<p>Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the United States. It is therefore important for agencies to systematically monitor signal performance to identify locations where operations do not function as desired and where mobility could be improved.</p> <p><br></p> <p>Currently, most signal performance evaluations are derived from infrastructure-based Automated Traffic Signal Performance Measures (ATSPMs). These performance measures rely on high-resolution detector and phase information that is collected at 10 Hz and reported via TCP/IP connections. Even though ATSPMs have proven to be a valid approach to estimate signal performance, significant initial capital investment required for infrastructure deployment can represent an obstacle for agencies attempting to scale these techniques. Further, fixed vehicle detection zones can create challenges in the accuracy and extent of the calculated performance measures.</p> <p><br></p> <p>High-resolution connected vehicle (CV) trajectory data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, this data set provides unique opportunities to derive accurate signal performance measures without the need for infrastructure upgrades. This dissertation provides a comprehensive suite of CV-based techniques to generate actionable and scalable traffic signal performance measures.</p> <p><br></p> <p>Turning movements of vehicles at intersections are automatically identified from attributes included in the commercial CV data set to facilitate movement-level analyses. Then, a trajectory-based visualization from which relevant performance measures can be extracted is presented. Subsequently, methodologies to identify signal retiming opportunities are discussed. An approach to evaluate closely-coupled intersections, which is particularly challenging with detector-based techniques, is then presented. Finally, a data-driven methodology to enhance the scalability of trajectory-based traffic signal performance estimations by automatically mapping relevant intersection geometry components is provided.</p> <p><br></p> <p>The trajectory data processing procedures provided in this dissertation can aid agencies make data-driven decisions on resource allocation and signal system modifications. The presented techniques are transferable to any location where CV data is available, and the scope of analysis can be easily varied from the movement to intersection, corridor, region, and state level.</p>

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Advances in microprocessor and communications technologies are making it possible to deploy advanced traffic signal controllers capable of integrating emergency vehicle preemption and transit priority operations. However, investment planning for such an integrated system is not a trivial task. Investment planning for such a system requires a holistic approach that considers institutional, technical and financial issues from a systems perspective. Two distinct service providers, fire and rescue providers and transit operators, with separate operational functions, objectives, resources and constituents are involved. Performance parameters for the integrated system are not well defined and performance data are often imprecise in nature. Transportation planners and managers interested in deploying integrated emergency vehicle preemption and traffic priority systems do not have an evaluation approach or a common set of performance metrics to make an informed decision. There is a need for a simple structured analytical approach and tools to assess the impacts of an integrated emergency vehicle preemption and transit priority system as part of investment decision making processes. This need could be met with the assistance of a decision support system (DSS) developed to provide planners and managers a simple and intuitive analytical approach to assist in making investment decisions regarding emergency vehicle preemption and transit signal priority. This dissertation has two research goals: (1) to develop a decision support system framework to assess the impacts of advanced traffic signal control systems capable of integrating emergency vehicle preemption and transit signal priority operations for investment planning purposes; and (2) to develop selected analytical tools for incorporation into the decision support system framework. These analytical tools will employ fuzzy sets theory concepts, as well as cost and accident reduction factors. As part of this research, analytical tools to assess impacts on operating cost for transit and fire and rescue providers have been developed. In addition, an analytical tool was developed and employs fuzzy multi-attribute decision making methods to rank alternative transit priority strategies. These analytical tools are proposed for incorporation into the design of a decision support system in the future. / Ph. D.

Modern traffic signal control systems have not changed significantly in the past 40-50 years. The most widely applied traffic signal control systems are still time-of-day, coordinated-actuated system, since many existing advanced adaptive signal control systems are too complicated and fathomless for most of people. Recent advances in communications standards and technologies provide the basis for significant improvements in traffic signal control capabilities. In the United States, the IntelliDriveSM program (originally called Vehicle Infrastructure Integration - VII) has identified 5.9GHz Digital Short Range Communications (DSRC) as the primary communications mode for vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v2i) safety based applications, denoted as v2x. The ability for vehicles and the infrastructure to communication information is a significant advance over the current system capability of point presence and passage detection that is used in traffic control systems. Given enriched data from IntelliDriveSM, the problem of traffic control can be solved in an innovative data-driven and mathematical way to produce robust and optimal outputs.In this doctoral research, three different problems within a v2x environment- "enhanced pseudo-lane-level vehicle positioning", "robust coordinated-actuated multiple priority control", and "multimodal platoon-based arterial traffic signal control", are addressed with statistical techniques and mathematical programming.First, a pseudo-lane-level GPS positioning system is proposed based on an IntelliDriveSM v2x environment. GPS errors can be categorized into common-mode errors and noncommon-mode errors, where common-mode errors can be mitigated by differential GPS (DGPS) but noncommon-mode cannot. Common-mode GPS errors are cancelled using differential corrections broadcast from the road-side equipment (RSE). With v2i communication, a high fidelity roadway layout map (called MAP in the SAE J2735 standard) and satellite pseudo-range corrections are broadcast by the RSE. To enhance and correct lane level positioning of a vehicle, a statistical process control approach is used to detect significant vehicle driving events such as turning at an intersection or lane-changing. Whenever a turn event is detected, a mathematical program is solved to estimate and update the GPS noncommon-mode errors. Overall the GPS errors are reduced by corrections to both common-mode and noncommon-mode errors.Second, an analytical mathematical model, a mixed-integer linear program (MILP), is developed to provide robust real-time multiple priority control, assuming penetration of IntelliDriveSM is limited to emergency vehicles and transit vehicles. This is believed to be the first mathematical formulation which accommodates advanced features of modern traffic controllers, such as green extension and vehicle actuations, to provide flexibility in implementation of optimal signal plans. Signal coordination between adjacent signals is addressed by virtual coordination requests which behave significantly different than the current coordination control in a coordinated-actuated controller. The proposed new coordination method can handle both priority and coordination together to reduce and balance delays for buses and automobiles with real-time optimized solutions.The robust multiple priority control problem was simplified as a polynomial cut problem with some reasonable assumptions and applied on a real-world intersection at Southern Ave. & 67 Ave. in Phoenix, AZ on February 22, 2010 and March 10, 2010. The roadside equipment (RSE) was installed in the traffic signal control cabinet and connected with a live traffic signal controller via Ethernet. With the support of Maricopa County's Regional Emergency Action Coordinating (REACT) team, three REACT vehicles were equipped with onboard equipments (OBE). Different priority scenarios were tested including concurrent requests, conflicting requests, and mixed requests. The experiments showed that the traffic controller was able to perform desirably under each scenario.Finally, a unified platoon-based mathematical formulation called PAMSCOD is presented to perform online arterial (network) traffic signal control while considering multiple travel modes in the IntelliDriveSM environment with high market penetration, including passenger vehicles. First, a hierarchical platoon recognition algorithm is proposed to identify platoons in real-time. This algorithm can output the number of platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine the future optimal signal plans based on the real-time platoon data (and the platoon request for service) and current traffic controller status. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the real-time platoon information. The integer feasible solution region is enhanced in order to reduce the solution times by assuming a first-come, first-serve discipline for the platoon requests on the same approach. Microscopic online simulation in VISSIM shows that PAMSCOD can easily handle two traffic modes including buses and automobiles jointly and significantly reduce delays for both modes, compared with SYNCHRO optimized plans.

Le transport a toujours été l'un des composants déterminants de la vie urbaine et de son développement économique. A partir de la seconde moitié du siècle dernier, l'amélioration du niveau de vie moyen et du taux d'équipement des ménages a permis au plus grand nombre d'accéder au déplacement par véhicule particulier. Nous avons donc assisté à une course entre la croissance du trafic routier et les progrès quantitatifs et qualitatifs de la voirie. Cette quantité d'actions génère des problèmes au niveau de la fluidité du trafic, d'où l'apparition de congestion.La congestion se produit aujourd'hui de façon quasi-quotidienne dans les réseaux routiers. Elle est source de perte de temps, augmentation de la consommation d'énergie, nuisance et détérioration de l'environnement. La solution aux problèmes de congestion routière ne passe pas toujours par l'augmentation de l'investissement dans les infrastructures de transport. En effet, l'offre de terrains est épuisée et le développement de l'infrastructure routière est coûteux. D'où, la tendance actuelle est plutôt à une meilleure utilisation des infrastructures existantes. En particulier, les feux de signalisation jouent un rôle important parmi les approches qui permettent d'éviter la congestion. En effet, la conception d'une meilleur commande des feux de signalisation a fait l'objet de plusieurs recherches afin d’améliorer la circulation au niveau du réseau à grande échelle.Dans ce mémoire, nous nous intéressons essentiellement à un travail en amont (action a priori) permettant d'éviter la congestion en forçant le nombre de véhicules à ne pas dépasser les capacités maximales des voies du réseau de transport. Après avoir décrire les réseaux de carrefours des feux, nous présentons d'une manière non exhaustive, les méthodes développées pour la gestion et la régulation des carrefours. Ensuite, nous proposons trois stratégies de contrôle qui traitent le problème de contrôle de manières différentes. La première fait appel à la théorie des systèmes dissipatifs, la deuxième consiste à stabiliser le système au sens de Lyapunov autour de sa situation nominale et la troisième le stabilise en temps fini (pendant les heures de pointe). Ces commandes proposées respectent les contraintes sur l'état et sur la commande et prennent en considération les incertitudes existantes dans le système. Finalement, l'existence des commandes proposées a été caractérisée par la faisabilité de certaines LMI en utilisant l'outil CVX sous MATLAB. De plus, les performances de chaque commande sont évaluées par des simulations. / Transport has always been one of the key components of urban life and its economic development. From the second half of the last century, the improvement in the average standard of living and the household equipment rate allowed the greatest number of people to access the journey by private vehicle. We therefore witnessed a race between the growth of road traffic and the quantitative and qualitative progress of roads. This quantity of actions generates problems with the fluidity of the traffic, hence the appearance of congestion.The congestion occurs today almost daily in road networks. It is source of waste of time, increase of the energy consumption, the nuisance and the deterioration of the environment. The solution to the problems of road congestion does not still pass by the increase of the investment in the infrastructures of transport. Indeed, the offer of grounds is exhausted and the development of the road infrastructure is expensive. Hence, the current trend is rather for a better use of the existing infrastructures. In particular, traffic lights play an important role in avoiding congestion. Indeed, the design of a better control of traffic lights has been the subject of several researches in order to improve the network circulation on a large scale.In this thesis, we are mainly interested in a work that prevents the congestion by forcing the number of vehicles to not exceed the lane capacities. After having described the network of intersections, we have realized a state of the art on the methods developed for the management and regulation of intersections. Next, we propose three control strategies that treat the control problem in different ways. The first one involves the theory of dissipative systems, the second one is to stabilize the system in the sense of Lyapunov around its nominal situation and the third one stabilizes it in finite time (during peak hours). These proposed controls respect the constraints on both state and control. In addition, they take into account the uncertainties in the system. Finally, the result of each strategy developed is presented by LMI in order to be solved by using the CVX tool under MATLAB. Besides, the performance of each control is evaluated by simulations.

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.

The thesis considers the effects of vehicle type on highway traffic control. The effects of vehicle type on the capacity of traffic signal approaches are examined by the experimental determination of passenger car units at intersections in London and West Yorkshire and in addition saturation flows and lost times are examined. Vehicle type effect at roundabout entries are investigated and the results of field observations reported. Details are given of the gap acceptance of varying vehicle types, the effect of vehicle type on delay and comparisons are made with existing recommendations for the capacity design of roundabout entries. Observations of traffic flow on a rural motorway are used to demonstrate the effect of vehicle type on speed and observed values are fitted to a normal distribution. Overtaking behaviour is also examined and conclusions drawn of the relative effect on capacity of vehicle type. A review is given of the effects of vehicle type on the design and operation of the highway system in Saudi Arabia.

In this paper, a new cyclic structure of a max pressure travel time-based traffic signal control is developed to seek an optimal coordination in large-scale urban networks. The focus of the current paper is on dynamic manipulation of cycle lengths within cyclic structure. Following the application of a decentralized approach, which requires only local information in order to offer proper phase durations, the control strategy aims at maximizing the overall network throughput. Previous works of cyclic max-pressure control have presented a cyclic notion to actuate the controller in a cyclic manner. However, no input has been provided on the optimal cycle length for each intersection to be chosen in a network, and along with the dynamic and stochastic nature of the trips, it is not clear what are the main phases of the intersections and how to coordinate them. The developed cyclic max pressure control schemes are compared with an exiting cyclic scheme in the literature. Simulation results show that the newly proposed cyclic structure of the time-based approach offers better decision-making.

The t h e s i s c o n s i d e r s t h e e f f e c t s of v e h i c l e type on highway t r a f f i c flow. The e f f e c t s of v e h i c l e type on t h e c a p a c i t y of t r a f f i c s i g n a l approaches are examined by t h e experimental d e t e r m i n a t i o n of passenger c a r u n i t s a t i n t e r s e c t i o n s i n London and West Yorkshire and i n a d d i t i o n s a t u r a t i o n flows and lost t i m e s a r e examined. . Vehicle type e f f e c t s a t roundabout e n t r i e s a r e i n v e s t i g a t e d and t h e r e s u l t s of f i e l d o b s e r v a t i o n s r e p o r t e d . Details a r e given of t h e gap acceptance of varying v e h i c l e t y p e s , t h e e f f e c t of v e h i c l e type on delay and comparisons a r e made with e x i s t i n g recommendat i o n s f o r t h e c a p a c i t y design of roundabout e n t r i e s . Observations of t r a f f i c flow on a r u r a l motorway a r e used to demonstrate t h e e f f e c t of v e h i c l e type on speed and observed v a l u e s a r e f i t t e d t o a normal d i s t r i b u t i o n . Overtaking behaviour is a l s o examined and conclusions drawn of t h e r e l a t i v e e f f e c t on c a p a c i t y of v e h i c l e t y p e . A review is given of t h e e f f e c t s of v e h i c l e type on t h e design and o p e r a t i o n of t h e highway system in Saudi Arabia.

A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated may be either abstract quantities such as time or concrete quantities such as manpower. The sequential decision making setting involves one or more agents interacting with an environment to procure rewards at every time instant and the goal is to find an optimal policy for choosing actions. Most of these problems involve multiple (infinite) stages and the objective function is usually a long-run performance objective. The problem is further complicated by the uncertainties in the sys-tem, for instance, the stochastic noise and partial observability in a single-agent setting or private information of the agents in a multi-agent setting. The dimensionality of the problem also plays an important role in the solution methodology adopted. Most of the real-world problems involve high-dimensional state and action spaces and an important design aspect of the solution is the choice of knowledge representation. The aim of this thesis is to answer important resource allocation related questions in different real-world application contexts and in the process contribute novel algorithms to the theory as well. The resource allocation algorithms considered include those from stochastic optimization, stochastic control and reinforcement learning. A number of new algorithms are developed as well. The application contexts selected encompass both single and multi-agent systems, abstract and concrete resources and contain high-dimensional state and control spaces. The empirical results from the various studies performed indicate that the algorithms presented here perform significantly better than those previously proposed in the literature. Further, the algorithms presented here are also shown to theoretically converge, hence guaranteeing optimal performance. We now briefly describe the various studies conducted here to investigate problems of resource allocation under uncertainties of different kinds: Vehicular Traffic Control The aim here is to optimize the ‘green time’ resource of the individual lanes in road networks that maximizes a certain long-term performance objective. We develop several reinforcement learning based algorithms for solving this problem. In the infinite horizon discounted Markov decision process setting, a Q-learning based traffic light control (TLC) algorithm that incorporates feature based representations and function approximation to handle large road networks is proposed, see Prashanth and Bhatnagar [2011b]. This TLC algorithm works with coarse information, obtained via graded thresholds, about the congestion level on the lanes of the road network. However, the graded threshold values used in the above Q-learning based TLC algorithm as well as several other graded threshold-based TLC algorithms that we propose, may not be optimal for all traffic conditions. We therefore also develop a new algorithm based on SPSA to tune the associated thresholds to the ‘optimal’ values (Prashanth and Bhatnagar [2012]). Our thresh-old tuning algorithm is online, incremental with proven convergence to the optimal values of thresholds. Further, we also study average cost traffic signal control and develop two novel reinforcement learning based TLC algorithms with function approximation (Prashanth and Bhatnagar [2011c]). Lastly, we also develop a feature adaptation method for ‘optimal’ feature selection (Bhatnagar et al. [2012a]). This algorithm adapts the features in a way as to converge to an optimal set of features, which can then be used in the algorithm. Service Systems The aim here is to optimize the ‘workforce’, the critical resource of any service system. However, adapting the staffing levels to the workloads in such systems is nontrivial as the queue stability and aggregate service level agreement (SLA) constraints have to be complied with. We formulate this problem as a constrained hidden Markov process with a (discrete) worker parameter and propose simultaneous perturbation based simulation optimization algorithms for this purpose. The algorithms include both first order as well as second order methods and incorporate SPSA based gradient estimates in the primal, with dual ascent for the Lagrange multipliers. All the algorithms that we propose are online, incremental and are easy to implement. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter updates obtained from the SASOC algorithms onto the discrete set. We validate our algorithms on five real-life service systems and compare their performance with a state-of-the-art optimization tool-kit OptQuest. Being ��times faster than OptQuest, our scheme is particularly suitable for adaptive labor staffing. Also, we observe that it guarantees convergence and finds better solutions than OptQuest in many cases. Wireless Sensor Networks The aim here is to allocate the ‘sleep time’ (resource) of the individual sensors in an intrusion detection application such that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We model this sleep–wake scheduling problem as a partially-observed Markov decision process (POMDP) and propose novel RL-based algorithms -with both long-run discounted and average cost objectives -for solving this problem. All our algorithms incorporate function approximation and feature-based representations to handle the curse of dimensionality. Further, the feature selection scheme used in each of the proposed algorithms intelligently manages the energy cost and tracking cost factors, which in turn, assists the search for the optimal sleeping policy. The results from the simulation experiments suggest that our proposed algorithms perform better than a recently proposed algorithm from Fuemmeler and Veeravalli [2008], Fuemmeler et al. [2011]. Mechanism Design The setting here is of multiple self-interested agents with limited capacities, attempting to maximize their individual utilities, which often comes at the expense of the group’s utility. The aim of the resource allocator here then is to efficiently allocate the resource (which is being contended for, by the agents) and also maximize the social welfare via the ‘right’ transfer of payments. In other words, the problem is to find an incentive compatible transfer scheme following a socially efficient allocation. We present two novel mechanisms with progressively realistic assumptions about agent types aimed at economic scenarios where agents have limited capacities. For the simplest case where agent types consist of a unit cost of production and a capacity that does not change with time, we provide an enhancement to the static mechanism of Dash et al. [2007] that effectively deters misreport of the capacity type element by an agent to receive an allocation beyond its capacity, which thereby damages other agents. Our model incorporates an agent’s preference to harm other agents through a additive factor in the utility function of an agent and the mechanism we propose achieves strategy proofness by means of a novel penalty scheme. Next, we consider a dynamic setting where agent types evolve and the individual agents here again have a preference to harm others via capacity misreports. We show via a counterexample that the dynamic pivot mechanism of Bergemann and Valimaki [2010] cannot be directly applied in our setting with capacity-limited alim¨agents. We propose an enhancement to the mechanism of Bergemann and V¨alim¨aki [2010] that ensures truth telling w.r.t. capacity type element through a variable penalty scheme (in the spirit of the static mechanism). We show that each of our mechanisms is ex-post incentive compatible, ex-post individually rational, and socially efficient