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Planning and Policy Analysis

Reinforcement Learning Methods for Traffic Demand Analysis and Control in Intelligent Transportation Systems

Within the dynamic field of transportation research, Reinforcement Learning (RL) has been recognized as a critical approach to monitor, model, and manage transportation systems. Among the diverse array of RL techniques, the Upper Confidence Bound (UCB) algorithm stands out for its potential in solving long-standing transportation problems.

Feasibility of employee shuttles for equitable mobility and improved housing options for low- and middle-income employees: A Case for Stony Brook University Campus

The objective of this project is to assess the feasibility of an employee shuttle for Stony Brook University (SBU) campus employees to reduce car dependency and to expand employee access to more affordable housing choices. The ultimate aim of the project is to develop a demand responsive employee shuttle pilot through an online mobility platform for work-home commute, complemented by on-demand service for noncommute trips (e.g., grocery) and carpool matching.

Transportation Risk and Resilience Metrics

This research, addressing the areas of Inclusive Advanced Technology Application and Climate Resilient Infrastructure, will evaluate a set of proof-of-concept transportation resilience measures to determine their utility and scalability as state and local performance measures. The research will review the latest scientific literature on risk and resilience measures to catalog methodologies scoring road network assets based on road segment attributes, hazard intersections, network centrality, and accessibility.

Developing NY Statewide Equity Measures and a Synthetic Dataset for Analysis of Equitable and Sustainable Mobility Technology and Policy Deployments

New innovations in transportation to improve mobility and solve problems such as congestion are not always equitably distributed and do not impact all travelers equally. This project proposes to develop equity-based performance measures for Intelligent Transportation Systems (ITS) and new mobility technology implementations that can be used to ensure inclusivity of all users. Best practices will be studied from across the nation, and interviews will be held with local stakeholders to gain feedback.

Updating Princeton’s circa 2010 nation-wide, virtual household, virtual individual, virtual personTrip files to circa 2020

For over ten (10) years, Princeton University’s Transportation Program, under the direction of Professor Alain Kornhauser has been developing interactive web-based tools to make readily available to planners and researchers the fundamental demand for mobility that supports a desirable quality-of-life that reflect where people live and the distribution of land uses in which real residential patterns are imbedded.

Dr. Abolfazl Karimpour

Dr. Karimpour is an Assistant Professor at the State University of New York Polytechnic Institute. Prior to this role, he was the Manager and Assistant Research Profesor at the Center for Applied Transportation Science at the University of Arizona. He graduated from The University of Arizona with a Ph.D. degree in Transportation Engineering. His research interests are Traffic Operation and Safety, Traffic signal Optimization, Data Analytics, Public Transportation, and Smart Cities Transportation.

Dr. Alain Kornhauser

Dr. Alain Kornhauser is Professor of Operations Research & Financial Engineering at Princeton University. He studied Aerospace Engineering at Penn State earning a BS and MS and Princeton, earning a PhD. In 1971 he joined the Aerospace Engineering faculty at U of Minnesota where he applied automation, network analysis and optimal control to the design of Personal Rapid Transit (PRT) Systems. He returned to Princeton in 1972 extending his pivotal work to more conventional forms of transportation. In 1979 he founded ALK Technologies, Inc.

Business Location Data Analysis and Editing Interface Tool Development

One of the most important aspects of transportation planning is understanding employment information of businesses and organizations. Information such as location of employment, size of organization or business in terms of employees, sales, can provide valuable input to understanding travel patterns and human activities. Visualizing this information along with several administrative, transportation and infrastructure facilities provides key contextual information to transportation planning agencies.

Performance Evaluation of Asphalt Mixtures Statewide

Currently, asphalt mixtures are design using volumetric concepts to determine optimum asphalt content levels with no means of verifying mixture performance prior to field production and placement. A new design methodology called Balanced Mixture Design (BMD) promotes the use of evaluating and design asphalt mixture using rutting and fatigue cracking methods and criteria to achieve an optimum asphalt content that will result in an asphalt mixture performing well in rutting and fatigue cracking scenarios – thereby “balancing” the asphalt mixture performance.

Juliette Spertus

Juliette Spertus is an architect, writer and curator. Her work focuses on the relationship between architecture and infrastructure and the possibilities for public space. Fast Trash is her first infrastructure exhibition. She previously worked as a project architect for Michielli Wyetzner Architects in New York and as a designer at Utile, Inc. in Boston. She completed a BA in art history at Williams College and received her professional architecture degree from l’Ecole d’Architecture des Villes et des Térritoires à Marne-la-vallée near Paris, France.

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