Developing a new optimal speed advisory algorithm for connected vehicles in signalised road networks

Rapid growth in urbanisation and vehicle ownership can cause traffic and environmental costs. Even though road transport carries along with important benefits to society, it also imposes detrimental impacts such as congestion, traffic collisions and emissions. Congestion leads to inefficient traffic performance and vehicular emissions released by interrupting the traffic flow, especially at signalised road networks and intersections. Traffic signals control these locations where conflicting traffic movements occur by assigning the right-of-way at regular intervals. 

Nevertheless, traffic signals are also a major contributor to congestion in terms of traffic inefficiency, increased number of stops, delays, and environmental problems. The emerging applications of Intelligent Transport Systems and vehicular communications pave the way for Connected Vehicle (CV) applications to alleviate congestion and minimise adverse traffic and emission impacts in signalised road networks. Connected vehicles are capable of improving traffic efficiency by providing and exchanging instantaneous traffic information among vehicles and/or between vehicles and infrastructure via vehicle-to-vehicle (V2V) or infrastructure-to-vehicle (I2V/V2I) communication technologies. Having access to essential traffic and signal control data in advance constructively improves driver behaviours. 

Henceforth, this PhD study focuses on an I2V application known as Green Light Optimal Speed Advisory (GLOSA). GLOSA recommends an optimal speed for connected vehicles under an I2V communication environment enabling them to pass through a downstream signalised intersection either without stopping or with minimised stopping time. This avoids unnecessary acceleration/deceleration behaviours and improves traffic flow. Some of the existing GLOSA algorithms in the literature mainly recommend acceleration, but a dynamic speed recommendation would be comparatively easier for the CVs implementation. Moreover, hardly any existing GLOSA algorithms are found to be sufficient to calculate an optimal speed advisory for the red-light arrivals. Another gap in the literature is the queue formations in the GLOSA algorithms on signalised road networks. Therefore, this PhD study mainly aims to develop, implement, and analyse a new optimal speed advisory algorithm for CVs, which advises optimal speed at both green and red-light arrivals by also considering potential queue formations (i.e. queue dissipation time). The analysis is based on a selected real-world signalised network scenario to enhance traffic efficiency at signalised intersections and mitigate vehicular emissions. An integrated simulation platform, which combines the External Driver Model of VISSIM with the Visual Studio (C++) programming language, is used to develop the GLOSA control algorithm to represent GLOSA-reflective behaviours. This algorithm incorporates information from both infrastructure and CVs to determine an optimal speed for approaching vehicles to allow them to pass through the traffic signal during a green phase or with minimised stopping times during a red phase. The control system uses kinematics data of CVs and real-time signal status of a relevant downstream traffic light involving the current and future signal phasing and timing (SPaT) information (e.g. cycle length, length of the green, amber, and red phases). Then, the phase at which the CV arrives the traffic light is predicted to determine an arrival phase as either green or red. Accordingly, an optimal speed is individually recommended to upcoming CVs depending on the traffic light status upon arrival and possible queue formations at the traffic signal, which can be either: (i) no change in speed, (ii) speeding up or (iii) slowing down. Real-world traffic data is used to realistically replicate and calibrate the scenario by using GEH statistic in a microsimulation environment. This aids in assessing the GLOSA system effectiveness on road traffic flow and exhaust emissions appropriately under mixed traffic environments (e.g., conventional and GLOSA-equipped CVs) at different penetration rates for the designed two types of signal controller scenarios: fixed-time and adaptive signal control. The results of this PhD study indicate that the developed GLOSA control algorithm is effective in traffic flow improvement and emission reduction for both fixed and adaptive signal control scenarios. Receiving information about the downstream traffic signal status in advance utilises speed profiles of GLOSA-equipped CVs while approaching the traffic signal. Accordingly, maintaining an improved driving behaviour relieved unnecessary stop-and-go conditions and hard braking manoeuvres, which consequently enhances traffic flow and mitigates exhaust emissions for CO2, NOx and PM10. Therefore, it is found that the number of stops is decreased by 81% for the fixed-time and 79% for the adaptive signal control, average queue lengths are reduced by up to 96% for the fixed-time and 93% for adaptive signal control, the average delay per vehicle is decreased up to 62% for the fixed-time and 70% for the adaptive signal control, and average travel time per vehicle is improved by up to 35% for both the fixed-time and adaptive signal control scenarios, respectively. Moreover, CO2, NOx and PM10 emissions are reduced by 6.6%, 14.2% and 6.6% for the fixed-time scenario, respectively, and by 6.9%, 15.9% and 5.2% for the adaptive signal operation scenario, respectively. As the penetration rate of GLOSA-equipped CVs increases in the mixed traffic environment, the more benefit it brings to the traffic efficiency and vehicular emissions.