The dissertation presents a connected vehicle based traffic signal control model (CVTSCM) for signalized arterials. The model addresses different levels of traffic congestion starting with the initial deployment of connected vehicle technologies focusing on two modules created in CVTSCM. For near/under-saturated intersections, an arterial-level traffic progression optimization model (ALTPOM) is being proposed. ALTPOM improves traffic progression by optimizing offsets for an entire signalized arterial simultaneously. To optimize these offsets, splits of coordinated intersections are first adjusted to balance predicted upcoming demands of all approaches at individual intersections. An open source traffic simulator was selected to implement and evaluate the performance of ALTPOM. The case studies’ field signal timing plans were coordinated and optimized using TRANSYT-7F as the benchmark. ALTPOM was implemented with connected vehicles penetration rates at 25% and 50%, ALTPOM significantly outperforms TRANSYT-7F with at least 26.0% reduction of control delay (sec/vehicle) and a 4.4% increase of throughput for both directions of major and minor streets. This technique differs from traditional traffic coordination which prioritizes major street traffic, and thereby generally results in degrading performance on minor streets. ALTPOM also provides smooth traffic progression for the coordinated direction with little impact on the opposite direction. The performance of ALTPOM improves as the penetration rate of connected vehicles increases. For saturated/oversaturated conditions, two queue length management based Active Traffic Management (ATM) strategies are proposed, analytically investigated, and experimentally validated. The first strategy distributes as much green time as possible for approaches with higher saturation discharge rate in order to reduce delay. For the second approach, green times are allocated to balance queue lengths of major and minor streets preventing queue spillback or gridlock. Both strategies were formulated initially using uniform arrival and departure, and then validated using field vehicle trajectory data. After validation of the modules, the effectiveness of CVTSCM is proven. Then, conclusions and recommendations for future researches are presented at the end.

Traffic signal control systems have been tremendously improved since the first colored traffic signal light was installed in London in December 1868. There are many different types of traffic signal control systems that can be categorized into three major control types: fixed-time, actuated, and adaptive. Choosing a proper traffic signal system is very important since there exists no perfect signal control strategy that fits every traffic network. One example is traffic signal coordination, which is the most widely used traffic signal control system. It is believed that performance measures, such as travel times, vehicle delay, and number of stops, can be enhanced by synchronizing traffic signals over a corridor. However, it is not always true that the coordination will have the same benefits for all the traffic in the network. Most of the research on coordination has focused only on strengthening the major movement along the coordinated routes without considering system-wide impacts on other traffic. Therefore, before implementing a signal control system to a specific traffic network, a thorough investigation should be conducted to see how the control strategy may impact the entire network in terms of the objectives of each type of traffic control system. This dissertation first considers two different kinds of systematic performance analyses for traffic signal control systems. Then, it presents two types of signal control strategies that account for current issues in coordination and priority control systems, respectively. First, quantitative analysis of smooth progression for traffic flow is investigated using connected vehicle technology. Many studies have been conducted to measure the quality of progression, but none has directly considered smooth progression as the significant factor of coordination, despite the fact that the definition of coordination states that the goal is to have smooth traffic flow. None of the existing studies concentrated on measuring a continuous smooth driving pattern for each vehicle in terms of speed. In order to quantify the smoothness, this dissertation conducts an analysis of the speed variation of vehicles traveling along a corridor. A new measure is introduced and evaluated for different kinds of traffic control systems. The measure can be used to evaluate how smoothly vehicles flow along a corridor based on the frequency content of vehicle speed. To better understand the impact of vehicle mode, a multi-modal analysis is conducted using the new measure. Second, a multi-modal system-wide evaluation of traffic signal systems is conducted. This analysis is performed for traffic signal coordination, which is compared with fully actuated control in terms of a systematic assessment. Many optimization models for coordination focus mainly on the objective of the coordinated route and do not account for the impacts on side street movements or other system-wide impacts. In addition, multi-modality is not considered in most optimized coordination plans. Thus, a systematic investigation of traffic signal coordination is conducted to analyze the benefits and impacts on the entire system. The vehicle time spent in the system is measured as the basis of the analysis. The first analysis evaluates the effect of coordination on each route based on a single vehicle mode (regular passenger vehicles). The second analysis reveals that how multi-modality affects the performance of the entire system. Third, in order to address traffic demand fluctuation and traffic pattern changes during coordination periods, this dissertation presents an adaptive optimization algorithm that integrates coordination with adaptive signal control using data from connected vehicles. Through the algorithm, the coordination plan can be updated to accommodate the traffic demand variation and remain optimal over the coordination period. The optimization framework consists of two levels: intersection and corridor. The intersection level handles phase allocation in real time based on connected vehicle trajectory data, while the corridor level deals with the offsets optimization. The corridor level optimization focuses on the performance of the vehicle movement along the coordinated phase, while at the intersection level, all movements are considered to create the optimal signal plan. The two levels of optimizations apply different objective functions and modeling methodologies. The objective function at the intersection level is to minimize individual vehicle delay for both coordinated and non-coordinated phases using dynamic programming (DP). At the corridor level, a mixed integer linear programming (MILP) is formulated to minimize platoon delay for the coordinated phase. Lastly, a peer priority control strategy, which is a methodology that enhances the multi modal intelligent traffic signal system (MMITSS) priority control model, is presented based on peer-to-peer (P2P) and dedicated short range communication (DSRC) in a connected vehicle environment. The peer priority control strategy makes it possible for a signal controller to have a flexible long-term plan for prioritized vehicles. They can benefit from the long-term plan within a secured flexible region and it can prevent the near-term priority actions from having a negative impact on other traffic by providing more flexibility for phase actuation. The strategy can be applied to all different modes of vehicles such as transit, freight, and emergency vehicles. Consideration for far side bus stops is included for transit vehicles. The research that is presented in this dissertation is constructed based on Standard DSRC messages from connected vehicles such as Basic Safety Messages (BSMs), Signal Phasing and Timing Messages (SPaTs), Signal Request Messages (SRMs), and MAP Messages, defined by Society of Automotive Engineers (SAE) (SAE International 2016).

This dissertation discusses network wide signal control strategies base on connected vehicle technology. Traffic congestion on arterials has become one of the largest threats to economic competitiveness, livability, safety, and long-term environmental sustainability in the United States. In addition, arterials usually experience more blockage than freeways, specifically in terms of intersection congestion. There is no doubt that emerging technologies provide unequaled opportunities to revolutionize “retiming” and mitigate traffic congestion. Connected vehicle technology provides unparalleled safety benefits and holds promise in terms of alleviating both traffic congestion and the environmental impacts of future transportation systems. The objective of this research is to improve the mobility, safety and environmental effects at signalized arterials with connected vehicles. The proposed solution of this dissertation is to formulate traffic signal control models for signalized arterials based on connected vehicle technology. The models optimize offset, split, and cycle length to minimize total queue delay in all directions of coordinated intersections. Then, the models are implemented in a centralized system—including closed-loop systems—first, before expanding the results to distributed systems. The benefits of the models are realized at the infant stage of connected vehicle deployment when the penetration rate of connected vehicles is around 10%. Furthermore, the benefits incentivize the growth of the penetration rate for drivers. In addition, this dissertation contains a performance evaluation in traffic delay, volume throughput, fuel consumption, emission, and safety by providing a case study of coordinated signalized intersections. The case study results show the solution of this dissertation could adapt early deployment of connected vehicle technology and apply to future connected vehicle technology development.