As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

The dissertation proposed an approach, named "Signal Optimization Under Rail Crossing sAfety cOnstraints"(SOURCAO), to the traffic signal control near a highway rail grade crossing (HRGC). SOURCAO targets two objectives: HRGC safety improvement (a high priority national transportation goal) and highway traffic delay reduction (a common desire for virtually all of us). Communication and data availability from ITS and the next generation train control are assumed available in SOURCAO. The first step in SOURCAO is to intelligently choose a proper preemption phase sequence to promote HRGC safety. An inference engine is designed in place of traditional traffic signal preemption calls to prevent the queue from backing onto HRGC. The potential hazard is dynamically examined as to whether any queuing vehicle stalls on railroad tracks. The inference engine chooses the appropriate phase sequence to eliminate the hazardous situation. The second step in SOURCAO is to find the optimized phase length. The optimization process uses the network traffic delay (close to the control delay) at the intersections within HRGC vicinities as an objective function. The delay function is approximated and represented by multilayer perceptron neural network (off-line). After the function was trained and obtained, an optimization algorithm named Successive Quadratic Programming (SQP) searches the length of phases (on-line) by minimizing the delay function. The inference engine and proposed delay model in optimization take the on-line surveillance detector data and HRGC closure information as input. By integrating artificial intelligence and optimization technologies, the independent simulation evaluation of SOURCAO by TSIS/CORSIM demonstrated that the objectives are reached. The average network delay for 20 runs of simulation evaluation is reduced over eight percent by a t-test while the safety of HRGC is promoted. The sensitivity tests demonstrate that SOURCAO works efficiently under light and heavy traffic conditions, as well as a wide range of HRGC closure times. / Ph. D.

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Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands. / Master of Science

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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.

This dissertation proposes a Dynamic Message Signs (DMS) diversion control system based on principles of existing Advanced Traveler Information Systems and Advanced Traffic Management Systems (ATMS). The objective of the proposed system is to alleviate total corridor traffic delay by choosing optimized diversion rate and alternative road signal-timing plan. The DMS displays adaptive messages at predefined time interval for guiding certain number of drivers to alternative roads. Messages to be displayed on the DMS are chosen by an on-line optimization model that minimizes corridor traffic delay. The expected diversion rate is assumed following a distribution. An optimization model that considers three traffic delay components: mainline travel delay, alternative road signal control delay, and the travel time difference between the mainline and alternative roads is constructed. Signal timing parameters of alternative road intersections and DMS message level are the decision variables; speeds, flow rates, and other corridor traffic data from detectors serve as inputs of the model. Traffic simulation software, CORSIM, served as a developmental environment and test bed for evaluating the proposed system. MATLAB optimization toolboxes have been applied to solve the proposed model. A CORSIM Run-Time-Extension (RTE) has been developed to exchange data between CORSIM and the adopted MATLAB optimization algorithms (Genetic Algorithm, Pattern Search in direct search toolbox, and Sequential Quadratic Programming). Among the three candidate algorithms, the Sequential Quadratic Programming showed the fastest execution speed and yielded the smallest total delays for numerical examples. TRANSYT-7F, the most credible traffic signal optimization software has been used as a benchmark to verify the proposed model. The total corridor delays obtained from CORSIM with the SQP solutions show average reductions of 8.97%, 14.09%, and 13.09% for heavy, moderate and light traffic congestion levels respectively when compared with TRANSYT-7F optimization results. The maximum model execution time at each MATLAB call is fewer than two minutes, which implies that the system is capable of real world implementation with a DMS message and signal update interval of two minutes.

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This research contributes to the state of the art and state of the practice in solving a very important and computationally challenging problem in the areas of urban transportation systems, operations research, disaster management, and public policy. Being a very active topic of research during the past few decades, the problem of developing an efficient and practical strategy for evacuation of real-sized urban traffic networks in case of disasters from different causes, quickly enough to be employed in immediate disaster management scenarios, has been identified as one of the most challenging and yet vital problems by many researchers. More specifically, this research develops fast methods to find the optimal integrated strategy for traffic routing and traffic signal control to evacuate real-sized urban networks in the most efficient manner. In this research a solution framework is proposed, developed and tested which is capable of solving these problems in very short computational time. An efficient relaxation-based decomposition method is proposed, implemented for two evacuation integrated routing and signal control model formulations, proven to be optimal for both formulations, and verified to reduce the computational complexity of the optimal integrated routing and signal control problem. The efficiency of the proposed decomposition method is gained by reducing the integrated optimal routing and signal control problem into a relaxed optimal routing problem. This has been achieved through an insight into intersection flows in the optimal routing solution: in at least one of the optimal solutions of the routing problem, each street during each time interval only carries vehicles in at most one direction. This property, being essential to the proposed decomposition method, is called "unidirectionality" in this dissertation. The conditions under which this property exists in the optimal evacuation routing solution are identified, and the existence of unidirectionality is proven for: (1) the common Single-Destination System-Optimal Dynamic Traffic Assignment (SD-SODTA) problem, with the objective to minimize the total time spent in the threat area; and, (2) for the single-destination evacuation problem with varying threat levels, with traffic models that have no spatial queue propagation. The proposed decomposition method has been implemented in compliance with two widely-accepted traffic flow models, the Cell Transmission Model (CTM) and the Point Queue (PQ) model. In each case, the decomposition method finds the optimal solution for the integrated routing and signal control problem. Both traffic models have been coded and applied to a realistic real-size evacuation scenario with promising results. One important feature that is explored is the incorporation of evacuation safety aspects in the optimization model. An index of the threat level is associated with each link that reflects the adverse effects of traveling in a given threat zone on the safety and health of evacuees during the process of evacuation. The optimization problem is then formulated to minimize the total exposure of evacuees to the threat. A hypothetical large-scale chlorine gas spill in a high populated urban area (downtown Tucson, Arizona) has been modeled for testing the evacuation models where the network has varying threat levels. In addition to the proposed decomposition method, an efficient network-flow solution algorithm is also proposed to find the optimal routing of traffic in networks with several threat zones, where the threat levels may be non-uniform across different zones. The proposed method can be categorized in the class of "negative cycle canceling" algorithms for solving minimum cost flow problems. The unique feature in the proposed algorithm is introducing a multi-source shortest path calculation which enables the efficient detection and cancellation of negative cycles. The proposed method is proven to find the optimal solution, and it is also applied to and verified for a mid-size test network scenario.