2017 Urban Science Intensive
Transportation & Networks

 



1. Performance Analysis and Tracking for NYC’s Transit System

CUSP Students: Hongting Chen, Francis Ko, Shay Lehmann, Nurvita Monarizqa, and Ian Wright

CUSP Mentors: Kaan Ozbay and Huy Vo

USI Sponsor: TransitCenter

New York City buses have been gradually losing ridership to an increasing overcapacity in the subway system for several years now. One reason for this shift may be declining service reliability throughout parts of the MTA bus system. This capstone team partnered with the public transit reform advocacy group, TransitCenter, to drill into this issue from a data-driven perspective. This project is an interactive dashboard that uses open data to build and display useful reliability metrics, right down to the bus stop level, for about 200 of the city’s most popular bus lines. The idea was to generate public interest in bus service reliability, in addition to building a robust tool that may be useful for government advocacy campaigns. The team believes that analytical tools like ours should help surface particular parts of the bus system that require deeper investment from the MTA, and ultimately begin to rebuild the public trust that is necessary for an effective transit system.


2. Predicting Unmet Trip Demand

CUSP Students: Anita Ahmed, Alexey Kalinin, Pooneh Famili, Xin Tang, and Ziman Zhou

CUSP Mentors: Kaan Ozbay and Huy Vo

USI Sponsor: NYC Taxi and Limousine Commission

The purpose of this project is to predict the number of taxi trip that goes unmet in NYC. As per TLC’s problem definition “Unmet Trip Demand” is a situation when New Yorkers or tourists would like to take a taxi, but hardly can do it and should spend more than 5 minutes to find one. According to community surveys reports conducted by TLC many of the neighborhoods claimed they do not get a Taxi when they are hailing on the street. TLC wanted to verify this claim using the trip data that is generated by Taxi cabs. The TLC’s goal of this project is to identify the contributing aspects and develop legitimate metrics to reveal unmet demand locations across New York City.

The project was broken down into 2 phases. In the first phase, we identify areas with potentially unmet demand where historical trip records and breadcrumbs data were made available. Three approaches were developed in this phase. The first approach was to compare the monthly total pickup and drop-off counts. If in a census tract, the number of pickups is less than 25% of the number of drop-offs, and the taxi activities have reached a certain level, then this area is identified as having a potential unmet demand. The second approach identified the underserved census tracts by finding those with the least number of vacant taxis within a given time duration. It is computed as the ratio of total evaluation duration and total “free” minutes of vacant taxis. In the third approach, the rate of change Uber pickup was compared with the rate of change of combined pick up of all taxi services over a 6 months period. In census tract where a positive growth of Uber was detected but an overall decline is taxi services was observed those census tracts were identified as having unmet demand. The census tracts that we identified having unmet demand using all 3 approaches were selected for study in Phase 2. In the second phase, we determine socioeconomic features in top-matched regions discovered in phase 1 to determine key factors that contribute to taxi trip demand. The features we included are median income, median rent, population density, car ownership, crime rate, access to public transit, and commercial land use. Based on the findings, we hope to derive a measurable unmet demand even for areas without sufficient historical trip data.TLC can then possibly make new rules and regulation to encourage drivers to serve that underserved neighborhood.


3. Optimizing the Location and Use of Taxi/FHV Relief Stands

CUSP Students: Cheng Hou, Le Xu, Vishwajeet Shelar, and Yao Wan

CUSP Mentors: Kaan Ozbay and Huy Vo

USI Sponsor: NYC Taxi and Limousine Commission

There are over 100,000 licensed For-Hire Vehicles (FHVs) in NYC. The For-Hire industry is constantly growing with more vehicles going out on the road every day. An increase of vehicles on the road has led to more congestion on the road. Some drivers may take short breaks from work and inadvertently add to this congestion. In an effort better manage our curb space, Taxi/FHV relief stands have been implemented to give drivers a place to pull over and take a break. However, there are only 69 Taxi/FHV Relief Stands (TRSs) spread across NYC. This projects aims to provide an analysis on the effectiveness of existing relief stands and suggest locations for installing new Taxi/FHV relief stands.

A model is developed to suggest new locations using two major datasets. One is the breadcrumb data and the other dataset is the taxi trip data. The total data size is about 30 GBs. The NYU High-Performance Hadoop Cluster is used to process this data. Breadcrumbs data records the coordinates of all the yellow and green taxis at every two minutes. This data is not openly available and it is authorized for CUSP students for certain taxi data related projects. The second dataset is the Taxi Trip data, which contains both taxi and FHVs trip records which are used to understand taxi demand within a certain geospatial unit (in our case a hexagonal zone). Additional datasets like public park toilets, food stands, food trucks and restaurants are used.

The final deliverable of the project is twofold. First is a descriptive analysis of the effectiveness of existing Taxi/FHV Relief Stands. The other is to provide a list of possible hexagonal zones where new Taxi/FHV Relief stands can be located.


4. Food Distribution Network Vulnerability

CUSP Students: Chenxi Cui, Patrick Gitundu, Kaylyn Levine, William Xia, and Xinshi Zheng

CUSP Mentor: Stan Sobolevsky

USI Sponsor: NYC Mayor’s Office of Recovery and Resiliency

This capstone project was conducted on behalf of the New York City Office of Recovery and Resiliency (ORR) to study the potential impacts of a disruption to the City’s food distribution network.  The ORR was created in response to Superstorm Sandy, which crippled much of the City: costing billions of dollars and damages and exposing many infrastructure vulnerabilities within it.  It is the ORR’s goal to mitigate a future catastrophic event, and to ensure that there is no need for emergency feeding on any level within the City.

Our CUSP team was tasked with describe the impact of food supply disruptions on the New York City population, with a specific focus on vulnerable communities.  From this, we aim to identify them as well as strategic point-of-sale (POS) locations within those communities that would impact residents the most if closed. Our results will provide specific recommendations to the New York City Office of Recovery and Resiliency to mitigate the impact on the food distribution system. More specifically, our results will identify strategic POS locations that the New York City Office of Recovery and Resiliency will target in their business assistance program to help maintain food accessibility to vulnerable communities in emergency scenarios.


5. Networks of Urban Vulnerability

CUSP Students: Sunny Kulkarni, Ekaterina Levitskaya, Lani M’cleod, Yuan Shi, Richard Vecsler

CUSP Mentor: Dr. Stanislav Sobolevsky

USI Sponsor: Lockheed Martin Advanced Technology Laboratories (ATL)

Urban vulnerability research is critical in understanding and mitigating exposure to disruptive events within the urban environment. While the literature is replete with the examination of individual disruptive events, few studies have taken scenarios including multiple simultaneous disruptions. This paper seeks to identify and measure the effect of multiple simultaneous disruptions on the NYC Subway system as an example of urban vulnerability.  We construct a network model based on train schedule information for a typical weekday rush hour and estimate demand distribution from origin-destination data derived from census data in order to assess the cumulative delay caused by first a single, then a pair of simultaneous disruptions to the network. In particular, we define the scenario in which the pair of simultaneous disruptions cause an effect greater than the effect of the single disruptions together as ‘synergy’.  We find top stations pairs that have a strong synergistic effect. Further we identify a marked difference in network characteristics between positive and negative synergy.  We discuss network characteristics, such as distance, degree, similarity and community, that may underlie this synergy and close with next steps on how our model can be used to analyze network vulnerability at different times of day,  for different categories of the population, and expanded to include other modes of transportation. To help communicate the findings, we provide a visualization tool that can be used to assess the synergy throughout the network.