Empirical modeling of vehicle-bicycle interactions on high-speed rural roads: passing lateral distance and speed

Driving behavior and interactions with bicyclists on rural roads have not been quantified and modeled extensively. Naturalistic bicycling data for 1,991 passing events were collected on a rural two-lane roadway (55 mph, 88 kph speed limit) to quantify how opposing traffic and vehicle platooning influence passing lateral distance, speed, and aerodynamic forces. Results indicate that opposing traffic significantly reduces passing lateral distance by an average of 2.0 ft (61 cm) and decreases speed by an average of 2.3 mph (3.7 kph). Platooning leads to progressively reduced passing distance and speed among following vehicles. The reductions reflect limited available space and increased risk for bicyclists when opposing vehicles are present. The estimated aerodynamic lateral forces created by passenger vehicles were well below tolerable safety limits for bicyclists. To surpass tolerable limits, passenger vehicles would have to pass at a lateral distance of 0.9 ft (27 cm) at a speed of 55 mph (88 kph). Lateral distance and speed were found to be independent at a disaggregate level. Leading vehicles' lateral distance followed a Log-normal distribution and speed followed a Weibull distribution. Theoretical joint probability density functions were developed for leading and following vehicles with and without opposing traffic. Pairwise differences among lead and follower vehicles were similar and resembled a Normal distribution. The developed joint probability density functions can be used for calibration and validation of driving simulators, or development of autonomous and artificial intelligence driving models. Results contribute to developing safer design guidance and risk mitigating strategies for bicyclists.

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Publication Details

Journal
Accident Analysis & Prevention
Published
2026-04-15
DOI
https://doi.org/10.1016/j.aap.2026.108548
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
0.00

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article

Empirical modeling of vehicle-bicycle interactions on high-speed rural roads: passing lateral distance and speed

David A. Noyce, Andrea Bill, Boris Claros, Madhav Chitturi
Accident Analysis & Prevention
Traffic and Road Safety
article

Empirical modeling of vehicle-bicycle interactions on high-speed rural roads: passing lateral distance and speed

David A. Noyce, Andrea Bill, Boris Claros, Madhav Chitturi
article en

Abstract

Driving behavior and interactions with bicyclists on rural roads have not been quantified and modeled extensively. Naturalistic bicycling data for 1,991 passing events were collected on a rural two-lane roadway (55 mph, 88 kph speed limit) to quantify how opposing traffic and vehicle platooning influence passing lateral distance, speed, and aerodynamic forces. Results indicate that opposing traffic significantly reduces passing lateral distance by an average of 2.0 ft (61 cm) and decreases speed by an average of 2.3 mph (3.7 kph). Platooning leads to progressively reduced passing distance and speed among following vehicles. The reductions reflect limited available space and increased risk for bicyclists when opposing vehicles are present. The estimated aerodynamic lateral forces created by passenger vehicles were well below tolerable safety limits for bicyclists. To surpass tolerable limits, passenger vehicles would have to pass at a lateral distance of 0.9 ft (27 cm) at a speed of 55 mph (88 kph). Lateral distance and speed were found to be independent at a disaggregate level. Leading vehicles' lateral distance followed a Log-normal distribution and speed followed a Weibull distribution. Theoretical joint probability density functions were developed for leading and following vehicles with and without opposing traffic. Pairwise differences among lead and follower vehicles were similar and resembled a Normal distribution. The developed joint probability density functions can be used for calibration and validation of driving simulators, or development of autonomous and artificial intelligence driving models. Results contribute to developing safer design guidance and risk mitigating strategies for bicyclists.

Accident Analysis & PreventionVol. 232
Wisconsin Department of Transportation (US), University of Wisconsin–Madison (US)
University of Wisconsin-Madison
Openalex Percentile: Top 17%
Traffic and Road Safety
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