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This study examines the impacts of cooperative adaptive cruise control (CACC) and cooperative lane changing (CLC) on the delay and the riding comfort in autonomous car–autonomous truck (AC–AT) mixed traffic at a freeway merging area. For this task, AC–AT mixed traffic on a 5.25 km section of freeway was analyzed using the Aimsun Next microscopic traffic simulation. The effects of different CACC parameters and CLC on the average speed and acceleration distributions as the measures of delay and riding comfort, respectively, were evaluated. It was found that (1) lower sensitivity to the lead vehicle reduced the merging time, (2) shorter time gaps between autonomous vehicles and between platoons decreased the delay, and (3) longer time gaps reduced the delay at higher percentage of ATs. These results demonstrate that the delay of AC–AT mixed traffic at a freeway merging area can be reduced and riding comfort can be increased by adjusting CACC parameters. [ABSTRACT FROM AUTHOR]

This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver’s reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U.S.A. It was found that the CPI was generally higher for the following heavy vehicle than the following car due to heavy vehicle’s lower braking capability. This study also validates the CPI using the simulated traffic data which replicate the observed traffic conditions a few minutes before the crash time upstream and downstream of the crash locations. The observed data were obtained from crash records and loop detectors on a section of the Gardiner Expressway in Toronto, Canada. The result shows that the values of CPI were consistently higher during the traffic conditions immediately before the crash time (crash case) than the normal traffic conditions (non-crash case). This demonstrates that the CPI can be used to capture rear-end collision risk during car-following maneuver on freeways. The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors. [ABSTRACT FROM AUTHOR]

This study investigates the effects of the traffic flow parameters related to individual drivers' lane-changing and car-following behavior on the occurrence of sideswipe and rear-end crashes on freeways. A total of 184 sideswipe and 605 rear-end crashes on I-4 freeway in Orlando, Florida, with the loop detector data were used for the analysis. A set of the Bayesian logistic regression models were developed to estimate the likelihood of crashes in a specific lane compared to the adjacent lanes using the 5 min average and lane-by-lane traffic flow parameters. The analysis results showed that the significant traffic flow parameters affecting crash occurrence in a specific lane are distinctively different between sideswipe and rear-end crashes. The flow-related variables were significant in the sideswipe crash models whereas the speed-related variables were significant in the rear-end crash models. The results suggest that the lane-by-lane traffic flow parameters are considered as surrogate measures of lane-changing and car-following behavior that are associated with the occurrence of sideswipe and rear-end crashes. These parameters can potentially be used for real-time monitoring of the likelihood of crash occurrence by lane on instrumented freeways. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines automated control strategies of variable speed limits that aim at reducing crash potential on instrumented freeways. A real-time crash prediction model was developed to estimate crash potential based on short-term variation of traffic flow characteristics. A microscopic traffic simulation model was used to realistically simulate changes in traffic conditions as an effect of variable speed limits and combined with the crash prediction model for the evaluation of control logics. Within this integrated evaluation framework, the study investigated the effect of strategy control factors on the crash potential reduction and total travel time. The study results indicated that variable speed limits could reduce crash potential by 5–17%, by temporarily reducing speed limits during risky traffic conditions when crash potential exceeded the pre-specified threshold. [Copyright &y& Elsevier]

This paper makes use of a probabilistic model that predicts the likelihood of crashes (crash potential) on freeways on the basis of traffic flow conditions, in real-time crash prevention. The model was developed using incident logs and loop detector data collected over a 13-month period on the Gardiner Expressway in Toronto. Previous work suggested that an increase in levels of traffic turbulence generally yields high crash potential. Traffic turbulence was defined in terms of a series of crash precursors that represent traffic conditions that were present prior to crash occurrence. To apply the model in crash prevention, the link needs to be established between crash potential and real-time safety intervention. The objective of this paper is to explore this link for different thresholds of crash potential. The paper discusses the guidelines for evaluating the safety benefit of one crash prevention strategy (variable speed limits) and suggests the risk-based evaluation framework for real-time traffic control. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of the novel idea of real-time traffic safety improvement on freeways. Crash prone conditions on the freeway mainline and ramps were identified using loop detector data, then intelligent transportation systems (ITS) strategies to reduce the crash risk in real-time are proposed. Separate logistic regression models for assessing the risk of crashes occurring under two speed regimes were estimated. The results show that the variables in the two models are consistent with probable mechanisms of crashes under the respective regimes (high-to-moderate and low speed). This study also discusses the analysis of parameters and conditions that affect crash occurrence on freeway ramps by type (on-/off-ramp) and configurations (diamond, loop, etc.) using five-minute traffic flow data obtained from the loop detectors upstream and downstream of ramps to reflect actual traffic conditions prior to the time of crashes. Finally, several traffic management strategies are evaluated for the resulting traffic safety improvement in real-time using PARAMICS microscopic traffic simulation and the measures of crash potential determined through the logistic regression models. The results show that, while variable speed limit strategies reduced the crash potential under moderate-to-high speed conditions, ramp metering strategies were effective in reducing the crash potential during the low-speed conditions. [ABSTRACT FROM AUTHOR]