Metropolitan Transportation Authority Research Articles

Inequality in behavioural heat adaptation: an empirical study with mobility data from the transport system in New York City, NY, USA

Heat exposure, which can negatively affect human health and wellbeing, is heterogeneous within US cities. However, little is known about who can avoid heat stress by adjusting their everyday behaviour. We aimed to analyse the effect of ambient temperature on mobility, specifically subway (ie, the underground railway system) use, in New York City, NY, USA, during 2014-19. For this empirical study, subway use across New York City was measured with turnstile data from the New York City Metropolitan Transportation Authority between Jan 1, 2014, and Dec 31, 2019. Passenger numbers were then aggregated to the zip code tabulation area (ZCTA) level. Daily observational climate data were obtained from the US National Weather Service between Jan 1, 2014, and Dec 31, 2019. Socioeconomic data at the ZCTA level originated from the American Community Survey 2019. We extracted data on population age, ethnicity, commuting, employment, median household income, rent, and health-insurance coverage. We used a fixed-effects panel-regression model to assess the influence of temperature on subway use in New York City, which was the main outcome of our study. We obtained data for 438 subway stations across New York City. After data cleaning and preprocessing, the final aggregated data sample consisted of 238 508 instances of subway use in 1955 days across 6 years for 122 ZCTAs, with 168 days missing in the raw data and 67 days removed as outliers. The results of the fixed-effects panel-regression analysis showed a strong, non-linear effect of daily maximum temperature on subway use. Subway use was highest at 11·5°C and substantially decreased for temperatures that were colder and warmer than that, with reductions reaching 6·5% (95% CI 2·5-10·5) for the coldest temperature (ie, -6·5°C) and 10·5% (6·0-14·0) for the hottest temperature (ie, 34·5°C). Reductions differed between weekdays and weekends, when residents generally had more freedom to adjust their behaviour. Neighbourhoods that were at a socioeconomic disadvantage experienced smaller or no reductions in mobility in heat; mobility increased in neighbourhoods with beach access. Our study showed that temperature had a strong, non-linear effect on subway use, but the magnitude of the effect on subway use was heterogeneous across areas of the city on warm days. Weaker avoidance of heat stress correlated with less privilege, indicating compounding health risks. Everyday behavioural adaptation to heat is therefore an effect pathway that contributes to unequal heat effects and should be explored in future research. The Volkswagen Foundation and the Werner Siemens Foundation.

Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A.

The COVID-19 pandemic has disrupted day-to-day lives and infrastructure across the United States, including public transit systems, which saw precipitous declines in ridership beginning in March 2020. This study aimed to explore the disparities in ridership decline across census tracts in Austin, TX and whether demographic and spatial characteristics exist that are related to these declines. Transit ridership data from the Capital Metropolitan Transportation Authority were used in conjunction with American Community Survey data to understand the spatial distribution of ridership changes caused by the pandemic. Using a multivariate clustering analysis as well as geographically weighted regression models, the analysis indicated that areas of the city with older populations as well as higher percentages of Black and Hispanic populations were associated with less severe declines in ridership, whereas areas with higher unemployment saw steeper declines. The percentage of Hispanic residents appeared to affect ridership most clearly in the center of Austin. These findings support and expand on previous research that found that the impacts of the pandemic on transit ridership have emphasized the disparities in transit usage and dependence across the United States and within cities.

The Death-Based Model of Organizational Learning: Accident, Pandemic, and Workplace Change in New York Public Transit

The public transportation workers of New York City lost their lives to COVID-19 at a shocking rate in the spring of 2020, likely abetted by their employer’s resistance to allow workers to wear masks until mere days before a region-wide lockdown was declared. We might see this death toll as a tragic outcome of uncertainty in the face of the unprecedented, yet the stance of the employer (the Metropolitan Transportation Authority, or MTA) was consistent with its longstanding reluctance to assimilate or pursue signals that suggest need for safety reforms — that is, until a worker dies. This article terms this pattern a “death-based model of organizational learning,” and situates the virus’ toll on transport workers from three angles: first, from workers’ experience of existential precarity in their workplaces, rooted in dangers workers readily problematize but which are not addressed by management; second, by showing how the MTA may modify rules following an employee fatality, at least when that death cannot be explained by individual failures alone; and third, by exploring the MTA’s longstanding hostility to health and safety research conducted in its physical and institutional bounds. These prior patterns articulated in the MTA’s response to COVID-19, such as in passivity in the face of general public health guidelines, disinterest in obvious founts of expertise to tailor its response to the pandemic, and in the eventual acceptance of a nascent public health role in light of the mounting death toll of its employees.

Performance Measurement and Evaluation Framework of Public Microtransit Service

This research project developed an evaluation framework and service standards for microtransit service at a metropolitan transportation authority. Microtransit provides an elevated level of service from traditional demand response, enabling more spontaneous travel and better connectivity to the fixed-route system, meaning microtransit service performance standards fit in between traditional demand response and fixed route. Microtransit neighborhood zones also serve distinct purposes depending on the operational and financial characteristics of a given zone. The purpose of this evaluation framework project was to determine the optimal performance measurement process for microtransit service. The research team developed the evaluation framework through gathering information from agency staff on current goals and objectives for microtransit, conducting an industry scan, and determining measurable metrics from available data sources. The project developed a typology of microtransit service zones, then created the evaluation framework using the performance measures applicable to agency goals. The research team conducted a final test of the framework at the six-month period of service to refine the measures as needed. Minimum standards and optimal goals were set for each performance measure based on system performance and agency goals for innovative service. The framework uses service standards and goals that are applicable to given types of neighborhood zones and distinct from baseline standards for fixed-route or other demand-responsive service. The evaluation framework can be integrated into transit agency service standards to measure the success of microtransit neighborhood zones and planning future service.

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.

Planning for the First and Last Mile: A Review of Practices at Selected Transit Agencies in the United States

A transit trip involves travel to and from transit stops or stations. The quality of what are commonly known as first and last mile connections (regardless of their length) can have an important impact on transit ridership. Transit agencies throughout the world are developing innovative approaches to improving first and last mile connections, for example, by partnering with ride-hailing and other emerging mobility services. A small but growing number of transit agencies in the U.S. have adopted first and last mile (FLM) plans with the goal of increasing ridership. As this is a relatively new practice by transit agencies, a review of these plans can inform other transit agencies and assist them in preparing their own. Four FLM plans were selected from diverse geographic contexts for review: Los Angeles County Metropolitan Transportation Authority (LA Metro), Riverside (CA) Transit Agency (RTA), and Denver Regional Transit District (RTD), and City of Richmond, CA. Based on the literature, we developed a framework with an emphasis on transportation equity to examine these plans. We identified five common approaches to addressing the FLM issue: spatial gap analysis with a focus on socio-demographics and locational characteristics, incorporation of emerging mobility services, innovative funding approaches for plan implementation, equity and transportation remedies for marginalized communities, and development of pedestrian and bicycle infrastructures surrounding transit stations. Strategies in three of the plans are aligned with regional goals for emissions reductions. LA Metro and Riverside Transit incorporate detailed design guidelines for the improvement of transit stations. As these plans are still relatively new, it will take time to evaluate their impact on ridership and their communities’ overall transit experience.

COVID Response: The Municipal Liquidity Facility

At the onset of the COVID-19 pandemic, state and local governments were among the sectors expected to experience the most severe distress. The combination of a sharply deteriorating revenue picture, a pressing need for additional expenditures, delays in the receipt of substantial taxes owed, and an inability to access the financial markets raised serious concerns among many observers about the ability of state and local governments to meet their public service delivery responsibilities. In April 2020, the Federal Reserve announced the establishment of the Municipal Liquidity Facility (MLF) to help municipalities manage the cash flow challenges that the pandemic produced. The MLF ultimately offered three-year loans at penalty rates to a set of eligible municipal issuers that included states, large cities and counties, and a number of revenue bond issuers. Research suggests that the MLF, in spite of lending to only the State of Illinois and the Metropolitan Transportation Authority, contributed to a healing in the municipal securities market as a whole. Effects on real economic outcomes like employment in the sector are harder to attribute to facility.

Heat-Moderating Effects of Bus Stop Shelters and Tree Shade on Public Transport Ridership.

Rising temperatures threaten the resilience of public transit systems. We determined whether bus stop shelters and tree canopy surrounding bus stops moderated the effect of warm season temperatures on ridership in Austin, Texas, and whether shelters and trees were equitably distributed. For bus stops (n = 2271) of Capital Metropolitan Transportation Authority, boardings per bus were measured 1 April–30 September 2019. Air temperature data originated from the Camp Mabry weather station. Tree canopy was calculated by classification of high-resolution aerial imagery from the National Agriculture Imagery Program. Data on race, ethnicity, poverty level, median age, and bus commuters within census tracts of bus stops originated from the 2014–2018 American Community Survey. Using multilevel negative binomial regression models, we found that shelters did not moderate the effect of high temperatures on ridership (p > 0.05). During high temperatures, each one-percent increase in tree canopy was associated with a lesser decrease (1.6%) in ridership compared to if there were no trees (1.7%) (p < 0.001). In general, shelters and trees were equitably distributed. Insignificant or modest effects of shelters and trees on ridership during high temperatures may be attributed to the transit dependency of riders. For climate change adaptation, we recommend tree planting at bus stops to protect from ridership losses and unhealthy exposure to extreme heat.

Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting.

Influence of Lane Width on Bus Crashes

Motor vehicle lane width is often reduced to accommodate non-motorized transportation and increase safety for all road users—in many cases from the conventional width of 12 ft to as narrow as 9–10 ft. Although it has not been a significant issue for passenger car drivers, the narrower lanes pose concerns for bus transit operators. This study analyzed data from the Capital Metropolitan Transportation Authority, the public transit provider in the Austin, Texas, area, to evaluate the influence of narrow travel lanes on bus crashes. It included a comprehensive review of literature, interviews with cities and transit agencies, an analysis to determine the correlation between lane width and target bus crashes (sideswipe, fixed-object, and mirror-to-mirror), and an assessment of the impact of a curb or parked car immediately adjacent to the narrow outside lane on bus crashes. The literature review and interview findings support a standard lane width of 11–12 ft for bus routes. The statistical analysis suggested that more target bus crashes were associated with narrower lane widths. A curb or parked car immediately adjacent to the outside lane was problematic regardless of whether the outside lane was less than 12 ft or not. Though narrower lane width could contribute to fewer catastrophic crashes because of the slower speeds, it increases the likelihood of certain bus crashes. Cities and transit agencies should work together to determine lane width to balance the needs of all road users.

Impact of COVID-19 on the Economic Output of the US Outbreak's Epicenter.

Coronavirus disease of 2019 (COVID-19) started in December 2019 in Wuhan, China. In a few months, it has become a pandemic with devastating consequences for the global economy. By the end of June, with almost 2.6 million confirmed COVID-19 cases, United States is above other countries in the rankings. Furthermore, New York with more than 416 thousand cases is the epicenter of outbreak in the US and had more cases than any other countries in the world until first half of June. In this paper, we use a two-step Vector Auto Regressive (VAR) model to forecast the effect of the virus outbreak on the economic output of the New York state. In our model, we forecast the effect of the shutdown on New York’s Gross Domestic Product (GDP) working with Unemployment Insurance Claim series representing a workforce factor, as well as the Metropolitan Transportation Authority (MTA) ridership data indicating the economic activity. We predict annualized quarterly growth rate of real GDP to be between -3.99 to -4.299% for the first quarter and between -19.79 to -21.67% for the second quarter of 2020.

Costs and Benefits of Electrifying and Automating Bus Transit Fleets

Diesel-powered, human-driven buses currently dominate public transit options in most U.S. cities, yet they produce health, environmental, and cost concerns. Emerging technologies may improve fleet operations by cost-effectively reducing emissions. This study analyzes both battery-electric buses and self-driving (autonomous) buses from both cost and qualitative perspectives, using the Capital Metropolitan Transportation Authority’s bus fleet in Austin, Texas. The study predicts battery-electric buses, including the required charging infrastructure, will become lifecycle cost-competitive in or before the year 2030 at existing U.S. fuel prices ($2.00/gallon), with the specific year depending on the actual rate of cost decline and the diesel bus purchase prices. Rising diesel prices would result in immediate cost savings before reaching $3.30 per gallon. Self-driving buses will reduce or eliminate the need for human drivers, one of the highest current operating costs of transit agencies. Finally, this study develops adoption schedules for these technologies. Recognizing bus lifespans and driver contracts, and assuming battery-electric bus adoption beginning in year-2020, cumulative break-even (neglecting extrinsic benefits, such as respiratory health) occurs somewhere between 2030 and 2037 depending on the rate of battery cost decline and diesel-bus purchase prices. This range changes to 2028 if self-driving technology is available for simultaneous adoption on new electric bus purchases beginning in 2020. The results inform fleet operators and manufacturers of the budgetary implications of converting a bus fleet to electric power, and what cost parameters allow electric buses to provide budgetary benefits over their diesel counterparts.

Generation of crowd arrival and destination locations/times in complex transit facilities

In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm for generating tens of thousands of high-level agent paths from this specification. This algorithm allows our specification to be used with traditional crowd simulation obstacle avoidance algorithms while still maintaining the realism required for the complex, real-world simulations of a transit facility. Our evaluation with industry professionals shows that our approach is intuitive and provides controls at the right level of detail to be used in large facilities (200,000+ people/day).

Translation software: An alternative to transit data standards

Data standardization is recognized in many disciplines as a critical aspect of data stewardship. Establishing and implementing data specifications increases the usefulness of data collection efforts and facilitates analysis techniques. With the advent of large quantities of machine-generated data, the use of standardized data formats feeds opportunities for visualization and advanced applications with machine-learning and Artificial Intelligence (AI). The transportation industry made substantial progress with data format specifications in the late 1990s, primarily for highway traffic. Unfortunately, establishing data standards has been an on-going challenge for the transit community. Archived Intelligent Transportation Systems (ITS) transit data (e.g., Automatic Vehicle Location (AVL), Automatic Passenger Counters (APCs), Automatic Fare Card (AFC)) still lack industry standards for data formats. Recent advancements in electronic transit scheduling (e.g., General Transit Feed Specifications (GTFS)) met a portion of this challenge with Open Data specifications. Now GTFS provides transit riders with agile information on services available at any location where the data is provided to developers of mobile device application (apps). Due to system and vendor limitations, the Metropolitan Transportation Authority (MTA), serving the New York City region, publishes its real-time subway system data in GTFS-R and its bus data in SIRI. This research develops an Application Programming Interface (API) to translate GTFS-R into SIRI to overcome the lack of standards making it possible to harmonize the subway and bus systems for the New York region. This solution offers the opportunity to develop a novel set of analytical tools, including pseudo-surveillance data for performance metrics.

Using Smart Farecard Data to Support Transit Network Restructuring: Findings from Los Angeles

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

Customer Journey Time Metrics for New York City Bus Service using Big Data

As data collection for public transportation improves and customers’ appetite for information grows, there has been a growing interest in performance measurement systems that better reflect customer experience and quantify the impacts of service while accounting for ridership. A fair amount of research has been dedicated to developing and refining these kinds of metrics, with a particular focus on comparing customers’ expected and actual waiting time on train platforms or at bus stops. Despite this, only a limited number of transit agencies have implemented such measures. This paper presents a set of metrics developed by the Metropolitan Transportation Authority (MTA) that calculates the additional time customers spend waiting for and riding buses in excess of the schedule, termed additional bus stop time (ABST) and additional travel time (ATT) respectively. Trip time performance, termed customer journey time performance (CJTP), is also computed. The methodology leverages MTA’s origin–destination (OD) ridership model and bus location data to calculate these values for each individual passenger. Measuring at the passenger level means that impacts of service delays or changes can be weighted by the number of passengers affected, unlike past bus-level measures. This enables the design of service management techniques that benefit the most riders possible. The form of the metrics, which puts service impacts in easy-to-understand terms that reflect actual customer experience, likewise provides the opportunity to better engage with customers. MTA is among the first to make these metrics regularly available to the public, and the first to publicly report them for buses.

Analysis of Customer Complaints Data using Data Mining Techniques

The Metropolitan Transportation Authority (MTA) is a public transportation service provider for the NewYork region. It is considered as the largest transportation network in North America, serving a population of 15.3 million people in the 5,000-square-mile area fanning out from New York City through Long Island, southeastern New York State, and Connecticut [1]. The public transportation services offered includes: subways, buses, and railroads which provide 2.73 billion trips each year to New Yorkers [1]. MTA receives and manages customers’ complaints through Customer Relationship Management System (CRM) for all agencies [2]. In this work, we apply data mining techniques on a real domain problem using a data set of MTA customer feedback. We analyze customer complaints data for useful information that help companies to improve the quality of services and identify factors that lead to low levels of customer satisfaction. We then classify agencies based on the different areas of received customer complaints. The results of our work can be used to aid decision makers in MTA to focus on areas which needs more improvement.

Amplifying Opportunities: Systems Engineering as a Lens to See the Big Picture [Career Advisor

Two decades in the transportation industry gave Anne O'Neil exposure to many methods of travel. She worked on projects spanning tunnels, airports, highways, and railways-including over a decade with Metropolitan Transportation Authority New York City Transit (MTA NYCT)-and her career was propelled forward as a result.

Anatomy of a new dollar van route: Informal transport and planning in New York City

Despite nearly eight million rides per day on formally planned and legally sanctioned buses and subways, dollar vans provide service that shadow some of the busiest bus lines in Brooklyn and Queens. Dollar vans are an informal transport service that occupy an awkward liminal space between legal and illegal—some are licensed and some are not, but all dollar vans operate illegally. It is this legal confusion that renders them informal. It is because of this informality that dollar van operators and drivers can also adapt their routes and service as they deem necessary. This “generative” mode of planning introduces a second dimension of informality; however, I argue that dollar van operators follow a similar logic as formal transportation operators when planning service. I demonstrate this by examining the case of a dollar van operator in Brooklyn developing a new route. I juxtapose his method with that of bus planners from the Metropolitan Transportation Authority (MTA) to show how both groups of planners rely on prompts they encounter, be it anecdotal stories about West Indians in Flatbush moving away or an angry letter from a state legislator, rather than a systematic approach that is taught in classrooms. By examining the planning of a new dollar van route in Brooklyn, I trace the operator's planning process and compare his data against quantitative datasets to show that his generative planning process is supported by the quantitative data and deepens its meaning when combined with his local knowledge. Seen in this light, it is clear that the distance between informal and formal transit is artificial rather than inherent.

Transforming the Transportation Customer Experience through Contemporary Artistic Practice: A Case Study in Strategic Implementation

Institutionalized government support for artwork incorporated into transportation environments has a history spanning more than 80 years in the United States. Although the Federal Transit Administration has a history of policy guidelines, and the American Public Transportation Association, and Americans for the Arts have developed best practices for the successful integration of artworks during the planning and construction phases of transportation projects, little research exists concerning the public life of those artworks during the operational phase, beyond maintenance concerns. This paper examines an artist-led tour program initiated by the Los Angeles County Metropolitan Transportation Authority (Metro), as a case study, to demonstrate one way contemporary artistic practices are strategically implemented to further community engagement, by extending the reach of existing, integrated artworks during a capital project’s operational phase. The study begins with a context for public art in transportation environments, including a brief overview of the tradition of government support for public art in the United States. Artworks integrated within the Metro system are examined with a focus on features that engage customers as landmarks, inspire responses from participants rooted in the memory of historical events or popular culture, and respond to pre-programmed cues. Lastly, this research discusses the transformational value offered by contemporary artistic practices rooted in relational aesthetics, approaches that prioritize transitory experience over material product, by providing continued engagement with communities beyond the planning and construction phases, as examined through an artist-led tour program developed by Metro.