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The Korean government has announced plans to supply electrified buses to achieve decarbonization in the transportation sector and to create next-generation growth engines. Although a multitude of technical and political studies have been conducted to support the successful introduction of electrified buses, studies on the attitudes and perceptions of passengers toward electrified buses remain insufficient. To evaluate the perceptions and preferences of potential passengers toward the specific attributes of electrified buses, this study performed an online survey (N = 586) that includes people who had experienced travel on public buses. Values of the relative importance of eight different attributes—safety, ride comfort, environmental friendliness, exterior design, cleanliness, crowding, seat comfort, and convenience getting on/off—were evaluated using the best-worst scaling method. The results showed that safety (share of preference: 41.3%) was the most important attribute when using electrified buses. This was followed by eco-friendliness (14.3%) and ride comfort (13.6%). On the other hand, the least important attribute was exterior design (1.8%). Gender differences were also observed in the valuation of certain attributes among the passenger preferences toward electrified buses. The results of this study contribute to the development of strategies for the wide-spread adoption of electrified buses and provide a stepping-stone to a more sustainable public transportation system.

This research explored the dual influence of fuel efficiency and roadway capacity on vehicle distance traveled. The empirical study was conducted by applying a generalized least square (GLS) analysis to the U.S. state-level panel data of fuel efficiency, roadway lane-miles, and vehicle miles traveled (VMT) for over three decades . The analysis confirmed the co-existence of rebound effect and induced demand on driving distances over the decades-improved fuel efficiency and expanded roadway capacity caused additional vehicle distance traveled, partially offsetting the benefits of the measures taken. Furthermore, the results showed that the magnitude of each effect would be unjustifiably overestimated if this dual influence was not taken into consideration.

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.