This project will develop a mobile app that streamlines the reporting of mis-parked dockless scooters and bikes, relaying data to companies responsible and/or local governments while generating a data set that can support a variety of research questions.
This project aims to extend and field-test CAV-based traffic signal/vehicle control methods developed by the research team in previous projects to understand and quantify the benefits of CAV-based control in the real world.
This research aims to develop modeling and analysis methods to capture the key behaviors and intersections of the major players when integrating ridesourcing with transit.
Traffic simulation is an important tool that can assist researchers, analysts, and policymakers to test vehicle/traffic control algorithms, gain insights of micro/macro traffic dynamics, and design traffic management strategies. However, different implementations require different simulation scales and there is no multiscale simulation platform that satisfies all requirements. In this research, we propose to establish a multiscale vehicle-traffic-demand (VTD) simulation platform for connected and automated transportation systems (CATS).
This study aimed to develop a demand model for an eFFCS service in the City of Seattle, which can increase the feasibility of eFFCS by reducing the cost of relocation by optimally locating the charging stations near the areas of heavy usage and real-time control to minimize manual relocation.
Overview Remote repositioning (RR) technology allows a scooter or similar lightweight, slow-moving vehicle to be controlled by a human driver from a remote location. RR can resolve many mis-parking problems