Forecasting future public transport mode choice behaviour of commuters in Bahrain using Logit and classification tree models: a comparative study

• Autorzy: Jazi Marwa , Gazder Uneb , Arsalan Mudassar , Mehdi Mohammed Raza

Identyfikatory

DOI

10.20858/sjsutst.2024.123.2

• Języki publikacji: EN

Abstrakty

EN

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country.

Słowa kluczowe

EN

public transport; Bahrain; mode choice; Logit model; decision tree

PL

transport publiczny; Bahrain; wybór środka transportu; model Logit; drzewo decyzyjne

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2024
• Tom: z. 123
• Strony: 23--55
• Opis fizyczny: Bibliogr. 40 poz.

Twórcy

autor

Jazi Marwa

• epartment of Civil Engineering, University of Bahrain, Sakhir, Bahrain

• https://orcid.org/0009-0000-2671-8086

autor

Gazder Uneb

• epartment of Civil Engineering, University of Bahrain, Sakhir, Bahrain

• https://orcid.org/0000-0002-9445-9570

autor

Arsalan Mudassar

• School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia

• https://orcid.org/0000-0001-9622-5930

autor

Mehdi Mohammed Raza

• Department of Civil Engineering, DHA Suffa University, Karachi, Pakistan

• https://orcid.org/0009-0009-3783-3456

Bibliografia

• 1. Davis K. 2015. “The urbanization of the human population”. In: The City Reader. Chapter: 43-53. Routledge.
• 2. Opoku O., O. Yeboah, E. Gyamfi, G. Afful. 2022. „Rising car ownership and traffic congestion in the university of cape coast campus”. International Journal of Research in Science & Engineering 24: 10-21.
• 3. Ding C., Y. Wang, T. Tang, S. Mishra, C. Liu. 2018. „Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice”. Transportation Research Part D: Transport and Environment 60: 28-40.
• 4. Hamed M.M., B.M. Al-Eideh. 2020. „An exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means”. Analytic Methods in Accident Research 25: 100116.
• 5. Kalmykova Y., L. Rosado, J. Patrício. 2016. „Resource consumption drivers and pathways to reduction: economy, policy and lifestyle impact on material flows at the national and urban scale”. Journal of Cleaner Production 132: 70-80.
• 6. Hamurcu M., T. Eren. 2020. „Strategic planning based on sustainability for urban transportation: An application to decision-making”. Sustainability 12(9): 3589.
• 7. Ojo T.K. 2019. „Quality of public transport service: An integrative review and research agenda”. Transportation Letters 11(2): 104-116.
• 8. Alotaibi O., D. Potoglou. 2017. „Perspectives of travel strategies in light of the new metro and bus networks in Riyadh City, Saudi Arabia”. Transportation Planning and Technology 40(1): 4-27.
• 9. Mushtaha E.S., R. Nahlé, N. Tahmaz, M. AlKadry. 2019. „Designing guidelines for metro stations in developing countries: The case of Dubai”. International Review for Spatial Planning and Sustainable Development 7(4): 83-96.
• 10. AlKhereibi A., M. AlSuwaidi, R. Al-Mohammed, S. Pokharel, M. Ayari. 2021. “An integrated urban-transport smart growth model around metro stations: A case of Qatar”. Transportation Research Interdisciplinary Perspectives 10: 100392.
• 11. Al Suleiman S., A. Cortez, A. Monzón, A. Lara. 2023. “How to improve public transport usage in a medium-sized city: key factors for a successful bus system”. European Transport Research Review 15(1): 47.
• 12. Alaali F., H. Naser. 2020. “Economic development and environmental sustainability: evidence from Bahrain”. Energy, Ecology and Environment 5: 211-219.
• 13. Sowjanya D., D. Tahlyan, C.R. Sekhar. 2014. “Travel demand modelling for a metropolitan city”. In: International Conference on Recent Trends and Challenges in Civil Engineering: 19-40.
• 14. Waghmare A., G. Yadav, K. Tiwari. 2022. “Four step travel demand modelling for urban transportation planning”. Sci. Eng. Technol. 5: 1254.
• 15. Xintong M. 2021. “A critical overview of four-stage model under the background of the rise of ride-sharing”. In: E3S Web of Conferences 253: 03020. EDP Sciences.
• 16. Nesamani K.S., J.D. Saphores, M.G. McNally, R. Jayakrishnan. 2017. “Estimating impacts of emission specific characteristics on vehicle operation for quantifying air pollutant emissions and energy use”. Journal of Traffic and Transportation Engineering 4(3): 215-229.
• 17. Modi K.B., L.B. Zala, F.S. Umrigar, T.A. Desai. 2011, May. “Transportation planning models: a review”. In: National Conference on Recent Trends in Engineering and Technology. Gujarat India.
• 18. Hillel I., E. Gazit, A. Nieuwboer, L. Avanzino, L. Rochester, A. Cereatti, J.M. Hausdorff. 2019. “Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring”. European Review of Aging and Physical Activity 16(1): 1-12.
• 19. Pineda-Jaramillo J. D. 2019. “A review of Machine Learning (ML) algorithms used for modelling travel mode choice”. Dyna 86(211): 32-41.
• 20. Panter J., E. Heinen, R. Mackett, D. Ogilvie. 2016. “Impact of new transport infrastructure on walking, cycling, and physical activity”. American Journal of Preventive Medicine 50(2): e45-e53.
• 21. Pham B.T., T.V. Phong, H.D. Nguyen, C. Qi, N. Al-Ansari, A. Amini, A., D. Tien Bui. 2020. “A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes, and logistic model tree for flash flood susceptibility mapping”. Water 12(1): 239.
• 22. Puan O.C., Y.A.H. Hassan, N. Mashros, M.K. Idham, N.A. Hassan, M.N.M. Warid, M.R. Hainin. 2019. “Transportation mode choice binary logit model: A case study for Johor Bahru city”. In: IOP Conference Series: Materials Science and Engineering 527(1): 012066. IOP Publishing.
• 23. Javaid A., F. Creutzig, S. Bamberg. 2020. “Determinants of low-carbon transport mode adoption: systematic review of reviews”. Environmental Research Letters 15(10): 103002.
• 24. Abdullah M., N. Ali, M.A. Javid, C. Dias, T. Campisi. 2021. “Public transport versus solo travel mode choices during the COVID-19 pandemic: Self-reported evidence from a developing country”. Transportation Engineering 5: 100078.
• 25. Oral O.L., V.A.H.A.P. Tecim. 2013. “Using decision trees for estimating mode choice of trips in Buca-Izmir”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 40: 139-145.
• 26. Lindner A., C.S. Pitombo, A.L. Cunha. 2017. “Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data”. Travel Behaviour and Society 6: 100-109.
• 27. Sekhar C.R., E. Madhu. 2016. “Mode choice analysis using random forest decision trees”. Transportation Research Procedia 17: 644-652.
• 28. Hagenauer J., M. Helbich. 2017. “A comparative study of machine learning classifiers for modelling travel mode choice”. Expert Systems with Applications 78: 273-282.
• 29. Zhao X., X. Yan, A. Yu, P. Van Hentenryck. 2020. “Prediction and behavioural analysis of travel mode choice: A comparison of machine learning and logit models”. Travel Behaviour and Society 20: 22-35.
• 30. Jedidi A., A. Mahdi, A.J. Ali. 2021. “Traffic congestion in the kingdom of Bahrain: social mobile application solution”. In: 3rd Smart Cities Symposium (SCS 2020) 2021: 337-343. DOI: 10.1049/icp.2021.0883.
• 31. Kumar M., A.S. Alsharqi, M.A. Al-Romaihi. 2021, December. “Consumers' choice of vehicle parameters and their impact on environment in relation to gasoline price increase in Bahrain”. In: 2021 International Conference on Decision Aid Sciences and Application (DASA): 792-796. IEEE.
• 32. Feneri A.M., S. Rasouli, H.J. Timmermans. 2022. “Modelling the effect of Mobility-as-a-Service on mode choice decisions”. Transportation Letters 14(4): 324-331.
• 33. Steinke A., B. Kopp. 2020. “RELEX: An Excel-based software tool for sampling split-half reliability coefficients”. Methods in Psychology 2: 100023.
• 34. de Vet H.C., L.B. Mokkink, D.G. Mosmuller, C.B. Terwee. 2017. “Spearman-Brown prophecy formula and Cronbach's alpha: different faces of reliability and opportunities for new applications”. Journal of Clinical Epidemiology 85: 45-49.
• 35. Fawcett T. 2008. “PRIE: a system for generating rulelists to maximize ROC performance”. Data Mining and Knowledge Discovery 17: 207-224.
• 36. Xiao Z., Y. Wang, K. Fu, F. Wu. 2017. “Identifying different transportation modes from trajectory data using tree-based ensemble classifiers”. ISPRS International Journal of Geo-Information 6(2): 57.
• 37. Chee W.L., J.L. Fernandez. 2013. “Factors that influence the choice of mode of transport in Penang: A preliminary analysis”. Procedia-Social and Behavioural Sciences 91: 120-127.
• 38. Anwar A.M., A.T. Oakil, A. Muhsen, A. Arora. 2023. “What would it take for the people of Riyadh city to shift from their cars to the proposed metro?”. Case Studies on Transport Policy 12: 101008.
• 39. Rahman M.M., P. Najaf, M.G. Fields, J.C. Thill. 2022. “Traffic congestion and its urban scale factors: Empirical evidence from American urban areas”. International Journal of Sustainable Transportation 16(5): 406-421.
• 40. Samal S.R., P.G. Kumar, J.C. Santhosh, M. Santhakumar. 2020, December. “Analysis of traffic congestion impacts of urban road network under Indian condition”. In: IOP Conference Series: Materials Science and Engineering 1006(1): 012002. IOP Publishing.