Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

An estimation of the effects of social distancing measures on transit vehicle capacity and operations

The COVID-19 pandemic has a direct impact on public transport operations. In this paper, impacts on transit operations of the physical distancing measures deployed to slow the spread of the virus are analyzed and recommendations are provided. At first, two social distancing optimization solutions are provided in order to keep riders at a safe distance. The first is a discrete optimization that can be used in buses with fixed seats, while the second is a continuous optimization that can be used to distribute riders on a grid and be applied on a bus or subway platform. Assuming that the ridership will eventually go back to its level before the pandemic, the second objective of this research is to address the transit operation parameters that need to be changed in order to serve the pre-COVID ridership level, while respecting the social distancing measures. An O-D distribution has been developed in this paper for New York City (NYC) subway line 1, based on the 2018 NYC Travel Survey conducted by the Metropolitan Transportation Authority. Five scenarios of physical distancing are simulated and analyzed in this paper: 3ft, 4ft, 5ft, 5.4ft, and 6ft of separation between passengers. The results show the number of additional trains required to accommodate the hypothetical pre-COVID ridership demand while maintaining social distancing. An interesting key finding is that, by decreasing the minimum distance from 6ft to 5.4ft, the number of additional trains required to serve the transit demand drastically decreases and hence more resources are saved

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations

Quantifying Transportation Benefits of Transit-Oriented Development in New Jersey

The cost of transportation plays an important role in residential location choice. Reducing transportation costs not only benefits the user but also improves the performance of the system as a whole. A direct impact of transit-oriented development (TOD) is the change in out-of-pocket costs for users, as well as the changes in costs of externalities and agency benefits. The prime mover for these changes is the shift in population when a TOD is built near train stations and the induced mode shifts from driving to transit. In this study several sites throughout New Jersey were evaluated to determine the cost of driving versus the cost of using rail transit to major employment destinations in New Jersey and New York City. Driving costs were composed of vehicle operating costs (including fuel, wear and tear, and depreciation), value of time based on the highway travel time from origin to destination, parking cost, and cost of externalities such as air and noise pollution, road maintenance, and accidents. Transit costs were composed of fares, parking costs, and values of travel time, waiting time, and transfer time. The likely changes in population resulting from the TOD were used to estimate changes in highway and transit trips. The costs were compared to derive the net benefit for transportation system users as a result of the TOD. Generally, TOD results in financial benefits to the user and the transportation system