A Study of Guidelines for the Development of Buriram Municipality to be a Smart City for Smart Mobility

Abstract

The objectives of this study were: 1) to examine the determinants influencing the drive towards smart cities in the domain of smart mobility, and 2) to investigate the correlation between factors influencing the advancement of intelligent transportation in smart cities and the level of success in realizing the status of a smart city with intelligent transportation infrastructure. The research employed a questionnaire as the primary data collection instrument, administered to a sample group comprising 356 government personnel actively engaged in transportation-related initiatives. The analytical methods encompassed descriptive statistics including mean, standard deviation, frequency, and percentage, alongside inferential statistics through Multiple Regression Analysis. The findings can be summarized as follows:

1. The majority of respondents were female (50.9%), aged between 25 and 35 years (35.6%), married (52.8%), held a bachelor's degree (65.9%), worked as government officers (24.7%), held permanent employment (24.7%), and reported a monthly income ranging from 15,001 to 25,000 baht (41.6%). The majority were affiliated with the Department of Highways in Buriram Province (46.9%), held various positions (32.8%), and were engaged in general administrative duties (32.8%).

2. Factors driving the smart city initiative in smart mobility within Buriram Municipality indicated that leadership exhibited the most significant influence, with an average score at a high level ( = 4.05, S.D. = 0.76), followed by public participation, also rated at a high level ( = 3.9, S.D. = 0.74). In contrast, research and development in technology and innovation demonstrated the lowest impact, but still registered at a high level ( = 3.72, S.D. = 0.79).

3. The outcomes of the multiple regression analysis revealed that two factors, namely infrastructure (INF) and political will (POL), exhibited significant effects at a significance level of 0.05 (p-value < 0.05) on the success of smart city intelligent transportation, particularly in terms of facilitating access to public transport networks (Accessibility: ACC), accounting for 14.70% of the variation. Other factors not addressed in this study were found to influence the remaining variance.