Development of an Open Source Multi-Agent Virtual Simulation Test Bed for Evaluating Emerging Transportation Technologies and Policies

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.

A Multi-Stakeholder Approach to Developing Effective Policies to Reduce the Impact Costs of Overweight Vehicles on Roads and Bridges

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.

An Artificial Intelligence Platform for Network-wide Congestion Detection and Prediction Using Multi-source Data

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.

Development of Advanced Weigh-In-Motion (A-WIM) System for Effective Enforcement of Overweight Trucks to Reduce their Socioeconomic Impact on Major Highways

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.

City-scalable Destination Recommender System for On-demand Senior Mobility

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.

Research and Field-Testing of Vehicle-Traffic Control with Limited-Capacity Connected/Autonomous Vehicles

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.

Quantifying Uncertainty and Distributed Adaptive Control for Unanticipated Traffic Patterns as a Result of Major Natural and Man-made Disruptions

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).