New technologies such as global positioning system, smartphone, and social media are changing the way we move around. Traditional transportation research has overwhelmingly emphasized the collection of quantitative data for modeling, without much collection of qualitative data to understand the processes of why and how individuals make their travel choices. We developed a prototype in this project to use realtime GIS and social media (Twitter) to collect, analyze, and display qualitative travel information from individuals. There are two goals in this research project.

The goal of this project is to develop a comprehensive framework with a set of models to improve multi-modal traffic signal control, by incorporating advanced floating sensor data (e.g. GPS data, etc.) and traditional fixed sensor data (e.g. loop detectors, etc.). In order to accomplish this goal, we completed five tasks. First, we conduct a comprehensive survey with transportation professionals, who can bring up existing state-of-practice, open issues and future challenges in multi-modal traffic signal control.

This report presents a general probability-based approach for assessment of roadway safety hardware (RSH). It was achieved using a reliability analysis method and computational techniques. With the development of high-fidelity finite element (FE) models, numerical crash simulations can be performed to evaluate various RSH systems, in addition to crash tests. For highly nonlinear and implicit impact responses, metamodeling techniques provide a rational approach to replace the expensive numerical simulations.

The research has tested several policy scenarios, and the practical implications of parking supply management is discussed. The results obtained help provide a better understanding of the need of freight parking and the policy alternatives available to improve the efficiency of urban freight systems. The results of the simulation provided the following insights related to which parking policies could be implemented to improve freight parking.

This paper investigates the spatial patterns of residential parking intrusions in New York City, their determinants, and an estimated number and spatial patterns of induced excessive vehicle miles traveled (VMT). The paper analyzes parking tickets data with driver registration demographic data, and determines potential residential parking intrusions of passenger vehicles. Results show that significant clusters of intrusions are mainly located in almost all the residential areas of Manhattan, and some residential areas of Bronx, Brooklyn, and Queens.

A Battery Electric Vehicle (BEV) feasibility considering State Of Charge (SOC) level is assessed using multiday activity-travel patterns to overcome the limitations of using oneday activity-travel patterns. Since multi-day activity-travel patterns are not readily available, we generate multi-day activity-travel patterns through sampling from readily available single-day household travel survey data with considerations of day-to-day intrapersonal variability.

The airfoil-based electromagnetic energy harvester containing parallel array motion between moving coil and trajectory matching multi-pole magnets was investigated. The magnets were aligned in an alternatively magnetized formation of 6 magnets to explore enhanced power density. In particular, the magnet array was positioned in parallel to the trajectory of the tip coil within its tip deflection span.

The purpose of this research was to identify the pressing workforce issues confronted by transit authorities nationwide and promising ways in which they are being addressed. The study also included a closer examination of New York City Transit (NYCT), the nation’s largest transit authority, to consider its challenges and which solutions could be brought to bear to address them.

The main objective of this project is to develop and conduct limited testing of novel sensors using Bluetooth technology (BT) to estimate OD demands and station wait times for users of public transit stations. The NYU research team tested the feasibility of the utilization of sensors with Bluetooth technology to estimate Origin-Destination (OD) demands and station waittimes of users of transit systems with a focus on subway systems.

This research project produced an open source transit market data visualization and analysis tool suite, The Bus Transit Market Analyst (BTMA), which contains user-friendly GIS mapping and data analytics tools, and state-of-the-art transit demand modeling microsimulation capabilities. BTMA combines both archived transit operations data (e.g., automatic fare-box data), and new open data resources, particularly GTFS and US Census.