Traffic signal timing optimization incorporating individual vehicle fuel consumption characteristics under connected vehicles environment

Abstract

This paper aims to develop a modeling framework for optimizing the timing of a set of traffic signals by considering individual vehicle characteristics (such as fuel consumption and travel time). Through the Vehicle to Infrastructure (V2I) communications, such individual vehicle information is available for the infrastructure center to produce optimal signal timing. The proposed strategy applies the intelligent driving model (IDM) to predict vehicle trajectories under the connected vehicle environment. The objective function is to minimize the total system travel and fuel consumption. The resulting model is a mixed integer (binary) nonlinear program (NLP). The Matlab tool box OPTI is applied to solve NLP to produce the optimal green time for each phase group and the optimal offset of each intersection. To test and evaluate the model, numerical examples are presented for three scenarios, accounting for various combination of vehicle types and traffic demands. The results are also compared with those generated by traffic simulation using VISSIM.