دورية أكاديمية

Identifying Potentially Risky Intersections for Heavy-Duty Truck Drivers Based on Individual Driving Styles

التفاصيل البيبلوغرافية
العنوان: Identifying Potentially Risky Intersections for Heavy-Duty Truck Drivers Based on Individual Driving Styles
المؤلفون: Yi Zhu, Yongfeng Ma, Shuyan Chen, Aemal J. Khattak, Qianqian Pang
المصدر: Applied Sciences; Volume 12; Issue 9; Pages: 4678
بيانات النشر: Multidisciplinary Digital Publishing Institute
سنة النشر: 2022
المجموعة: MDPI Open Access Publishing
مصطلحات موضوعية: driving style, driving behavior, K-means clustering, traffic control types of intersections, heavy-duty trucks
جغرافية الموضوع: agris
الوصف: In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behavior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersections, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kinematic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Python. Second, driving styles were classified into three driving styles: cautious, normal, and aggressive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, according to classifications of driving style, the impacts of four different intersection traffic control types: two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic control at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. ...
نوع الوثيقة: text
وصف الملف: application/pdf
اللغة: English
العلاقة: https://dx.doi.org/10.3390/app12094678Test
DOI: 10.3390/app12094678
الإتاحة: https://doi.org/10.3390/app12094678Test
حقوق: https://creativecommons.org/licenses/by/4.0Test/
رقم الانضمام: edsbas.1D1349C7
قاعدة البيانات: BASE