viet Nam, the PT mode share has been just around 1% for the last few years. A better understanding of the users’ PT choice behaviour is important not only for policy makers but also for transport authorities in terms of developing measures aiming to encourage the residents to switch to this sustainable transport mode. This study explores factors affecting urban PT choice behaviour as well as develops a model predicting the probability of PT choice among road users. A binary logit regression model is employed to analyse the data collected from more than 800 respondents living around bus stops in Da Nang. Key findings show that individual characteristics and travel-related factors such as occupation, driving license, trip purpose, frequency, travel time and travel cost have significant impacts on the bus choice decision. Other factors including the complexity of trips (the number of temporary stops) and environmental factors (central area) is also found to affect the choice of buses. Finally, several measures for encouraging the bus choice are proposed.
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Acknowledgment
The authors highly appreciate the support of The University of Da Nang – University of Science and Technology for this study. This work was also supported by the Domestic Master/PhD Scholarship Programme of Vingroup Innovation Foundation. The authors would like to thank the Vingroup Innovation Foundation for their financial assistance.
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Anh, TT.P., Duy, NP.Q., Tho, P.C., Nakamura, F. (2021). Modeling of Urban Public Transport Choice Behaviour in Developing Countries: A Case Study of Da Nang, Vietnam. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_29
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