This paper describes efforts to optimize the parameters for a platoon identification and accommodation algorithm that minimizes vehicle emissions. The algorithm was developed and implemented in the AnyLogic framework, and was validated by comparing it to the results of prior research. A four-module flowchart was developed to analyze the traffic data and to identify platoons. The platoon end time was obtained from the simulation and used to calculate the offset of the downstream intersection. The simulation calculates vehicle emissions with the aid of the VT-Micro microscopic emission model. Optimization experiments were run to determine the relationship between platoon parameters and minimum- and maximum-emission scenarios. Optimal platoon identification parameters were found from these experiments, and the simulation was run with these parameters. The total time of all vehicles in the simulation was also found for minimum and maximum emissions scenarios. Time-space diagrams obtained from the simulations demonstrate that optimized parameters allow all cars to travel through the downstream intersection without waiting, and therefore cause a decrease in emissions by as much as 15.5%. This paper also discusses the outcome of efforts to leverage high resolution data obtained from WV-705 corridor in Morgantown, WV. The proposed model was developed for that purpose and implemented in the AnyLogic framework to simulate this particular road network with four coordinated signal-controlled intersections. The simulation was also used to calculate vehicle CO, HC, NOx emissions with the aid of the VT-Micro microscopic emission model. Offset variation was run to determine the optimal offsets for this particular road network with traffic volume, signal phase diagram and vehicle characteristics. A classifier was developed by discriminant analysis based on significant attributes of HRD. Equation of this classifier was developed to distinguish between set of timing plans that produce maximum emission from set of timing plans that produce maximum emission. Also, current work investigates the potential use of the GPS-based and similar priority systems by giving preemption through signalized intersections. Two flowcharts are developed to consider presence of emergency vehicle (EV) in the system so called EV life cycle and EV preemption (EVP). Three scenarios are implemented, namely base case scenario when no EV is involved, EV scenario when EV gets EVP only, and EV scenario when EV gets preemption by signals and right-of-way by other vehicles. Research makes an attempt to compare emission results of these scenarios to find out whether EV effects vehicle emission in the road network and what is the level of this influence if any. / Master of Science

According to NCHRP Report 493, there are five major left turn signal indications for permitted operations in the United States. They are: Circular Green (CG), Flashing Circular Red (FCR), Flashing Red Arrow (FRA), Flashing Circular Yellow (FCY) and Flashing Yellow Arrow (FYA). The main goal of this thesis is to study the driver behavior and analyze safety of drivers for different left turn indications using a real-time driving simulator. Different signal indications alter driver behavior which influences velocity and acceleration profiles. These profiles influence vehicular emissions and hence need to be studied as well. For this purpose, different scenarios are implemented in the driving simulator. Data is analyzed using Microsoft Excel, JMP Statistical tool and MATLAB. Safety of drivers is analyzed with respect to the parameter "Time to Collision (TTC)" which is directly obtained from simulator data. Vehicular emissions and fuel consumption are calculated using VT-Micro microscopic emissions model. Graphs are plotted for TTC and total emissions. Results indicate that for a day-time scenario, FCY and FYA are the most suitable left-turning indications whereas FCR and FRA are most suitable for a night-time scenario. / Master of Science