Understanding electric bike riders’ intention to violate traffic rules and accident proneness in China

2021 ◽  
Vol 23 ◽  
pp. 25-38
Author(s):  
Tianpei Tang ◽  
Yuntao Guo ◽  
Xizhao Zhou ◽  
Samuel Labi ◽  
Senlai Zhu
1974 ◽  
Vol 7 (2) ◽  
pp. 118-121 ◽  
Author(s):  
Joseph T. Kunce ◽  
Charles W. Reeder
Keyword(s):  

2021 ◽  
Author(s):  
Muhammad Asyraf Mohd Kassim ◽  
Suhaila Abdul Hanan ◽  
Muhammad Safizal Abdullah ◽  
Chan Pei Hong

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 437
Author(s):  
Yuya Onozuka ◽  
Ryosuke Matsumi ◽  
Motoki Shino

Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.


2021 ◽  
Vol 13 (12) ◽  
pp. 6816
Author(s):  
Gaofeng Gu ◽  
Tao Feng ◽  
Chixing Zhong ◽  
Xiaoxi Cai ◽  
Jiang Li

Life course events can change household travel demand dramatically. Recent studies of car ownership have examined the impacts of life course events on the purchasing, replacing, and disposing of cars. However, with the increasing diversification of mobility tools, changing the fleet size is not the only option to adapt to the change caused by life course events. People have various options with the development of sustainable mobility tools including electric car, electric bike, and car sharing. In order to determine the impacts of life course events on car ownership and the decision of mobility tool type, a stated choice experiment was conducted. The experiment also investigated how the attributes of mobility tools related to the acceptance of them. Based on existing literature, we identified the attributes of mobility tools and several life course events which are considered to be influential in car ownership decision and new types of mobility tools choice. The error component random parameter logit model was estimated. The heterogeneity across people on current car and specific mobility tools are considered. The results indicate people incline not to sell their current car when they choose an electric bike or shared car. Regarding the life course events, baby birth increases the probability to purchase an additional car, while it decreases the probability to purchase an electric bike or joining a car sharing scheme. Moreover, the estimation of error components implies that there is unobserved heterogeneity across respondents on the sustainable mobility tools choice and the decision on household’s current car.


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