Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information
1
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2
Jiangsu Provincial Key Laboratory of Image Processing and Image Communication, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
3
School of Information Science and Electrical Engineering, ShanDong JiaoTong University, Jinan 250357, China
4
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Washington Yotto Ochieng
Sensors 2021, 21(3), 687; https://doi.org/10.3390/s21030687
Received: 8 December 2020 / Revised: 6 January 2021 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Special Issue High Performance Positioning, Navigation and Timing for Mobile Platforms)
The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.
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Keywords:
driving behavior; trajectory analysis; pseudorange differential positioning; road information; BeiDou navigation satellite system
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MDPI and ACS Style
Yang, Y.; Yan, J.; Guo, J.; Kuang, Y.; Yin, M.; Wang, S.; Ma, C. Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information. Sensors 2021, 21, 687. https://doi.org/10.3390/s21030687
AMA Style
Yang Y, Yan J, Guo J, Kuang Y, Yin M, Wang S, Ma C. Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information. Sensors. 2021; 21(3):687. https://doi.org/10.3390/s21030687
Chicago/Turabian StyleYang, Yuan; Yan, Jingjie; Guo, Jing; Kuang, Yujin; Yin, Mingyang; Wang, Shiniu; Ma, Caoyuan. 2021. "Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information" Sensors 21, no. 3: 687. https://doi.org/10.3390/s21030687
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