The main objective of this project is to develop a large-scale, open-source, agent-based model of the study are in NYC using MATSim, an activity-based, extendable, multi-agent simulation framework implemented in Java, and calibrate it based on public sector data from the New York metropolitan area.
This multi-disciplinary project will combine the results of engineering modeling in the area of transportation infrastructure deterioration related to overweight trucks in New Jersey with economic approaches to estimate the contribution of these vehicles to maintenance costs. The research team will engage multiple stakeholders to provide viable strategies that can inform policy formation regarding reducing the impact costs of overweight vehicles.
Congestion detection and prediction have been proposed to support transportation agencies and help them establish effective traffic management measures, as well as aid road users in their adoption of smarter trip strategies, including route and departure time selection. Understanding how congestion at one location can cause ripples throughout a large-scale transportation network is vital for transportation researchers and practitioners to be able to pinpoint the locations of traffic bottlenecks as points to focus on for congestion mitigation.
The research team will first establish a test bed for the development of the advanced WIM (A-WIM) system by collaborating with local transportation agencies for the selection of the test bed site near a static weighing station. Then, it will develop a set of calibration procedures to guarantee that the level of accuracy is reached and preserved over time. These procedures will include, but are not limited to, the effect of temperature, humidity, and pavement type.
This project will use analytical and simulation-based tools for bus network redesign in the presence of ride-hail/for-hire vehicle (FHV) services, particularly for areas regarded as transit deserts.
This project aims to improve the efficiency of mobility-on-demand services with the help of machine learning. The goal is to create an algorithm that public paratransit services, private rideshare companies, and future autonomous vehicle fleets could use to improve operations and lower costs.
This project aims to quantify the various safety, environmental and financial benefits of connected vehicle technologies applied to the New York City municipal fleet.
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.
The project resulted in a new kind of backpressure algorithm tailored to traffic dynamics (i.e. queue buildup and dissipation). The proposed approach overcomes drawbacks in the original theory from a traffic dynamics point of view, specifically, infinite arc capacities, point queues, independence of commodities (turning movements), and there being no analogue for start-up lost times in communications networks (where BP was originally developed).