Development of Adaptive Signal Control (ASC) Based on Automatic Vehicle Location (AVL) System and Its Applications

Download or read book Development of Adaptive Signal Control (ASC) Based on Automatic Vehicle Location (AVL) System and Its Applications written by Guoyuan Wu and published by . This book was released on 2010 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growth of population and increase of travelling requirements in metropolitan areas, public transit has been recognized as a promising remedy and is playing an ever more important role in sustainable transportation systems. However, the development of the public transit system has not received enough attention until the recent emergence of Bus Rapid Transit (BRT). In the conventional public transit system, little to no communication passes between transit vehicles and the roadside infrastructure, such as traffic signals and loop detectors. But now, thanks to advancements in automatic vehicle location (AVL) systems and wireless communication, real-time and high-resolution information of the movement of transit vehicles has become available, which may potentially facilitate the development of more advanced traffic control and management systems. This dissertation introduces a novel adaptive traffic signal control system, which utilizes the real-time location information of transit vehicles. By predicting the movement of the transit vehicle based on continuous detection of the vehicle motion by the on-board AVL system and estimating the measures of effectiveness (MOE) of other motor vehicles based on the surveillance of traffic conditions, optimal signal timings can be obtained by solving the proposed traffic signal optimization models. Both numerical analysis and simulation tests demonstrate that the proposed system improves a transit vehicle's operation as well as minimizes its negative impacts on other motor vehicles in the traffic system. In summary, there are three major contributions of this dissertation: a) development of a novel AVL-based adaptive traffic signal control system; b) modeling of the associated traffic signal timing optimization problem, which is the key component of the proposed system; c) applications of the proposed system to two real world cases. After presenting background knowledge on two major types of transit operations, i.e., preemption and priority, traffic signal control and AVL systems, the architecture of the proposed adaptive signal control system and the associated algorithm are presented. The proposed system includes a data-base, fleet equipped with surveillance system, traffic signal controllers, a transit movement predictor, a traffic signal timing optimizer and a request server. The mixed integer quadratic programming (MIQP) and nonlinear programming (NP) are used to formulate signal timing optimization problems. Then the proposed system and algorithm are applied to two real-world case studies. The first case study concerns the SPRINTER rail transit service. The proposed adaptive signal control (ASC) system is developed to relieve the traffic congestion and to clear the accumulated vehicle queues at the isolated signal around the grade crossing, based on the location information on SPRINTER from PATH-developed cellular GPS trackers. The second case study involves the San Diego trolley system. With the information provided by the AVL system, the proposed ASC system predicts the arrival times of the instrumented trolley at signals and provides the corresponding optimal signal timings to improve the schedule adherence, thus reducing the delays at intersections and enhancing the trip reliability for the trolley travelling along a signalized corridor in the downtown area under the priority operation. The negative impact (e.g., delay increase) on other traffic is minimized simultaneously. Both numerical analysis and simulation tests in the microscopic environment are conducted using the PARAMICS software to validate the proposed system for the aforementioned applications. The results present a promising future for further field operational testing.