BIM for Subways: an Ontology of Subway Stations
Building Information Modelling (BIM) allows architects, engineers, and policymakers to collaboratively consolidate data on several aboveground urban systems. This technology has no correspondent in underground systems such as subway stations, although they represent an important part of the urban experience. To overcome that gap, students devised a systematic way to represent data in the form of an ontology.
Students: Sandy Zhang (NYU Tandon); Gabriel S. Agostini (Columbia); Michael H. Stanley (NYU Tandon)
Advisor: Debra F. Laefer
Subsurface Testbed
Students generated an integrated subsurface map in a GIS by harvesting and integrate existing data about existing construction, utility failures, and storm information (both historical and predicted) and to use as the basis for future, neighborhood-level, performance, as well as infrastructure investment planning.
Students: Xinru Li (NYU Tandon); Silvia Wei (NYU Tandon) Michael H. Stanley (NYU Tandon)
Advisor: Debra F. Laefer
Research Abstracts
Impact of Double Parking on Congestion
Tania Bteish reviewed New York City parking policies and regulations alongside a comprehensive analysis of double parking data across the city using real-time GTFS data, traffic simulation and prediction, spatial and statistical analysis, and data visualization.
Student: Tania Bteish | Advisor: Kaan Ozbay
Deep Learning-Based Object Detection For Pedestrians, Vehicles and Cyclists Density Approximation and Social Distancing
The fast-evolving COVID-19 pandemic has dramatically changed the traffic patterns and pedestrian behaviors. Students developed a deep learning algorithm, based on a pre-trained convolutional neural network model, to estimate pedestrian, vehicle and cyclist density based on information extracted from real-time traffic cameras in New York City (NYC). The objective of the project is to improve the performance of the pre-trained algorithm by additional training using class-specific datasets and the application of a variety of pre- and post-processing filters.
Students: Omar Hammami (NYU Tandon); Murat Ledin Barlas (NYU Tandon)
Advisor: Kaan Ozbay