APTA has released an introduction and guidance summary for artificial intelligence (AI) and machine learning (ML) in public transportation. This document provides a comprehensive resource for public transit agencies to understand, evaluate, and implement AI and ML tools across their operations.
The primer and guidance overview is based on transit agency research and interviews with staff to understand current and planned AI applications across eight functional areas: back office, operations, customer support, maintenance, safety and security, customer analytics, planning, and fares and ticketing. This primer includes real-world examples from member institutions. The four guidance summaries include tools and infrastructure needs, policy and governance needs, agency readiness and staff capacity, and implementation guidance.
Key findings show that customer support and customer analytics are the most common areas for AI adoption. When including AI implementation plans for public transport, back-office and operational functions top the list, with 50% and 47% of survey respondents reporting current or planned use, respectively.
“Public transit agencies of all sizes are deploying AI to make their services more reliable, efficient, and responsive to riders,” said APTA President and CEO Paul P. Skoutelas.
This material was developed with research support from EBP and Foursquare ITP. Findings were derived from an online survey of 32 APTA member transit agencies, supplemented by interviews with transit agency staff and a review of public documentation regarding transit agency AI implementation.
Access the introduction and guidance overview on APTA’s website, Artificial Intelligence and Machine Learning in Public Transportation.
