Candace Brakewood

An analysis of commuter rail real-time information in Boston

Prior studies have assessed the impacts of real-time information (RTI) provided to bus and heavy rail riders but not commuter rail passengers. The objective of this research is to investigate the benefits of providing commuter rail RTI. The method is a three-part statistical analysis using data from an on-board survey on two commuter rail lines in the Boston region. The first analysis assesses overarching adoption, and the results show that one-third of commuter rail riders use RTI. The second part conducts difference of means tests and regression analysis on passenger wait times, which reveals that riders’ use of RTI is correlated with a decrease in self-reported “usual” wait times. The third part analyzes 12 quality-of-service indicators, which have a limited relationship with RTI utilization. The results suggest that the benefits of commuter rail RTI are modest. Despite this, many commuter rail riders choose to use this new information source, which has important implications for transit managers considering deploying RTI systems.

Use of Mobile Ticketing Data to Estimate an Origin–Destination Matrix for New York City Ferry Service

One of the fundamental components of transit planning is understanding passenger demand, which is commonly represented with origin–destination (O-D) matrices. However, manual collection of detailed O-D information through surveys can be expensive and time-consuming. Moreover, data from automated fare collection systems, such as smart cards, often include only entry information without tracking where passengers exit the transit network. New mobile ticketing systems offer the opportunity to prompt riders about their specific trips when they purchase a ticket, and this information can be used to track O-D patterns during the ticket activation phase. Therefore, the objective of this research is to use back-end mobile ticketing data to generate passenger O-D matrices and compare the outcome with O-D matrices generated with traditional onboard surveys. Iterative proportional fitting was used to create O-D matrices with both mobile ticketing and onboard survey data. These matrices were compared using Euclidean distance calculations. This work was done for the East River Ferry in New York City, and the results show that during peak periods, mobile ticketing data closely match survey data. However, in the off-peak period and during weekends, when travelers are more likely to be noncommuters and tourists, matrices developed from mobile ticketing and survey data have greater differences. The impact of occasional riders making noncommute trips is the likely cause for these differences, because commuters are familiar with using the mobile ticketing product and occasional riders are more likely to use paper tickets on the ferry service.

Trends in Mobile Transit Information Utilization: An Exploratory Analysis of Transit App in New York City

Smartphone applications that provide transit information are now very popular. However, there is limited research that examines when and where passengers use mobile transit information. The objective of this research was to perform an exploratory analysis of the use of a smartphone application known as Transit App, which provides real-time transit information and trip planning (schedule) functionality. Backend data from Transit App were examined by time of day and day of week in the New York City metropolitan area. The results show that the pattern of both the trip planning feature and overall realtime information usage follow the typical pattern of transit ridership, which has morning and evening peaks. Additionally, self-reported household locations of Transit App users in the New York City area were compared with household socioeconomic characteristics (specifically, income, ethnicity, and age) from census data using GIS visualizations and the Pearson correlation coefficient, but they do not appear to be correlated. This implies that passengers use Transit App regardless of household income, race, or age trends in their neighborhood. This exploratory study examined a rich new data source—backend data from a transit information smartphone application—that could be used in many future analyses to help transit agencies better understand how transit riders use information and plan their trips.