Capital Program Resource Model (CPRM)
The objective of this study is to develop a model that links the resource requirements for Capital Program delivery functions with the NYSDOT Capital Program.
The objective of this study is to develop a model that links the resource requirements for Capital Program delivery functions with the NYSDOT Capital Program.
This perception of transit can be captured through the sentiment of posts made by users on social media. Performance metrics based on user perception can provide the service provider a customer-facing view of their service and help enhance their service and address the user concerns, especially from a public health standpoint in a post-COVID lockdown world, appropriately.
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.
The research team aims to test a new queueing network-based dynamic rebalancing strategy in test cases provided by ReachNow in Brooklyn, NY. In addition, the researchers will develop a MATSim agent model of the study area in NYC and calibrate it based on household travel survey data from NYMTC, Openstreetmaps, traffic data from NYCDOT, and transit schedules from GTFS.
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
Cities like NYC and Seattle need to deal with significant growth of urban deliveries as a result of increasing e-commerce compounded by increased stay-at-home behavior due to COVID-19. We propose to develop a citywide model of truck network flows, one that relates changes to truck routes to changes in truck tours or to time-of-day congestion pricing policies.