Development and evaluation of cooperative intersection management algorithm under connected vehicles environment

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various automated and connected vehicle (C/AV) solutions around the globe. Wireless communication technologies such as the dedicated short-range communication (DSRC) protocol are enabling instantaneous information exchange between vehicles and infrastructure. Such information exchange produces tremendous benefits with the possibility to automate conventional traffic streams and enhance existing signal control strategies. While many promising studies in the area of signal control under connected vehicle (CV) environment have been introduced, they mainly offer solutions designed to operate a single isolated intersection or they require high technology penetration rates to operate in a safe and efficient manner. Applications designed to operate on a signalized corridor with imperfect market penetration rates of connected vehicle technology represent a bridge between conventional traffic control paradigm and fully automated corridors of the future. Assuming utilization of the connected vehicle environment and vehicle to infrastructure (V2I) technology, all vehicular and signal-related parameters are known and can be shared with the control agent to control automated vehicles while improving the mobility of the signalized corridor. This dissertation research introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. The Trajectory-driven Optimization for Automated Driving (TOAD) provides an optimal trajectory for automated vehicles while maintaining safe and uninterrupted movement of general traffic, consisting of regular unequipped vehicles. Signal status parameters such as cycle length and splits are continuously captured. At the same time, vehicles share their position information with the control agent. Both inputs are then used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. To determine the most efficient trajectory for automated vehicles, an evolutionary-based optimization is utilized. Influence of the prevailing traffic conditions is incorporated into a control algorithm using conventional data collection methods such as loop detectors, Bluetooth or Wi-Fi sensors to collect vehicle counts, travel time on corridor segments, and spot speed. Moreover, a short-term, artificial intelligence prediction model is developed to achieve reasonable deployment of data collection devices and provide accurate vehicle delay predictions producing realistic and highly-efficient longitudinal vehicle trajectories. The concept evaluation through microsimulation reveals significant mobility improvements compared to contemporary corridor management approach. The results for selected test-bed locations on signalized arterials in New Jersey reveals up to 19.5 % reduction in overall corridor travel time depending on different market penetration and lane configuration scenario. It is also discovered that operational scenarios with a possibility of utilizing reserved lanes for movement of automated vehicles further increases the effectiveness of the proposed algorithm. In addition, the proposed control algorithm is feasible under imperfect C/AV market penetrations showing mobility improvements even with low market penetration rates