A Transportation Digital-Twin Approach for Adaptive Traffic Control Systems

A transportation digital twin represents a digital version of a transportation physical object or process, such as a traffic signal controller, and thereby a two-way real-time data exchange between the physical twin and digital twin. This paper introduces a digital twin approach for adaptive traffic signal control (ATSC) to improve a traveler's driving experience by reducing and redistributing waiting time at an intersection. While an ATSC combined with a connected vehicle concept can reduce waiting time at an intersection and improve travel time in a signalized corridor, it is nearly impossible to reduce traffic delay for congested traffic conditions. To remedy this defect of the traditional ATCS with connected vehicle data, we have developed a digital twin-based ATSC (DT-based ATSC) that considers the waiting time of approaching vehicles towards a subject intersection along with the waiting time of those vehicles at the immediate upstream intersection. We conducted a case study using a microscopic traffic simulation, Simulation of Urban Mobility (SUMO), by developing a digital replica of a roadway network with signalized intersections in an urban setting where vehicle and traffic signal data were collected in real-time. Our analyses reveal that the DT-based ATSC outperforms the connected vehicle-based baseline ATSC in terms of average cumulative waiting time, distribution of drivers' waiting time, and level of services for each approach for different traffic demands and therefore demonstrates our method's superior efficacy.