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Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models

Author

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  • Sajjakaj Jomnonkwao

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand)

  • Savalee Uttra

    (Department of Logistics Engineering and Transportation Technology, Faculty of Engineering and Industrial Technology, Kalasin University 62/1 Kaset Sombun Road, Kalasin Subdistrict, Mueang District, Kalasin 46000, Thailand)

  • Vatanavongs Ratanavaraha

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-District, Muang District, Nakhon Ratchasima 30000, Thailand)

Abstract

In 2018, 19,931 people were killed in road accidents in Thailand. Thus, reduction in the number of accidents is urgently required. To provide a master plan for reducing the number of accidents, future forecast data are required. Thus, we aimed to identify the appropriate forecasting method. We considered four methods in this study: Time-series analysis, curve estimation, regression analysis, and path analysis. The data used in the analysis included death rate per 100,000 population, gross domestic product (GDP), the number of registered vehicles (motorcycles, cars, and trucks), and energy consumption of the transportation sector. The results show that the best three models, based on the mean absolute percentage error (MAPE), are the multiple linear regression model 3, time-series with exponential smoothing, and path analysis, with MAPE values of 6.4%, 8.1%, and 8.4%, respectively.

Suggested Citation

  • Sajjakaj Jomnonkwao & Savalee Uttra & Vatanavongs Ratanavaraha, 2020. "Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:1:p:395-:d:305012
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    References listed on IDEAS

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    1. Ratanavaraha, Vatanavongs & Jomnonkwao, Sajjakaj, 2015. "Trends in Thailand CO2 emissions in the transportation sector and Policy Mitigation," Transport Policy, Elsevier, vol. 41(C), pages 136-146.
    2. Sajjakaj Jomnonkwao & Onanong Sangphong & Buratin Khampirat & Siradol Siridhara & Vatanavongs Ratanavaraha, 2016. "Public transport promotion policy on campus: evidence from Suranaree University in Thailand," Public Transport, Springer, vol. 8(2), pages 185-203, September.
    3. Dadashova, Bahar & Ramírez Arenas, Blanca & McWilliams Mira, José & Izquierdo Aparicio, Francisco, 2014. "Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain," Transport Policy, Elsevier, vol. 32(C), pages 203-217.
    4. Garcia-Ferrer, A. & de Juan, A. & Poncela, P., 2006. "Forecasting traffic accidents using disaggregated data," International Journal of Forecasting, Elsevier, vol. 22(2), pages 203-222.
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    2. Kazi Enamul Hoque & Husaina Banu Bt Kenayathulla & Malar Vili D/O Subramaniam & Reazul Islam, 2020. "Relationships Between Supervision and Teachers’ Performance and Attitude in Secondary Schools in Malaysia," SAGE Open, , vol. 10(2), pages 21582440209, May.
    3. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    4. Lungu Adrian, 2024. "Analysis of Economic Convergence in the European Union," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 405-423.
    5. Thanapong Champahom & Sajjakaj Jomnonkwao & Chinnakrit Banyong & Watanya Nambulee & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2021. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach," Sustainability, MDPI, vol. 13(18), pages 1-19, September.

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