Prediction-Based Eco-Approach and Departure at Signalized Intersections With Speed Forecasting on Preceding Vehicles

Abstract

Using connected vehicle technology, a number of eco-approach and departure (EAD) strategies have been designed to guide vehicles through signalized intersections in an eco-friendly way. Most of the existing EAD applications have been developed and tested in traffic-free scenarios or in a fully connected environment, where the presence and behavior of all surrounding vehicles are detectable. In this paper, we describe a prediction-based EAD strategy that can be applied toward more realistic scenarios, where the surrounding vehicles can be either a connected or non-connected. Unlike highway scenarios, predicting speed trajectories along signalized corridors is much more challenging due to disturbances from signals, traffic queues, and pedestrians. Based on vehicle activity data available via inter-vehicle communication or onboard sensing (e.g., by radar), we evaluate three state-of-the-art nonlinear regression models to perform short-term speed forecasting of the preceding vehicle. It turns out radial basis function neural network outperformed both Gaussian process and multi-layer perceptron network in terms of prediction accuracy and computational efficiency. Using signal phase and timing information and the predicted state of the preceding vehicle, our prediction-based EAD algorithm achieved better fuel economy and emissions reduction in urban traffic and queues at intersections. Results from the numerical simulation using the next generation simulation data set show that the proposed prediction-based EAD system achieve 4.0% energy savings and 4.0% – 41.7% pollutant emission reduction compared with a conventional car following strategy. Prediction-based EAD saves 1.9% energy and reduces criteria pollutant emissions by 1.9% – 33.4% compared with an existing EAD algorithm without prediction in urban traffic.