Yancheng Ling , Zhenliang Ma , Yuchen Song , Qi Zhang , Xiaoxiong Weng , Xiaolei Ma
{"title":"基于多源车载传感器数据的交叉口公交司机减速行为建模","authors":"Yancheng Ling , Zhenliang Ma , Yuchen Song , Qi Zhang , Xiaoxiong Weng , Xiaolei Ma","doi":"10.1016/j.jpubtr.2025.100123","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding the impact of various factors on bus deceleration behavior at intersections has important implications for bus operations control, management, and safety. This paper develops a multiple linear regression model to analyze the factors influencing bus driver deceleration (a proxy of safe driving state) at intersections using data from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation, and the driver profile data. We develop a comprehensive model data extraction framework and corresponding methods to effectively estimate/calculate the bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, and approaching speed), intersection characteristics (types, the number of lanes, zebra crossing, divider, bus lanes, right turn lanes, the stop location) and traffic conditions (surrounding vehicles). Generally, drivers with younger ages (having short reaction times) and driving with psychological anticipation of complex situations (from surrounding vehicles and pedestrians or unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system alarms (rather than flooding all alerts). Also, the planning of bus infrastructures (e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors at intersections.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100123"},"PeriodicalIF":2.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bus driver deceleration behavior modeling at intersections using multi-source on-board sensor data\",\"authors\":\"Yancheng Ling , Zhenliang Ma , Yuchen Song , Qi Zhang , Xiaoxiong Weng , Xiaolei Ma\",\"doi\":\"10.1016/j.jpubtr.2025.100123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding the impact of various factors on bus deceleration behavior at intersections has important implications for bus operations control, management, and safety. This paper develops a multiple linear regression model to analyze the factors influencing bus driver deceleration (a proxy of safe driving state) at intersections using data from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation, and the driver profile data. We develop a comprehensive model data extraction framework and corresponding methods to effectively estimate/calculate the bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, and approaching speed), intersection characteristics (types, the number of lanes, zebra crossing, divider, bus lanes, right turn lanes, the stop location) and traffic conditions (surrounding vehicles). Generally, drivers with younger ages (having short reaction times) and driving with psychological anticipation of complex situations (from surrounding vehicles and pedestrians or unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system alarms (rather than flooding all alerts). Also, the planning of bus infrastructures (e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors at intersections.</div></div>\",\"PeriodicalId\":47173,\"journal\":{\"name\":\"Journal of Public Transportation\",\"volume\":\"27 \",\"pages\":\"Article 100123\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Public Transportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077291X25000086\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Public Transportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077291X25000086","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Bus driver deceleration behavior modeling at intersections using multi-source on-board sensor data
Understanding the impact of various factors on bus deceleration behavior at intersections has important implications for bus operations control, management, and safety. This paper develops a multiple linear regression model to analyze the factors influencing bus driver deceleration (a proxy of safe driving state) at intersections using data from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation, and the driver profile data. We develop a comprehensive model data extraction framework and corresponding methods to effectively estimate/calculate the bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, and approaching speed), intersection characteristics (types, the number of lanes, zebra crossing, divider, bus lanes, right turn lanes, the stop location) and traffic conditions (surrounding vehicles). Generally, drivers with younger ages (having short reaction times) and driving with psychological anticipation of complex situations (from surrounding vehicles and pedestrians or unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system alarms (rather than flooding all alerts). Also, the planning of bus infrastructures (e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors at intersections.
期刊介绍:
The Journal of Public Transportation, affiliated with the Center for Urban Transportation Research, is an international peer-reviewed open access journal focused on various forms of public transportation. It publishes original research from diverse academic disciplines, including engineering, economics, planning, and policy, emphasizing innovative solutions to transportation challenges. Content covers mobility services available to the general public, such as line-based services and shared fleets, offering insights beneficial to passengers, agencies, service providers, and communities.