Cities worldwide are faced with the challenge of improving transit service in urban areas using lower cost means. Transit signal priority is considered to be one of the most effective ways to improve the service of transit vehicles. Transit signal priority has become a very popular topic in transportation in the past 20 to 30 years and it has been implemented in many places around the world. In this thesis, transit signal priority strategies are categorized and an extensive literature review on past research on transit signal priority is conducted. Then a case study on Columbia Pike in Arlington (including 21 signalized intersections) is conducted to assess the impacts of integrating transit signal priority and SCOOT adaptive signal control. At the end of this thesis, an isolated intersection is designed to analyze the sensitivity of major parameters on performance of the network and transit vehicles. The results of this study indicate that the prioritized vehicles usually benefit from any priority scheme considered. During the peak period, the simulations clearly indicate that these benefits are typically obtained at the expense of the general traffic. While buses experience reductions in delay, stops, fuel consumption, and emissions, the opposite typically occurs for the general traffic. Furthermore, since usually there are significantly more cars than buses, the negative impacts experienced by the general traffic during this period outweigh in most cases the benefits to the transit vehicles, thus yielding overall negative impacts for the various priority schemes considered. For the off-peak period, there are no apparent negative impacts, as there is more spare capacity to accommodate approaching transit vehicles at signalized intersections without significantly disrupting traffic operations. It is also shown in this study that it is generally difficult to improve the system-wide performance by using transit priority when the signal is already optimized according to generally accepted traffic flow criteria. In this study it is also observed that the system-wide performance decreases rapidly when transit dwell time gets longer. / Master of Science

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 / Traffic signal timings are traditionally designed to reduce vehicle congestion at an intersection. This thesis is based on two studies that develop new formulations to compute the most efficient signal cycle lengths of intersections, considering vehicle fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model, a model that is traditionally used in traffic signal design. Simulations were run to determine the intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NO_x), and carbon dioxide (CO₂) of the study intersections. To account for the random nature of traffic, each simulation scenario was run twenty different times. The cycle lengths that minimized the noted simulation outputs were then used to develop the proposed formulations. The new formulations demonstrated its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths designed to minimize vehicle congestion. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle traffic.

The growth of serious injuries and fatalities resulting from traffic accidents at intersections is one of the main problems in urban areas. Signal control was proposed as an alternative intersection design on rural roads. There were many reasons behind this, the most outstanding of which was the traffic signals can be used as a cost effective tools for traffic management in urban areas. The LHOVRA technique was intended to improve safety and reduce lost time at signalized intersection along high speed roads. The LHOVRA technique is an isolated traffic control strategy in Sweden which is formed from different concepts. This thesis work is aimed to evaluate the LHOVRA technique with a focus on the O-function. Hence, two different scenarios, one with O-function and one without O-function were implemented in the micro traffic simulation software, VISSIM. VISSIM has been used to simulate the traffic situation of the Gamla Övägen – Albrektsvägen intersection by considering the LHOVRA scenario (with O-function) as well as traditional scenario (without O-function) of the intersection. Field measurements were used as input data for VISSIM simulation. The VISSIM simulation model was calibrated to find a close match between simulated and real data. Finally, a comparison of alternatives was carried out based on traffic performance and traffic safety measurements. The simulation experiment results gained by the comparisons were presented a higher time-to-collision value. The higher time-to-collision value the safer situation is. Both delays and travel time were reduced to primary road traffic.

New dynamic signal control methods such as fuzzy logic and artificial intelligence developed recently mainly focused on isolated intersection. In this study, a Fuzzy Logic Control for a Diamond Interchange incorporating with Fuzzy Ramp System (FLDI) has been developed. The signalization of two closely spaced intersections in a diamond interchange is a complicated problem that includes both increasing the diamond interchange capacity and reduce delays at the same time. The model comprises of three main modules. The Fuzzy Phase Timing module controls the current phase green time extension, the Phase Selection module select the next phase based on the pre-defined phase sequence or phase logics and the Fuzzy Ramp module determines the cycle time of the ramp meter bases on current traffic volumes and conditions of the interchanges and the motorways. The developed FLDI model has been compared with the traffic actuated simulation with respects to flow rates and the average delays of the vehicles. The model of an actual diamond interchange is described and simulated by using AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Network) software. Simulation results show the FLDI model outperformed the traffic actuated models with lower system total travel time, average delay and improvements in downstream average speed and average delay.

No / Selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task, which requires significant expert knowledge and experience. Also, the application of a control action for solving a local traffic problem could create traffic congestion at different locations in the network because of the strong interrelations between traffic situations at different locations of a road network. Therefore, coordination of control strategies is required to make sure that all available control actions serve the same objective. In this paper, an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach is proposed to assist the human operator of a road traffic control centre to manage the current traffic state. In the proposed system, the network is divided into sub-networks, each of which has its own associated agent. The agent of the sub-network with an incident reacts with other affected agents in order to select the optimal traffic control action, so that a globally acceptable solution is found. The agent uses an effective way of calculating the control action fitness locally and globally. The capability of the proposed ITCS has been tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show its ability to identify the optimal global control action. (C) 2012 Elsevier B.V. All rights reserved.

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.

Traffic signals are very cost effective tools for urban traffic management in urban areas. The number of intersections in Sweden controlled by traffic signals has increased since the seventies, but efforts to study the traffic performance of the employed strategies are still lacking. The LHOVRA technique is the predominant isolated traffic signal control strategy in Sweden. Past-end green was originally incorporated as part of LHOVRA (the “O” function) and was intended to reduce the number of vehicles in the dilemma zone. Coordinated signal control in Sweden is often fixed-time with local vehicle actuated signal timing adjustments and bus priority. This research study was undertaken to increase the knowledge of the traffic performance impacts of these strategies. The aim was to evaluate the following control strategies using Stockholm as a case study: 1. The LHOVRA technique with a focus on the “O” function; 2. Fixed time coordination (FTC); 3. Fixed time coordination with local signal timing adjustment (FTC-LTA); 4. FTC-LTA as above + active bus priority (PRIBUSS); 5. Self-optimizing control (SPOT). Field measurements were used for study of driver behavior and traffic impacts as well as for collecting input data needs for simulation. The results from low speed approaches showed a higher proportion of stopped vehicles after receiving green extension. Moving the detectors closer to the stop line, and/or making the detectors speed dependent were suggested as measures to solve these problems. The VISSIM simulation model calibrated and validated with empirical data was used to study traffic performance and safety impacts of the LHOVRA technique as well as to test the suggested improvements. The simulation experiment results from these design changes were shown to reduce accident risk with little or no loss of traffic performance. TRANSYT was used to produce optimized fixed signal timings for coordinated intersections. HUTSIM simulations showed that local signal timing adjustment by means of past-end green was beneficial when applied to coordinated traffic signal control in the study area. Both delays and stops were reduced, although not for the main, critical intersection which operated close to capacity. To study the impacts of strategies for coordinated signal control with bus priority, extensive field data collection was undertaken during separate time periods with these strategies in the same area using mobile and stationary techniques. A method to calculate the approach delay was developed based on the observed number of queuing vehicles at the start and end of green. Compared to FTC-LTA, the study showed that PRIBUSS reduced bus travel time. SPOT reduced both bus and vehicle travel time. Future research efforts for the development of signal control strategies and their implementation in Sweden should be focused on strategies with self-optimization functionality. / QC 20100408

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.

Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent optimal sequence of actions that minimizes a certain long-term performance criterion. The standard dynamic programming approach to solve an MDP for the optimal decisions requires a complete model of the MDP and is computationally feasible only for small state-action MDPs. Reinforcement learning (RL) methods, on the other hand, are model-free simulation based approaches for solving MDPs. In many real world applications, one is often faced with MDPs that have large state-action spaces whose model is unknown, however, whose outcomes can be simulated. In order to solve such (large) MDPs, one either resorts to the technique of function approximation in conjunction with RL methods or develops application specific RL methods. A solution based on RL methods with function approximation comes with the associated problem of choosing the right features for approximation and a solution based on application specific RL methods primarily relies on utilizing the problem structure. In this thesis, we investigate the problem of choosing the right features for RL methods based on function approximation as well as develop novel RL algorithms that adaptively obtain best features for approximation. Subsequently, we also develop problem specie RL methods for applications arising in the areas of wireless sensor networks and road traffic control. In the first part of the thesis, we consider the problem of finding the best features for value function approximation in reinforcement learning for the long-run discounted cost objective. We quantify the error in the approximation for any given feature and the approximation parameter by the mean square Bellman error (MSBE) objective and develop an online algorithm to optimize MSBE. Subsequently, we propose the first online actor-critic scheme with adaptive bases to find a locally optimal (control) policy for an MDP under the weighted discounted cost objective. The actor performs gradient search in the space of policy parameters using simultaneous perturbation stochastic approximation (SPSA) gradient estimates. This gradient computation however requires estimates of the value function of the policy. The value function is approximated using a linear architecture and its estimate is obtained from the critic. The error in approximation of the value function, however, results in sub-optimal policies. Thus, we obtain the best features by performing a gradient descent on the Grassmannian of features to minimize a MSBE objective. We provide a proof of convergence of our control algorithm to a locally optimal policy and show numerical results illustrating the performance of our algorithm. In our next work, we develop an online actor-critic control algorithm with adaptive feature tuning for MDPs under the long-run average cost objective. In this setting, a gradient search in the policy parameters is performed using policy gradient estimates to improve the performance of the actor. The computation of the aforementioned gradient however requires estimates of the differential value function of the policy. In order to obtain good estimates of the differential value function, the critic adaptively tunes the features to obtain the best representation of the value function using gradient search in the Grassmannian of features. We prove that our actor-critic algorithm converges to a locally optimal policy. Experiments on two different MDP settings show performance improvements resulting from our feature adaptation scheme. In the second part of the thesis, we develop problem specific RL solution methods for the two aforementioned applications. In both the applications, the size of the state-action space in the formulated MDPs is large. However, by utilizing the problem structure we develop scalable RL algorithms. In the wireless sensor networks application, we develop RL algorithms to find optimal energy management policies (EMPs) for energy harvesting (EH) sensor nodes. First, we consider the case of a single EH sensor node and formulate the problem of finding an optimal EMP in the discounted cost MDP setting. We then propose two RL algorithms to maximize network performance. Through simulations, our algorithms are seen to outperform the algorithms in the literature. Our RL algorithms for the single EH sensor node do not scale when there are multiple sensor nodes. In our second work, we consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the -greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through numerical experiments, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method. In the context of road traffic control, optimal control of traffic lights at junctions or traffic signal control (TSC) is essential for reducing the average delay experienced by the road users. This problem is hard to solve when simultaneously considering all the junctions in the road network. So, we propose a decentralized multi-agent reinforcement learning (MARL) algorithm for solving this problem by considering each junction in the road network as a separate agent (controller) to obtain dynamic TSC policies. We propose two approaches to minimize the average delay. In the first approach, each agent decides the signal duration of its phases in a round-robin (RR) manner using the multi-agent Q-learning algorithm. We show through simulations over VISSIM (microscopic traffic simulator) that our round-robin MARL algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm over two real road networks. In the second approach, instead of optimizing green light duration, each agent optimizes the order of the phase sequence. We then employ our MARL algorithms by suitably changing the state-action space and cost structure of the MDP. We show through simulations over VISSIM that our non-round robin MARL algorithms perform significantly better than the FST, SAT and the round-robin MARL algorithms based on the first approach. However, on the other hand, our round-robin MARL algorithms are more practically viable as they conform with the psychology of road users.