New York University City Urban Mobility and Intelligent Transportation Systems Laboratory (UrbanMITS)is a multi-modal transportation infrastructure research and education facility combining a series of new concepts, technologies and services to integrate information, vehicles and transportation infrastructure to increase mobility, safety and comfort, and reduce energy waste and pollution. Since its inception, It has been a collaborative effort between the University, state and federal governments, and industry.
C2SMART, a USDOT Tier 1 University Transportation Center, uses cities as living laboratories to study challenging transportation problems and find solutions from the unprecedented recent advances in communication and smart technologies. C2SMART’s main research priority is improving mobility of people and goods, with a focus on smart cities. The center's research activities are divided into three areas, which together embody 9 of the 12 vision elements identified by USDOT as the major defining features of a smart city.
Double parking is one of the key contributors to traffic congestion on urban streets. This study utilizes parking violation records for New York City along with field data collected using video recording, and adopts a comprehensive modeling approach that combines available data with two types of models: M/M/∞ queueing model and micro-simulation model. It can provide traffic agencies a potential approach to quantify the impact of double parking in a large-scale network and insights into the management and alleviation of on-street parking problems including incentives for encouraging off-hour deliveries and more effective enforcement during peak hours.
The role of location in digital world has changed as expanding numbers of internet users including location information to their posts and these digital footprints allowed researchers to study the spatial and temporal characteristics of human activity and mobility patterns. This paper introduces an approach to collecting and utilizing geo-located Twitter status updates to report a quantitative assessment of human mobility. The results show that Twitter users follow the “Lévy Flight” mobility patterns. Moreover, the estimated mobility flows are found to be similar to the ground-truth data obtained from NYMTC Regional Household Travel Survey.
This study explores the potential of using big data including taxi trip, subway turnstile, road network, land use, socio-demographic data in advancing the pedestrian safety analysis including the investigation of contributing factors and the hotspot identification. A tobit model is developed to relate grid cell-specific contributing factors to crash costs which are left-censored at zero. The potential for safety improvement (PSI) which could be obtained by using the actual crash cost minus the cost of “similar” sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots.
The study investigates spatio-temporal variations of transportation system recovery behavior, Recovery curves are estimated for each evacuation zone category to model time-dependent recovery patterns of the roadway and transit systems. The methodology proposed in this study can be used to evaluate the resilience of transportation systems to natural disasters and the findings can provide government agencies with useful insights into emergency management.
Professor Joseph Chow’s book, Informed Urban Transport Systems: Classic and Emerging Mobility Methods Toward Smart Cities, has been published by Elsevier.Details
Elsa Kong, an undergraduate in the civil engineering department, is this year’s recipient of the Molitoris Leadership Scholarship for Undergraduates, awarded by the WTS Foundation.Details
C2SMART has several new faces for the summer, including four undergraduate researchers and four high school students through the ARISE program.Details