Transportation Research Interdisciplinary Perspectives (Sep 2019)
Translation software: An alternative to transit data standards
Abstract
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.
