Linking Incidents to Customers (LINC): An Algorithm for Linking Incidents to Rail Customer Delays Inspired by Traffic Flow Theory
Document Type
Journal Article
Publication Date
2022
Subject Area
mode - rail, mode - subway/metro, place - north america, place - urban, operations - performance, planning, planning - methods, planning - signage/information
Keywords
planning and analysis, effects of information and communication technologies (ICT) on travel choices, big data, transportation planning analysis and application, planning data analysis, public transportation, rail transit systems, subway
Abstract
Rail transit agencies have greatly advanced the ability to measure delays to rail system customers and have developed key performance indicators for rail systems based on customer travel time. The ability for operators to link these customer delay metrics to root causes would provide great benefit to agencies, from incident response improvement to capital program prioritization. This paper describes a method for linking late train arrivals to both late customers and incident tickets. Inspired by traffic flow theory, the method identifies impact zones in time and space that can then be linked to a potential root cause by way of incident tickets. This algorithm is currently under development by the Washington Metropolitan Area Transit Authority’s Office of Planning, and its outputs are being integrated into a variety of operations- and capital-related business processes.
Rights
Permission to publish the abstract has been given by SAGE, copyright remains with them.
Recommended Citation
Eichler, M. (2022). Linking Incidents to Customers (LINC): An Algorithm for Linking Incidents to Rail Customer Delays Inspired by Traffic Flow Theory. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2676(3), pp. 598-607.
