• Energy Research

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.

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.