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Bluetooth Data collection technique is recently proven as a reliable data collection technique that provides the opportunity to modify traditional methodologies to improve system performance. Actual volume in the network is a result of the timing plans which are designed and modified based on the volume which is generated using existing timing plans in the system. This interdependency between timing plan and volume on the network is a dynamic process and should be captured to obtain actual traffic states in the network. The current practice is to calculate synthetic origin destination information based on detector volume that doesn't necessarily represent the volume scenario accurately. The data from Bluetooth technology can be utilized to calculate dynamic volume on the network which can be further used as input for signal timing design. Application of dynamic volume improves the system performance by providing the actual volume in system to design optimal timing plans. This thesis proposes a framework that can be used to integrate data obtained from the Bluetooth technology with the traditional methods to design timing plans. The proposed methodology utilizes the origin destination information obtained from Bluetooth data, detector data, characteristics of intersections such as number of lanes, saturation flow rate and existing timing plans as a basis for the calculation of the dynamic volume for the various movements at each intersection. The study shows that using the Bluetooth based OD matrix to calculate accurate dynamic volumes results in better system performance compared to the traditional way of using the static detector volume alone. / Master of Science

IntelliDrive is an initiative developed by United States Department of Transportation (USDOT) that aims to enable safe, interoperable networked wireless communications among vehicles, the infrastructure, and passengers' personal communications devices. IntelliDrive technology has the ability to provide data that would be helpful in enhancement of the existing traffic management applications. IntelliDrive data has attributes that cannot be measured using traditional surveillance technology and which can be used for the development of new traffic management and traveler information applications. The traffic responsive plan selection (TRPS) mode of operation is used in coordinated traffic network to improve the performance of the system. This mode of operation has the ability to implement the best possible timing plan for the existing traffic conditions by switching between timing plans. The data from IntelliDrive technology can be utilized in the traffic responsive mode to improve the system performance by reducing the overall delay in the system. This paper proposes a system that can be used to integrate the data obtained from the IntelliDrive technology to the traffic responsive mode of operation. The proposed method utilizes the number of stops and delay of the vehicles in an intersection as a basis for the implementation of the best timing plan for the prevailing traffic condition. The study shows that using the IntelliDrive based TRPS results in the selection of the traffic plan that minimizes the delay of the system and thus results in better system performance compared to the traditional traffic responsive mechanism. / Master of Science

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures.

This dissertation develops and evaluates a new adaptive traffic signal control system for arterials. This control system is based on reinforcement learning, which is an important research area in distributed artificial intelligence and has been extensively used in many applications including real-time control. In this dissertation, a systematic comparison between the reinforcement learning control methods and existing adaptive traffic control methods is first presented from the theoretical perspective. This comparison shows both the connections between them and the benefits of using reinforcement learning. A Neural-Fuzzy Actor-Critic Reinforcement Learning (NFACRL) method is then introduced for traffic signal control. NFACRL integrates fuzzy logic and neural networks into reinforcement learning and can better handle the curse of dimensionality and generalization problems associated with ordinary reinforcement learning methods. This NFACRL method is first applied to isolated intersection control. Two different implementation schemes are considered. The first scheme uses a fixed phase sequence and variable cycle length, while the second one optimizes phase sequence in real time and is not constrained to the concept of cycle. Both schemes are further extended for arterial control, with each intersection being controlled by one NFACRL controller. Different strategies used for coordinating reinforcement learning controllers are reviewed, and a simple but robust method is adopted for coordinating traffic signals along the arterial. The proposed NFACRL control system is tested at both isolated intersection and arterial levels based on VISSIM simulation. The testing is conducted under different traffic volume scenarios using real-world traffic data collected during morning, noon, and afternoon peak periods. The performance of the NFACRL control system is compared with that of the optimized pre-timed and actuated control. Testing results based on VISSIM simulation show that the proposed NFACRL control has very promising performance. It outperforms optimized pre-timed and actuated control in most cases for both isolated intersection and arterial control. At the end of this dissertation, issues on how to further improve the NFACRL method and implement it in real world are discussed.

With the rapid growth in modern cities and congestion on major freeways and local streets, public transit services have become more and more important for urban transportation. As an important component of Intelligent Transportation Systems (ITS), Transit Signal Priority (TSP) systems have been extensively studied and widely implemented to improve the quality of transit service by reducing transit delay. The focus of this research is on the development of a platform with the physical queue representation that can be employed to evaluate and/or improve TSP strategies with the consideration of the interaction between transit vehicles and queues at the intersection.This dissertation starts with deterministic analyses of TSP systems based on a physical queue model. A request oriented TSP decision process is then developed which incorporates a set of TSP decision regions defined on a time-space diagram with the physical queue representation. These regions help identify the optimal detector location, select the appropriate priority control strategy, and handle the situations with multiple priority requests. In order to handle uncertainties in TSP systems arising in bus travel time and dwell time estimation, a type-2 fuzzy logic forecasting system is presented and tested with field data. Type-2 fuzzy logic is very powerful in dealing with uncertainty. The use of Type-2 fuzzy logic helps improve the performance of TSP systems. The last component of the dissertation is the development of a Colored Petri Net (CPN) model for TSP systems. With CPN tools, computer simulation can be performed to evaluate various TSP control strategies and the decision process. Examples for demonstrating the process of implementing the green extension strategy and the proposed TSP decision process are presented in the dissertation. The CPN model can also serve as an interface between the platform developed in this dissertation and the implementation of the control strategies at the controller level.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The fully actuated signal control detects the pedestrian density using sensors and, according to that, it prioritizes pedestrians crossing. One major problem, worldwide, is using fixed time traffic light as a traffic regulator at intersections with high pedestrian and vehicular volume. Lima is no exception, continuing to use this kind of traffic lights completely harms pedestrian accessibility, it increases their waiting and crossing times, it also affects road safety and service levels at the structures. The proposal on this article is to design a fully actuated signal control using logical controls that are able to perceive the pedestrian density on the refuge islands, making everything more accessible. In order to do this, a study to identify the pedestrian and vehicle volume was conducted on the Lima Panamerican highway. There was a total of 7506 pedestrians during rush hour, proving there is a large amount of people at the intersection at that time. Thereby, by using the VisVap module of the Vissim, the study managed to simulate and validate the priority control required. All in all, the results showed a remarkable improvement, the pedestrian crossing time was reduced by 6.84% and the service level of the intersection went from E to D.

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Traffic responsive mode of operation with its two mechanisms, threshold-based and pattern matching, is considered one of the effective and efficient signal control modes. This operation mode is underutilized due to its cumbersome configuration procedure. The research presented in this thesis aims to give some guidelines regarding traffic responsive and issues that might improve the system performance. Four different issues related to traffic responsive are considered: The first issue is the generation of different traffic scenarios that drive the design of the system. This point is not limited to traffic responsive only but it is more general for different traffic engineering applications that need different traffic scenarios. The second issue is presenting an approach to implement traffic responsive control mode of operation in a large arterial network in Northern Virginia. Pattern matching mechanism is used for this application. Compared to time-of-day control mode, traffic responsive control saves up to 26.94% of the average delay and 21.13% of average number of stops for Reston Parkway network. The third issue is an attempt to improve the current threshold mechanism by relaxing the threshold constraints and using variable thresholds for different levels of plan selection parameters. The last issue is a study for the pedestrian effect on the performance of networks operating by traffic responsive control. The effects of pedestrian calls and pedestrian phases on traffic responsive control are compared and the results shows that pedestrian calls are better for low pedestrian volumes while pedestrian phases are better for high pedestrian volumes. / Master of Science