Automated vehicles (AVs) provide several advantages in solving issues of road traffic; including enhanced safety, reduced greenhouse gas emissions, and reduced traffic congestion. As AVs are still relatively new developments in developing countries, AV adoption faces challenges from both technological and psychological issues. Therefore, our initial research focus is on identifying the factors that influence the intention to use conditionally automated vehicles (CAVs; SAE Level 3). An extended technology acceptance model (TAM), which includes Trust, Perceived Risks, and Environmental concerns, is proposed as the predictor model in this study. The 299 participants gathered through online surveys in Thailand were examined using the Structural Equation Model (SEM) technique. In this study, Trust was shown to be the strongest predictor of Intention, followed by Perceived Ease of Use, whereas Perceived Usefulness had no impact on intention to use the SAE Level 3. The results of this study will be able to guide the forming of future policies that aim at promoting the use of AVs and helping technology developers create systems to better meet the needs of users in developing nations.

In developing countries, motorcycle riders normally attempt to stop at their desired locations during queue formation on signalized intersection approaches. Under mixed-traffic conditions, motorcycle positioning in a queue affects the operational and safety performance of the intersection. This study aimed to identify factors influencing motorcycle riders’ stopping locations at signalized urban intersections. This study applied Unmanned Aerial Vehicles (UAVs) to observe the stopping behavior of 1413 motorcycle riders on 24 approaches from 10 signalized intersections in Thailand (N = 1413). Multinomial logistic regression analysis was used to determine the relationship between the stopping locations of motorcycle riders and rider- and motorcycle-related variables and traffic- and environmental-related variables. The statistical analyses presented a Cox and Snell R² and Nagelkerke R² of 0.466 and 0.499, respectively, indicating that the model accounted for almost 50% of the variation among the five stopping locations of motorcycle riders. The results showed that, under mixed-traffic conditions in Thailand with left-hand traffic, motorcycle riders intending to turn right, the morning peak period, the presence of shadows, motorcycle riders not wearing helmets, the presence of a larger vehicle in the queue, and the density of desired stopping locations significantly influenced the motorcyclists’ choice of stopping locations on signalized intersection approaches. Practical policy-related recommendations drawn from the findings are provided to improve motorcyclists’ safety on signalized intersection approaches.

Currently, studies regarding the factors influencing the real-world electricity consumption of electric motorcycles are lacking. The objective of this study was to examine the factors influencing the real-world electricity consumption of electric motorcycles when driving along an uncongested road network. This study developed an onboard measurement device to collect on-road data, including instant speed data and electricity consumption, from the test electric motorcycle while it was driving on a real-world road. Overall, 105 participants (n = 105) drove the test motorcycle along the uncongested urban road network. Multiple linear regression analysis was applied to explore the effect of influencing variables on the electricity consumption of electric motorcycles. The analysis results revealed that the rider’s weight and average running speed positively influenced electricity consumption, whereas decelerating time negatively influenced electricity consumption. Noticeably, the rider’s weight affected electricity consumption more than other factors. The lightweighting of electric motorcycles was mainly recommended to lower electricity consumption. Subsequently, CO₂ emissions from electricity generation could be reduced.

Climate change is a major issue all around the world. The transportation industry currently accounts for most CO₂ emissions. The goal of this research is to develop a real-world eco-driving cycle for internal combustion engine motorcycles that can reduce fuel consumption and CO₂ emissions. This study developed onboard measuring equipment to measure the speed profile and fuel consumption of a motorcycle driving in real time. A total of 78 motorcycle riders rode a test motorcycle with the onboard equipment along a road network to collect real-world data. All of the collected real-world data were analyzed by cluster analysis based on fuel consumption (km/L) to divide riders into two groups, high-fuel-consumption riders and low-fuel-consumption riders. The collected real-world data of the low-fuel-consumption riders were used to develop a real-world eco-driving cycle, whereas the collected real-world data from the high-fuel-consumption riders were used to develop a real-world non-eco-driving cycle. The CO₂ emissions were calculated by the speed profiles of the developed driving cycles. The findings reveal that the real-world eco-driving cycle provided a fuel consumption rate 39.3% lower than the real-world non-eco-driving cycle. In addition, the real-world eco-driving cycle provided a CO₂ emission rate 17.4% lower than the real-world non-eco-driving cycle. The application of the developed real-world eco-driving cycle for motorcycles is proposed.