ESTIMATION OF ROUTE-LEVEL TRANSIT OD MATRIX USING BOARDING AND ALIGHTING DATA

Abstract

Transit OD matrix is a fundamental element of service planning and operation.As it provides information about passenger trips between each pair of stops, OD matrix can be crucial for route designing, route modification (extending or splitting), transit/crew scheduling, vehicle composition, etc. Transit origin-destination matrices can be generated at route or network-level. At route-level, no transfers are considered and trips are assumed to be independent of each other, whereas working at network-level necessitates dealing with trip interchanges, which makes the estimation process more complicated. Traditionally, transit OD matrices are generated by conducting on-board surveys. Such surveys are labor-intensive, time consuming and highly expensive. Therefore, utilizing the readily available data in the estimation process has been the subject of many research for the past few decades. Owing to the popularity of automatic systems such as Automatic Fare Collection (AFC) , Automatic Passenger Count (APC) and Automatic Vehicle Location (AVL) systems in public transport, it is more convenient now to collect large-scale information about passengers’ mobility. The data obtained from such systems have been widely used to study the mobility pattern of passengers in public transport. Most recent studies used smart card data to infer passengers’ boarding and alighting stations using trip chaining algorithm. The problem of estimating OD matrix using boarding and alighting data is ill-posed in nature. That is, there can be more than one possible solutions to the problem. To reduce the degree of ill-posedness, previous methods such as Iterative Proportional Fitting (IPF) and Maximum Likelihood Estimation (MLE) used a seed matrix and tried to improve the seed matrix through an iterative process. However, a base O-D matrix is usually not available. In such cases, these methods fail to generate reliable results.