Book Description
Understanding how vehicle drivers and pedestrians interact is key to identifying countermeasures that improve the safety of the interactions. As a result, techniques that can be used to evaluate the effectiveness of traffic control device-based safety countermeasures without the need to wait for the availability of crash data are needed. Using video data, the interactions between right-turning vehicles and conflicting pedestrians were documented for sites with a permissive circular green indication or a flashing yellow arrow (FYA) permissive right turn indication and quantified using vehicle and pedestrian position timestamps. Multiple non-probabilistic linear regression models were created to describe the relationship between the position of the pedestrian within the crosswalk and the time for a right turning vehicle maneuver to be completed. Given the nature of the models output, a Pedestrian Respect Indicator (PRI) is introduced as an indicator of the safety of vehicle-pedestrian interactions. The higher the PRI, the more "respect" towards pedestrians. Surrogate safety measures (SSMs) have allowed to step away from traditional approach and analyze safety performance without relying on crash records. In recent years, the use of surrogate measures to estimate crash probabilities with extreme value theory (EVT) models has been an alternative approach to its use as aggregate crash frequency predictors. Univariate and bivariate extreme value theory models were developed using the block maxima (BM) approach and the peak over threshold (POT) approach. In addition, Bayesian hierarchical models were developed for each approach. Using the resulting estimates, the number of crashes was estimated for each model. The estimated crashes from the Bayesian hierarchical models were closer to the observed number of crashes than those from other models. Time to complete a turn produced better fit models indicating that the time to complete a turn is a good representation of traffic interactions. Obtaining SSMs from video data requires complex processing and large video data sizes. A software-based framework to estimate SSMs, such as PET and time-to-collision (TTC) values between right-turn-on-red (RTOR) and through vehicles was proposed and it demonstrated the feasibility of using vehicle trajectories obtained from existing radar-based vehicle detection systems to calculate such measures.
