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Much effort has been made in the past on the supply side to relieve road traffic congestion which undermines the mobility in urban networks and brings heavy social costs, but building additional roadway capacity is no longer considered a viable option. A better alternative is the efficient management of existing networks, for which we can envisage new possibilities that emerge in light of the recent increase in the use of private providers' digital map and traffic information systems. These systems have evolved mostly without much public sector influence, but some paradigm shift is needed for thinking about the directions of future developments that will show societal benefits also open up private-sector opportunities. In this context, we develop a multi-agent advanced traffic management and information systems (ATMIS) framework with day-to-day dynamics where private agencies are included as traffic information service providers (ISPs) together with public agencies handling the traffic control and the users (drivers) as the decision-makers. One important paradigm shift is that the emergence of private ISPs makes it possible to obtain path-based data via retrieval of individual trajectory diaries and current position information from their subscribers. The availability of such path-based data can bring about the development of new path-based ATMIS algorithms. Such new algorithms can be capable of taking into account the routing effects of advanced traveler information systems (ATIS). Under the assumption that the traffic management center (TMC) has some (even approximate) knowledge of the ISPs' optimal strategies, it is possible to design optimal route guidance and control strategies (ORGCS) that takes into account the anticipated ISP reactions in terms of route-level flows. In light of these issues, we develop a routing-based real-time cycle-free network-wide signal control scheme (R2CFNet) that uses path-based data. The scheme also allows the avoidance of day-to-day games between ISPs and signal control through the use of weights on the queue delays in the control objective function. The weights are essentially operator parameters designed to incorporate ORGCS and day-to-day behavior. The proposed control scheme, of course, responds to detected traffic (demand) rates on a real-time basis in response to the control delays on network routes. Another theoretical advance in the research is in the development of a modeling scheme that uses a new optimization algorithm for a convergent simulation-based dynamic traffic assignment (DTA) model. This model incorporates a Gradient Projection (GP) algorithm, as opposed to the traditionally-used Method of Successive Averages (MSA), and it displays significantly better convergence characteristics. A consistent day-to-day dynamic framework is also developed, incorporating an elaborate microscopic simulation model to capture traffic network performance, to study network dynamics under multiple private ISPs and the new signal control scheme. The results of parametric simulations have shown that the proposed framework is capable of effectively capturing the effects of the interplay of urban traffic route guidance, network control and user response. It is seen that an appropriate combination of ATIS market penetration rate and the special-purpose signal control settings could divert some portion of travel demand to different routes. This is achieved by constraining the signal settings to conform to certain longer-term strategies. The performance and efficiency of the components of the proposed framework such as the DTA model, the day-to-day dynamics model and the R2CFNet control scheme have been investigated through various numerical experiments that show promising results. Lastly, several future topics of relevance to the framework are discussed.
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.