The advances of wireless communication and information technology have enabled the technological foundation and provided an unprecedented data-rich environment known as “big data”. One emerging transformative technological initiative is Connected Vehicle, which aims to enable networked wireless communications among vehicles, infrastructure and passengers’ personal devices.
The proposed research aims to develop a new connected vehicle technology that enables eco-driving of vehicles at signalized intersections where adaptive control is instrumented. The work capitalizes on the emerging advanced technologies including Connected Vehicle, Adaptive Traffic Signal Control, and Big Data Analytics. The outcome includes smoother vehicle movement trajectories, reduced fuel consumption and green-house gas emissions, hence system-wide better mobility, efficiency and environmental benefits.
The proposed work is extremely timely and significantly different from other on-going connected vehicle research, in that it aims to integrate the developed technology with New York City’s real-time adaptive control system, applying big-data analytics on the already available big traffic data. Mostly notably, New York City’s big traffic data environment include millions of records of per-trip travel times from 8 million daily commuters, volumes and occupancies from a wireless sensor network, and detailed historical and real-time controller status data for more than 10,000 ASTC controllers. One of the team members, namely, KLD is the developer of New York City’s adaptive control system. This enables the proposed work as an innovative solution providing practical and workable contributions to New York’s transportation community.
The proposed research involves developing the following methodologies and evaluating them using microscopic traffic simulation:
• Data fusion of real-time large-scale multi-source traffic, vehicle and environmental data. The data includes traffic conditions, network-wide signal operational status, real-time adaptive signal timing information, registered Transit Priority Preemption Request, vehicle dynamics and engine economy data. The sources of the data include ITS roadway sensors, Electronic Toll Collection (ETC) tag readers, connected vehicle equipment’s and central adaptive signal control systems at Traffic Management Center.
• Big Data Analytics to synthetize the data and evaluate traffic and environmental parameters and develop operational strategies for individual vehicles at signalized intersections, focusing on smoother vehicle trajectories, and reducing real-time fuel consumption and emissions.
• Connected Eco-Driving. By virtue of V2I and V2V, real-time adaptive signal timing data (and relevant transit signal priority request, if any) from the central TMC are synthesized with vehicles mechanical dynamics and engine-economy status. These data are analyzed to generate customized driving advice to drivers so that they can adjust their driving behavior for a smoother movement trajectory, save fuel and reduce emissions, while clearing the intersection safely and efficiently.
• Test the methodologies through rigorous microscopic traffic simulation, explore the feasibility of a commercializable system prototype, and outline steps to the implementation of such a prototype.