Connected vehicle (CV) technologies enable safe and interoperable wireless communication among vehicles and the infrastructure with the possibility to run many applications that can improve safety, and enhance mobility. This paper develops CV-based algorithms which use transit vehicle speed and the estimated time that the vehicle needs to arrive at an intersection to trigger transit signal priority (TSP) initiation. This information is updated each second based on the traffic conditions such as speed, a current distance of a transit vehicle to the intersection, and queue conditions. The algorithm uses the actual speed of a transit vehicle and its latitude/longitude (lat/lon) coordinates to compute the time that the vehicle needs to reach the stop line. It was tested on a real-world network using VISSIM traffic simulation, but can easily be implemented in the field, since it is using world coordinates. The upgraded algorithm was applied to a future bus rapid transit (BRT) scenario, and included different levels of conditional TSP, which depend on three combined conditions: the time that a transit vehicle needs to reach the stop line, the number of passengers on board, and the lateness that the transit vehicle experiences. The test-case network used for model building is a corridor consisting of ten signalized intersections along State Street in Salt Lake City, UT. The CV algorithms coupled with TSP can yield notable delay reductions for both the regular bus and the BRT of 33% and 12%, respectively.

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

This paper investigates the significant contributing factors affecting the injury severity of car drivers, car passengers, and truck occupants from the aspects of car-truck collisions along interstate 80 (I-80) in Wyoming using a binary logit model with a Bayesian inference approach. Fixed- and random-effects models were developed to examine the effects on severe and non-severe injuries. Results showed that the random-effects model provided a better fit to the data than the fixed-effects model. Parameter estimates were sampled from the posterior distributions using No-U-Turn Hamiltonian Monte Carlo (NUT HMC) sampling method because of its efficiency over other Monte Carlo Markov Chain (MCMC) techniques. The analysis showed that occupant behavior characteristics, such as impaired driving, alcohol or illegal drugs, fatigue, and dangerous driving significantly increased the probability of injury severity. Unlit conditions, inclement weather, challenging roadway geometry, presence of junctions, downgrades, and curve sections were also likely to lead to more severe occupant injuries. The significant finding of this paper concludes that car drivers are more responsible than truck drivers contributing more severe injuries in car-truck collisions. Findings from this study are expected to help WYDOT and other related agencies take necessary actions and decide on management strategies in reducing car-truck collisions.