Abstract
This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.
Highlights
In China and many other developing countries, it has been a common phenomenon that passenger car (PC) driving is mixed with heavy vehicles (HVs), i.e., trucks and buses, on both freeways and arterials [1,2,3], which is defined as heterogeneous traffic flow
This paper employed a video-based speed data collection approach to investigate the impacts of heavy vehicles on speed variation; it assessed the risk of rear-end crashes for different vehicle-following patterns
Field data revealed that average speed displays a decreasing trend with the increase of HV percentage; the variation of speed first increases with HV percentage and reaches the maximum when HV percentage is at around 0.35; it shows a decreasing trend with HV percentage
Summary
In China and many other developing countries, it has been a common phenomenon that passenger car (PC) driving is mixed with heavy vehicles (HVs), i.e., trucks and buses, on both freeways and arterials [1,2,3], which is defined as heterogeneous traffic flow. HV drivers tend to overestimate space headways, which increases the risk of rear-end collisions [30] With this concern, it is necessary to take into account the impacts of vehicle characteristics and driving behavior on traffic operations when assessing the risk of crashes on freeways. It is necessary to take into account the impacts of vehicle characteristics and driving behavior on traffic operations when assessing the risk of crashes on freeways In this regard, this research aims to investigate the risks of rear-end crashes for different vehicle-following patterns based on microscopic real-world traffic flow data; it could provide recommendations to drivers regarding the potential crash risk and minimum safe vehicle-following headways, so that they can timely adjust their driving behavior to reduce the possibility of being involved in a rear-end crash.
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