Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems

TLDR

ADC systems are increasingly deployed worldwide and generate data that can be applied far beyond their original narrow purposes. This study demonstrates how ADC data can provide transit agencies with low‑cost, rich information, identifies the gap between ADC outputs and agency needs, and proposes a method to derive rail OD matrices from an origin‑only AFC system to replace costly surveys. The authors employ DBMS and GIS technologies to process AFC data, develop a method for inferring OD matrices, and implement it in a software tool. The software tool was applied to the CTA rail system, yielding inferred OD matrices that replace expensive surveys.

Abstract

Abstract: Automatic data collection (ADC) systems are becoming increasingly common in transit systems throughout the world. Although these ADC systems are often designed to support specific fairly narrow functions, the resulting data can have wide‐ranging application, well beyond their design purpose. This article illustrates the potential that ADC systems can provide transit agencies with new rich data sources at low marginal cost, as well as the critical gap between what ADC systems directly offer and what is needed in practice in transit agencies. To close this gap requires data processing and analysis methods with support of technologies such as database management systems (DBMS) and geographic information systems (GIS). This research presents a case study of the automatic fare collection (AFC) system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin‐destination (OD) matrices from an origin‐only AFC system to replace expensive passenger OD surveys. A software tool is developed to facilitate the method implementation and the results of the application in CTA are reported.