Current Projects


A Trusted Data Platform for Transportation Data Sharing

Principal Investigator: William G. Raisch, New York University

An information sharing and situational awareness technology platform developed as part of the Metropolitan Resilience Network, a unique public-private partnership, is being adapted to support transportation data sharing and stakeholder engagement / input in New York City and each of the cities where C2SMART collaborating institutions are located. This effort is being used to identify key issues and vet targeted solutions of the Center as well as inform approaches to assure that solutions once developed reflect the concerns / context of the user community in their implementation.


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

Principal Investigator: Joseph Chow, New York University

Co-Principal Investigator: Kelvin Cheu, University of Texas at El Paso

As one of the direct beneficiaries of advances in information & communications technologies, mobility on-demand (MOD) fleet operations have grown increasingly relevant to the operation of smart cities. Many seniors face mobility issues and differences in life-style from the rest of the population; as a result, recommended destinations for the general population may not be preferable to this specific subpopulation. This project is developing a new inference tool that fleet operators can use over time to efficiently learn users’ preferences for different destinations. A “routing-constrained recommender system” is being developed with a component to select a subset of destinations that are route-constrained to recommend to the user to select, and sequential learning component that takes different users’ feedback (e.g. a 5-star rating and selection of destination in each case) to update the knowledge base.


Designing and Managing Infrastructure for Shared Connected Electric Vehicles

Principal Investigator: Don MacKenzie, University of Washington

The long-term goal of this work is to integrate strategic and tactical management of the vehicle-infrastructure system to support electrified shared mobility. Algorithms are being developed that integrate real-time information on state of charge, customer demand trends, and infrastructure availability to maximize shared electric mobility at minimal cost to the operator. This tactical control model will then be integrated into a strategic infrastructure network design model that determines the locations, charging rates, and number of plugs for each charging station. Constituent sub-models of mobility patterns and demand will be calibrated using data from existing connected vehicle services (BMW’s ReachNow, which operates in Seattle, Portland, and New York City).


Development of A Mobile Navigation Smartphone Applications for Seniors in Urban Area

Principal Investigator:  Kelvin Cheu, University of Texas at El Paso

Co-Principal Investigator: Natalia Villanueva-Rosales, University of Texas at El Paso

Co-Principal Investigator: Joseph Chow, New York University

The objectives of this project are (a) to understand the lifestyle and mobility needs of seniors; (b) to develop a prototype smartphone smart mobility application that caters to the needs identified; (c) to conduct pilot tests in El Paso and New York.


Emerging Leaders in Transportation

Principal Investigator: Sarah Kaufman, New York University

The Emerging Leaders in Transportation program develops early-career transportation professionals to develop and promote innovations within their organizations. The three-day program includes professional development with executive leaders, communication work through networking activities, and site visits to major transportation management locations. Emerging leaders will learn how to evaluate the feasibility of innovation for their workplaces, incorporate it into the workflow, and build long-term technology plans. These discussions will contribute to an invigorated leadership cadre, with a systematic understanding of planning for the future and how to bring new organizational tools to their agencies.


Integrative Vehicle Infrastructure Traffic System (iVITS) Control in Connected Cities

Principal Investigator: Camille Kamga, City College of New York

A simulation-based approach is being used for the evaluation of traffic control algorithms that will utilize CV technologies. Given the ongoing CV pilot deployment in NYC, the proposed project will tie in to the objectives set out to be achieved as a part of the NYC CV pilot. The City College of New York (CCNY) team will work with NYU and UW researchers to test the models and algorithms in microsimulation and hardware-in-the loop simulations on a NYC-specific network.


Monitoring and Control of Overweight Trucks for Smart Mobility and Safety of Freight Operations

Principal Investigator: Hani Nassif, Rutgers University

Technologies to screen overweight trucks including high-speed weigh-in-motion (HS-WIM) system integrated with license plate reader and/or security camera, and the feasibility of such technologies compared to current screening practices at weighing stations are being evaluated.  In addition, infrastructure damage cost associated with the overweight trucks, as well as permits, are being evaluated to provide guidelines in developing the overweight and permit policies and fee structures. Two Apps, one for autonomous ticketing and the other for damage cost evaluation, will be developed.


Quantifying uncertainty and distributed adaptive control for unanticipated traffic patterns as a result of major natural and man-made disruptions

Principal Investigator: Saif Jabari, NYU Abu Dhabi

The project aims to develop control tools (algorithms) for traffic management in congested urban networks in a way that (i) takes advantage of data made available to intersection controllers by the vehicles and (ii) adapts to dramatic changes in traffic conditions (namely, incidents and no-notice emergency management events). The first part of the research quantifies uncertainty in traffic conditions due to data and data processing limitations, the second part of the research utilizes this understanding of uncertainty to develop scalable and robust control techniques.


Sustainability of Urban Consumption Practices

Principal Investigator: Sarah Kaufman, New York University

Urban logistics are becoming increasingly complex: in addition to the heterogeneity of goods transported and of the means of transportation, urban logistics encompass diverse multiple stakeholders (local authorities, transporters, retailers). The sustainability of goods transported and personal mobility will be a major challenge for both public and private sectors. The NYU Rudin Center for Transportation is conducting research aiming to understand the sustainability challenges associated with freight and passenger mobility in cities, and recommend policies for sustainable consumption practices at the local level. The sustainability research will deepen understanding of the true sustainability of urban residents who commute by public transportation, but regularly receive packages from online retailers, and recommend corresponding policies. This work is being conducted with the research group 6-t: Bureau de Recherche, based in Paris. The project aims to better understand and compare the consumption practices and mobility behaviors of the residents living in two of the major cities in the world (Paris and NYC). The work will be conducted through a simultaneous survey in both cities, analysis, stakeholder meeting and narrative.


Traffic Signal Optimization and Coordination in Connected Cities

Principal Investigator: Jeff Ban, University of Washington

This research is investigating traffic signal control optimization and coordination under the connected vehicle (CV) environment in smart cities. The goal of the project is to reduce traffic congestion and energy use, by leveraging the data and connectivity of enabled by CV.


Understanding Mobility Patterns and Decision-making Using an Integrated, Multi-modal Sensing Platform in a Quantified Community

Principal Investigator: Constantine Kontokosta, New York University

A dataset of more than 500,000,000 WiFi probe requests from mobile devices, together with land use, demographic, and city administrative data, from an urban test-bed in New York City is being used to develop high resolution pedestrian mobility models in dense urban environments.