Safety and operational impact of connected vehicles' lane configuration on freeway facilities with managed lanes.

Managed lanes (MLs) have been implemented as a vital strategy for traffic safety and management improvements. Previous studies involving MLs didn't examine the impact of connected vehicles' (CVs) lane configuration on freeway facilities with managed toll lanes. CVs are quickly expanding in the transportation industry and are among the most recent promising developments in traffic and safety engineering. In this study, several scenarios were tested using microscopic traffic simulation to determine the optimal CV lane configuration strategies while taking into consideration the market penetration rate (MPR%) of CVs and traffic conditions (i.e., peak, off-peak). Both safety (i.e., conflict frequency, conflict reduction) and operational (i.e., average speed, average delay) performance measures were included in the analyses. A Negative Binomial model was developed for investigating the factors that affect the safety measures. Tobit models were used to evaluate traffic operation. The results of the safety and operational analyses suggested that an MPR% between 10 % and 30 % was recommended when the CVs were only allowed in MLs. By converting one of the general-purpose lanes (GPLs) to a managed lane, the MPR% could reach 60 %. It was also concluded that restricting CVs to only the CV lane was not recommended. Lastly, the findings suggested that by allowing CVs to use all the lanes in the network (MLs, GPLs and CV lane), the optimal MPR% could reach between 70 % and 100 %. This study has major implications for improving MLs by recommending the optimal CV lane configuration and market penetration rate for each design.

Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones.

INTRODUCTION: In this paper, we present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. METHODS: We collected the 2010-2012 Statewide Traffic Analysis Zone (STAZ) level crash data and developed rigorous machine learning approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To our knowledge, this is the first application of DTR models in the burgeoning macro-level traffic safety literature. RESULTS: The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.

Longitudinal safety evaluation of connected vehicles' platooning on expressways.

Connected vehicles (CV) technology has recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. One of the biggest issues facing CVs popularization associates it with the market penetration rate (MPR). The full market penetration of CVs might not be accomplished recently. Therefore, traffic flow will likely be composed of a mixture of conventional vehicles and CVs. In this context, the study of CV MPR is worthwhile in the CV transition period. The overarching goal of this study was to evaluate longitudinal safety of CV platoons by comparing the implementation of managed-lane CV platoons and all lanes CV platoons (with same MPR) over non-CV scenario. This study applied the CV concept on a congested expressway (SR408) in Florida to improve traffic safety. The Intelligent Driver Model (IDM) along with the platooning concept were used to regulate the driving behavior of CV platoons with an assumption that the CVs would follow this behavior in real-world. A high-level control algorithm of CVs in a managed-lane was proposed in order to form platoons with three joining strategies: rear join, front join, and cut-in joint. Five surrogate safety measures, standard deviation of speed, time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), and sideswipe crash risk (SSCR) were utilized as indicators for safety evaluation. The results showed that both CV approaches (i.e., managed-lane CV platoons, and all lanes CV platoons) significantly improved the longitudinal safety in the studied expressway compared to the non-CV scenario. In terms of surrogate safety measures, the managed-lane CV platoons significantly outperformed all lanes CV platoons with the same MPR. Different time-to-collision (TTC) thresholds were also tested and showed similar results on traffic safety. Results of this study provide useful insight for the management of CV MPR as managed-lane CV platoons.