The Best Paper AwardAwards and Grants
No.43 In 2021
Title：Attitude toward physical activity as a determinant of bus use intention: A case study in Asuke, Japan
Recipient：Yen Tran, Toshiyuki Yamamoto, Hitomi Sato, Tomio Miwa, Takayuki Morikawa
Reason for award：
The promotion of bus usage is positioned as one of the important public policies not only for the revitalization of regional mobility and regional public transportation projects, but also for the revitalization of regional economy and society, global warming countermeasures, welfare measures for the elderly and health promotion measures for local residents. This research paper has been evaluated as excellent in providing important new knowledge on the promotion of bus use and has been recommended as a candidate for the Best Paper Award.
The most typical measure to promote bus use has been to enhance the level of the objective bus service, and, in recent years, in addition to such basic measures, the need for various intangible measures has been recognized, and the use of marketing as a psychological strategy based on various promotions and information provisions has been put into practice. Such efforts have been widely researched and practiced under the name of "mobility management," but the fact is that little has been done to focus on "health" compared to its importance in such efforts.
In this context, this study theoretically and empirically revealed the existence of a causal process in which the more positive the attitude toward physical activity, i.e., the stronger the tendency to perceive the health-promoting effects of physical activity such as going out and walking, the stronger the tendency to use the bus. This suggests that the promotion of health consciousness among the public may lead to the unintended consequence of encouraging people to use the bus, and at the same time, the promotion that includes the message that bus use is more physically active than driving and "good for health" may promote bus use more effectively. Furthermore, by applying the theory of expected behavior, which is a standard behavioral model in psychology, the more people believe that the health-promoting effects of going out and walking are important, the more they will feel positive about using the bus, and the more they will think that using the bus is not a difficult act but rather an easy act. It is also theoretically and empirically clear that people will come to believe that the people around them will also have a positive view of their own bus usage, and that this belief may lead to further increases in bus usage. These findings can serve as a keystone for the development of measures to promote bus usage with health as the keyword.
Finally, through a multi-group test, the results show that the effect of attitudes toward these activities on promoting bus use is more pronounced among young people, car users, and people who live near bus stops. This can be a valuable basic knowledge to assume more effective targets when considering the above-mentioned message delivery efforts.
As described above, this study tackles the issue of bus utilization promotion measures, which have been strongly emphasized in recent years when considering regional transportation policies, from a new perspective of health awareness, conducts a high level of empirical analysis, and succeeds in extracting many findings of practical value. I recommend this paper as suitable for the Best Paper Award of the International Association of Traffic and Safety Sciences.
Title：Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis
Recipient：Elhashemi M. Ali, Mohamed M. Ahmed, Guangchuan Yang
Reason for award：
As represented by the keyword "CASE" in recent years, the use of real-time data and efficient information processing are becoming extremely important in a mixed traffic environment, such as the spread of advanced driver assistance systems and the appearance of SAE automated driving level 3 production vehicles, as well as reference to advanced map information and bidirectional data communication using connected technologies. If it is possible to capture changes in driving behavior due to weather factors and reflect them in the support timing, the realization of a higher level of safety and security becomes a possibility.
This paper analyzes the occurrence of collision risk on highways using a subset of the Naturalistic Driving Study (NDS) data that was extensively collected during the Second Strategic Highway Research Program (SHRP2) at the U.S. Department of Transportation, including vehicle motion, radar data, GPS data, brake pedal operation, and front and rear video recordings. It shows how drivers compensate differently depending on weather conditions to avoid collision events, provides thresholds to distinguish between normal and dangerous driving patterns in both rainy and sunny conditions, and shows how trajectory analysis can help to better identify driving patterns during specific events.Here, using items related to vehicle kinematics such as time to collision (TTC) and yaw rate change, it was found that drivers slowed down and changed lanes less frequently in wet weather than in sunny weather. In addition, in order to detect dangerous driving patterns more efficiently, K-means cluster analysis, an unsupervised machine learning method, is used to analyze when the transition of vehicle motions from normal driving to risky driving conditions occurs for both sunny and rainy weather. The results of the analysis showed that the time of exposure to collision risk tends to be longer in rainy weather than in sunny weather, both before and after the onset of risk, and the time of influence. It is reported that the difference between risky driving and normal driving can be clearly classified into patterns by means of data plots using speed, acceleration/deceleration rate, and yaw rate.
This analysis has the advantage of being able to quickly classify patterns of non-normal driving behavior because it uses only dynamic data from the connected vehicle and does not use secondary task information. Using this analysis in a connected environment, which is becoming more and more popular, variable speed limits can be updated for hot spots of dangerous risk due to weather changes. In this paper, only K-means cluster analysis is used for pattern classification, but there is a possibility of improving the accuracy and expanding the adaptation to other driving environments by introducing advanced time series clustering algorithms with additional parameters in the future.
As connected vehicles are expected to spread rapidly in the future, the analysis method based on social implementation is practical, and the research results will contribute to the realization of a more advanced road safety society. Therefore, I would like to recommend this paper as a suitable one to be nominated for the Best Paper Award of the International Association of Traffic and Safety Sciences.
