Over the last few years, public transportation has become more desirable as capacity of existing roadways failed to keep up with rapidly increasing traffic demand. Buses are one of the most common modes of public transportation with low impact on network capacity, especially in small and congested urban areas. However, the use of regularly scheduled buses as the main public transport mode can become useless with the presence of traffic congestion and dense construction areas. In cases like these, innovative solutions, such as bus rapid transit (BRT), can provide an increased level of service without having to resort to other, more expensive modes, such as light rail transit (LRT) and metro systems (subways). Transit signal priority (TSP), which provides priority to approaching buses at signalized intersections by extending the green or truncating the red, can also increase the performance of the bus service. Understanding the combined impact of TSP and BRT on network traffic operations can be complex. Although TSP has been implemented worldwide, none of the previous studies have examined in depth the effects of using conditional and unconditional TSP strategies with a BRT system. The objective of this research is to evaluate the effectiveness of BRT without TSP, then with conditional or unconditional TSP strategies. The micro-simulation software VISSIM was used to compare different TSP and BRT scenarios. These simulation scenarios include the base scenario (before implementation of the TSP and BRT systems), Unconditional TSP (TSP activates for all buses), Conditional TSP 3 minutes behind (TSP only activates for buses that are 3 minutes or more behind schedule), Conditional TSP 5 minutes behind (only activates for buses 5 minutes or more behind schedule), BRT with no TSP, BRT with Unconditional TSP, BRT with Conditional TSP 3 minutes behind, and BRT with Conditional TSP 5 minutes behind. The VISSIM simulation model was developed, calibrated and validated using a variety of data that was collected in the field. These data included geometric data, (number of lanes, intersection geometries, etc.); traffic data (average daily traffic volumes at major intersections, turning movement percentages at intersections, heavy vehicle percentages, bus passenger data, etc.); and traffic control data (signal types, timings and phasings, split history, etc.). Using this field data ensured the simulation model was sufficient for modeling the test corridor. From this model, the main performance parameters (for all vehicles and for buses only) for through movements in both directions (eastbound and westbound) along the corridor were analyzed for the various BRT/TSP scenarios. These parameters included average travel times, average speed profiles, average delays, and average number of stops. As part of a holistic approach, the effects of BRT and TSP on crossing street delay were also evaluated. Simulation results showed that TSP and BRT scenarios were effective in reducing travel times (up to 26 %) and delays (up to 64%), as well as increasing the speed (up to 47%), compared to the base scenario. The most effective scenarios were achieved by combining BRT and TSP. Results also showed that BRT with Conditional TSP 3 minutes behind significantly improved travel times (17 – 26%), average speed (30 – 39%), and average total delay per vehicle (11 – 32%) for the main corridor through movements compared with the base scenario, with only minor effects on crossing street delays. BRT with Unconditional TSP resulted in significant crossing street delays, especially at major intersections with high traffic demand, which indicates that this scenario is impractical for implementation in the corridor. Additionally, BRT with Conditional TSP 3 minutes behind had better travel time savings than BRT with Conditional TSP 5 minutes behind for both travel directions, making this the most beneficial scenario. This research provided an innovative approach by using nested sets (hierarchical design) of TSP and BRT combination scenarios. Coupled with microscopic simulation, nested sets in the hierarchical design are used to evaluate the effectiveness of BRT without TSP, then with conditional or unconditional TSP strategies. The robust methodology developed in this research can be applied to any corridor to understand the combined TSP and BRT effects on traffic performance. Presenting the results in an organized fashion like this can be helpful in decision making. This research investigated the effects of BRT along I-Drive corridor (before and after conditions) at the intersection level. Intersection analysis demonstrated based on real life data for the before and after the construction of BRT using the Highway Capacity SoftwareTM (HCS2010) that was built based on the Highway Capacity Manual (HCM 2010) procedures for urban streets and signalized intersections. The performance measure used in this analysis is the level of service (LOS) criteria which depends on the control delay (seconds per vehicle) for each approach and for the entire intersection. The results show that implementing BRT did not change the LOS. However, the control delay has improved at most of the intersections' approaches. The majority of intersections operated with an overall LOS "C" or better except for Kirkman Road intersection (T2) with LOS "E" because it has the highest traffic volumes before and after BRT construction. This research also used regression analysis to observe the effect of the tested scenarios analyzed in VISSIM software compared to the No TSP – No BRT base model for all vehicles and for buses only. The developed regression model can predict the effect of each scenario on each studied Measures of Performance (MOE). Minitab statistical software was used to conduct this multiple regression analysis. The developed models with real life data input are able to predict how proposed enhancements change the studied MOEs. The BRT models presented in this research can be used for further sensitivity analysis on a larger regional network in the upcoming regional expansion of the transit system in Central Florida. Since this research demonstrated the operational functionality and effectiveness of BRT and TSP systems in this critical corridor in Central Florida, these systems' accomplishments can be expanded throughout the state of Florida to provide greater benefits to transit passengers. Furthermore, to demonstrate the methodology developed in this research, it is applied to a test corridor along International Drive (I-Drive) in Orlando, Florida. This corridor is key for regional economic prosperity of Central Florida and the novel approach developed in this dissertation can be expanded to other transit systems.

Traffic light control (TLC) with transit signal priority (TSP) is an effective way to deal with urban congestion and travel delay. The growing amount of available connected vehicle data offers opportunities for signal control with transit priority, but the conventional control algorithms fall short in fully exploiting those datasets. This paper proposes a novel approach for dynamic TLC with TSP at an urban intersection. We propose a deep reinforcement learning based framework JenaRL to deal with the complex real-world intersections. The optimisation focuses on TSP while balancing the delay of all vehicles. A two-layer state space is defined to capture the real-time traffic information, i.e. vehicle position, type and incoming lane. The discrete action space includes the optimal phase and phase duration based on the real-time traffic situation. An intersection in the inner city of Jena is constructed in an open-source microscopic traffic simulator SUMO. A time-varying traffic demand of motorised individual traffic (MIT), the current TLC controller of the city, as well as the original timetables of the public transport (PT) are implemented in simulation to construct a realistic traffic environment. The results of the simulation with the proposed framework indicate a significant enhancement in the performance of traffic light controller by reducing the delay of all vehicles, and especially minimising the loss time of PT.