The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this study, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data with instrumented vehicles that were carried out on public roads in Ann Arbor, Michigan. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message (BSM) data in connected vehicle dataset, this study modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of Random Forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers' instantaneous decisions and identify their aggressive driving behavior. The results of this study inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles (V2V) and vehicles-to-infrastructure (V2I) applications.
Crossing elimination is a relatively recent strategy that emergency managers and departments of transportation may consider during no-notice evacuations. In this strategy, certain intersection movements that may be permissible under normal operating conditions are prohibited so that arterial traffic flow will increase. A few previous studies examined this strategy with contraflow operations. However, the benefits of crossing elimination alone remain unclear. An assessment of the effects of intersection crossing elimination during evacuations helps fill this knowledge gap. A simulation–optimization model was developed to determine the near-optimal configuration of intersection movements from a set of pre-specified possible configurations for intersections in a given area. At the optimization level, the total travel time of evacuees was minimized, and at the lower level, all traffic was assigned to the network with DYNUST traffic assignment simulation software. A simulated annealing heuristic was used to solve the optimization problem. The entire method was applied to a real network to assess the impact of crossing elimination. Three scenarios were developed with combinations of evacuee destination and departure time distributions. Results for these scenarios indicated that total evacuee travel time was improved by about 3% to 5% (9,700 to 11,200 h for about 300,000 evacuees). The availability of through movements and the elimination of merging points were the two factors influencing the selection of modified configurations for intersection movement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.