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

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 03 Jan 2020Publisher:MDPI AGJournal:Sustainability, volume 12, page 395 (eissn: 2071-1050,

Authors: Sajjakaj Jomnonkwao; Savalee Uttra; Vatanavongs Ratanavaraha;

doi: 10.3390/su12010395

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

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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.

Related Organizations

Suranaree University of Technology
Thailand
Kalasin University
Thailand
Suranaree University of Technology
Thailand
Suranaree University of Technology
Thailand
Suranaree University of Technology
Thailand

Keywords

Environmental effects of industries and plants, time-series, TJ807-830, accident forecasting, TD194-195, Renewable energy sources, multiple linear regression model, Environmental sciences, path analysis, GE1-350

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	35
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%