Empirical Analysis to Determine Effectiveness of Dynamic Mobility Management Systems

Empirical Analysis to Determine Effectiveness of Dynamic Mobility Management Systems

Transportation systems are experiencing several significant innovations in terms of shared mobility with carsharing and ride hailing companies; electrification with plug-in hybrid electric vehicles (PHEV) and fully electric vehicles (EV); connectivity with the ever-expanding vehicle-to-everything (V2X) communications; and automation with several deployments of automated or partially automated vehicles. With these innovations there is still major traffic congestion, leading to more fuel consumption, significant greenhouse gas (GHG) emissions, and poor air quality. One framework that can be utilized to address these issues is Dynamic Mobility Management Systems (DyMMS). The DyMMS framework will allow of the collection of real-time data from vehicles, infrastructure, and the atmosphere, and the utilization of these data to implement real-time management of vehicles, driver behavior and transportation infrastructure. The DyMMS framework will facilitate innovations in Intelligent Transportation Systems (ITS) that will improve traffic safety, mobility, and reduce energy consumption and emissions. DyMMS examples might include, where and when to drop off a shared vehicle, when a PHEV should switch the drivetrain to all electric, and how an automated vehicle’s behavior should change in an unpredictable situation.

This research is focused on developing three strategies within the DyMMS framework: 1) an infrastructure related strategy that will combine the efforts of the Multi-Modal Intelligent Traffic Safety System (MMITSS) and eco-driving in order to improve safety, mobility, and emissions at intersections; 2) a vehicle dynamics related strategy that will combine Eco-Approach and Departure (EAD) application and MMITSS; and 3) a powertrain related strategy that will integrate MMITSS, EAD, and selective catalytic reduction (SCR) heating in order to improve the performance of SCR by using real-time emissions feedback, calculated trajectory, and a heating element in order to reduce NOx emissions.

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