Traffic responsive intersection control algorithm using GPS data

Abstract

This paper reports on the performance of signalised intersection control using vehicle GPS information compared to fixed-time and inductive loop based control. Traffic congestion forecasts estimate an increase of about 60% in 2030. At present, poor choice of signal timings by isolated intersection controllers cause traffic delays that have enormous negative impacts on the economy and environment. Signal timings can be improved by using vehicles' GPS information to overcome the control action deficit at isolated intersections. This new signal control algorithm is beneficial for traffic engineers and governmental agencies, as traffic flow can be optimised and, hence, fuel consumption and emissions decreased. Under the open European Telecommunication Standards Institute (ETSI) Cooperative Awareness Message (CAM) framework, a traffic responsive GPS based vehicle actuation algorithm (GPS-VA) is proposed. GPS-VA uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection. The gathered information is then used to actuate intersection signal timings. Microscopic simulations comparing GPS-VA to fixed-time control and inductive loop based vehicle actuation (Loop-VA) on four urban road networks were performed to see how the proposed GPS-VA algorithm performs compared to existing control strategies. The results show that GPS-VA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 50% for connected vehicle fleet penetrations above 30%.

Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles

“…The MATS algorithm does this in a novel way that combines 3 data sources (fixedtime plans, loop detectors, and CVs) rather than two, as is typical in the literature. This paper completes the concepts introduced in [23,24] by modifying the algorithm to be robust in real-world networks, addressing mode-switching issues, and performing simulations under a comprehensive testing framework. The proposed microsimulation testing framework is unique in that it combines data from the Birmingham and West Midlands traffic data portal [25] with OpenStreetMap (OSM) data [26] to create a large-scale, current, and realistic simulation case study.…”

A Review of Vehicle Speed Control Strategies

“…It is expected that in the future, because of the continuous extension of road networks, continuous increase in the amount of motor vehicle ownership, and increase in the mileage of annual driving, the energy consumption of road traffic will continue to increase (Leard et al, 2019), making the pollution problem more serious. And expected to increase traffic congestion by 60% in 2030 (Rafter et al, 2017). At present, relevant government departments, research institutes, and various types of enterprises have conducted significant research to solve traffic congestion and pollution.…”

Enabling a Cooperative Driver Messenger System for Lane Change Assistance Application

“…Signalized intersections comprise of traffic lights, whereas the unsignalized intersections consists of stop or yield signs, or in some instances, no signs at all. The ever-increasing vehicles on roads can lead to congestion [1], traffic delays [2], and economic and societal costs due to crashes [3]. In this context, unsignalized [4] and signalized [5] intersections have been found to be hazardous and inefficient towards catering large volumes of traffic.…”

“…According to the United States National Motor Vehicle Causation Survey, around 35% of the crashes between 2005 and 2007 occurred on intersections, out of which, insufficient situational awareness and ambiguity over intersection priority 1 Connected & Autonomous Vehicle Research Lab (CAVREL), University of Central Florida, Orlando, FL, USA. gshah8@knights.ucf.edu 2 Honda Research Institute, Ann Arbor, MI constituted for 44.1% and 8.4%, respectively [6].…”