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

Traffic congestion has become a menace to civilized society. It degrades air quality, jeopardizes safety and causes delay. Traffic congestion can be alleviated by providing an effective traffic control signal system. Closed-loop traffic control systems are an example of such a system. Closed-loop traffic control systems can be operated primarily in either of two modes: Time of Day Mode (TOD) or Traffic Responsive Plan Selection Mode (TRPS). TRPS mode, if properly configured, can easily handle time independent variation in traffic volumes. It can also reduce the effect of timing plan aging. Despite these advantages, TRPS mode is not used as frequently as TOD mode. The reason being a lack of methodologies and formal guidelines for predicting the factors and thresholds associated with TRPS mode. In this research, a new methodology is developed for determining the thresholds and factors associated with the TRPS mode. This methodology, when tested on a closed-loop system in Odem, Texas, produced a classification accuracy of 94%. The classification accuracy can be increased to 98% with a proposed TRPS architecture.

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

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.