Connected Vehicle Supported Adaptive Traffic Control for Near-congested Condition in a Mixed Traffic Stream

Abstract

Connected Vehicles (CVs) have the potential to significantly increase the safety, mobility, and environmental benefits of transportation applications. In this research, we have developed a real time adaptive traffic signal control algorithm that utilizes only CV data to compute the signal timing parameters for an urban arterial in the near congested condition. We have used a machine learning based short term traffic forecasting model to predict the overall traffic counts in CV based platoons. Using a multi objective optimization technique, we compute the green interval time for each intersection using CV based platoons. Later, we dynamically adjust intersection offsets in real time, so the vehicles in the major street can experience improved operational conditions compared to loop detector based actuated coordinated signal control. Using a 3 mile long simulated corridor of US 29 in Greenville, SC, we have evaluated the performance of our CV based adaptive signal control. For the next time interval, using only 5% CV data, the Root Mean Square Error of the machine learning based prediction is 10 vehicles. Our analysis reveals that the CV based adaptive signal control improves operational conditions in the major street compared to the actuated coordinated scenario. Also, using only CV data, the operational performance improves even for a low CV penetration (5% CV), and the benefit increases with increasing CV penetration. We can provide operational benefits to both CVs and non CVs with the limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% reduction in average maximum queue length and stopped delay, respectively, in major street coordinated direction compared to the actuated coordinated scenario in the same direction.