Traffic signal control using queueing theory

Traffic signal control has drawn considerable attention in the literatures thanks to its ability to improve the mobility of urban networks. Queueing models are capable of capturing performance or effectiveness of a queueing system. In this report, SOCPs (second order cone program) are proposed based on different queueing models as pre-timed signal control techniques to minimize total travel delay. Stochastic programs are developed in order to handle the uncertainties in the arrival rates. In addition, the superiority of the proposed model over Webster’s model has been validated in a microscopic traffic simulation software named CORSIM.Statistic

Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

TRA-950: A DYNAMIC PROGRAMMING APPROACH FOR ARTERIAL SIGNAL OPTIMIZATION IN A CONNECTED VEHICLE ENVIRONMENT

Within the Connected Vehicle (CV) environment, vehicles are able to communicate with each other and with infrastructure via wireless communication technology. The collected data from CVs provide a much more complete picture of the arterial traffic states and can be utilized for signal control. Based on the real-time traffic information from CVs, this paper enhances an arterial traffic flow model for arterial signal optimization. Then a dynamic programming optimization model is created to solve the signal optimization application. A real-world arterial corridor is modeled in VISSIM to validate the algorithms. This approach is shown to generate good results and may be superior to well-tuned fixed-time control