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Identifying Risky Driving Behaviors through Vehicle Trajectories Collected by On-Road Millimeter-Wave Radars
https://orcid.org/0000-0001-5330-3871
Policymakers demonstrate a keen interest in understanding risky driving behaviors to formulate effective countermeasures aimed at reducing accidents and economic losses. With the increasing deployment of millimeter-wave (MMW) radars on roadways, there exists a viable opportunity to gather extensive vehicle information at big data levels from individual drivers traversing through the radar detection range. This study endeavors to analyze traffic flow characteristics and identify risky driving behaviors using the noisy raw vehicle position and speed profiles obtained from MMW radars installed on a highway in China. A series of data cleaning procedures are meticulously implemented to address several typical trajectory errors stemming from MMW radars. Subsequently, after data cleaning, the study identifies risky driving behaviors through established methods found in the literature and evaluates the prevalence of these behaviors across different times of day and days of the week. This research mitigates the gap between raw vehicle trajectories from MMW radar and popular existing risk analysis methods. In addition, this research analyzes the temporal pattern of different risks and pinpoints their inherent connections. The outcomes of this research endeavor hold the potential to furnish practical insights for the formulation of targeted safety enhancement policies by governmental bodies or relevant agencies.
Identifying Risky Driving Behaviors through Vehicle Trajectories Collected by On-Road Millimeter-Wave Radars
https://orcid.org/0000-0001-5330-3871
Policymakers demonstrate a keen interest in understanding risky driving behaviors to formulate effective countermeasures aimed at reducing accidents and economic losses. With the increasing deployment of millimeter-wave (MMW) radars on roadways, there exists a viable opportunity to gather extensive vehicle information at big data levels from individual drivers traversing through the radar detection range. This study endeavors to analyze traffic flow characteristics and identify risky driving behaviors using the noisy raw vehicle position and speed profiles obtained from MMW radars installed on a highway in China. A series of data cleaning procedures are meticulously implemented to address several typical trajectory errors stemming from MMW radars. Subsequently, after data cleaning, the study identifies risky driving behaviors through established methods found in the literature and evaluates the prevalence of these behaviors across different times of day and days of the week. This research mitigates the gap between raw vehicle trajectories from MMW radar and popular existing risk analysis methods. In addition, this research analyzes the temporal pattern of different risks and pinpoints their inherent connections. The outcomes of this research endeavor hold the potential to furnish practical insights for the formulation of targeted safety enhancement policies by governmental bodies or relevant agencies.
Identifying Risky Driving Behaviors through Vehicle Trajectories Collected by On-Road Millimeter-Wave Radars
https://orcid.org/0000-0001-5330-3871
Policymakers demonstrate a keen interest in understanding risky driving behaviors to formulate effective countermeasures aimed at reducing accidents and economic losses. With the increasing deployment of millimeter-wave (MMW) radars on roadways, there exists a viable opportunity to gather extensive vehicle information at big data levels from individual drivers traversing through the radar detection range. This study endeavors to analyze traffic flow characteristics and identify risky driving behaviors using the noisy raw vehicle position and speed profiles obtained from MMW radars installed on a highway in China. A series of data cleaning procedures are meticulously implemented to address several typical trajectory errors stemming from MMW radars. Subsequently, after data cleaning, the study identifies risky driving behaviors through established methods found in the literature and evaluates the prevalence of these behaviors across different times of day and days of the week. This research mitigates the gap between raw vehicle trajectories from MMW radar and popular existing risk analysis methods. In addition, this research analyzes the temporal pattern of different risks and pinpoints their inherent connections. The outcomes of this research endeavor hold the potential to furnish practical insights for the formulation of targeted safety enhancement policies by governmental bodies or relevant agencies.
Identifying Risky Driving Behaviors through Vehicle Trajectories Collected by On-Road Millimeter-Wave Radars
J. Transp. Eng., Part A: Systems
Liu, Shaojie (author) / Deng, Bo (author) / Li, Aizeng (author)
2024-09-01
Article (Journal)
Electronic Resource
English
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