Big data analytic for estimation of origin-destination matrix in Bus Rapid Transit system

Abstract

In the field of transportation, the origin-destination matrix is one of the main and important components, especially in analyzing, planning, and managing a public transport network. The traditional survey can be used to determine passengers travel patterns and generate origin-destination matrix, but it is inefficient and cost a lot of resources. In the recent years, various methods have been studied to estimate origin-destination matrix to reduce costs and increase the accuracy of passengers flow. Many of them take advantages of big data technology to gather passengers travel information, mostly using smart card data. In this paper, we perform origin-destination matrix estimation using information from the smart card that was collected from automatic fare collection systems in Jakarta's Bus Rapid Transit. There are approximately 160 million records from 20 months of transactions between June 2014 and January 2016. This study utilized trip chaining algorithm that generates 610 daily OD matrices, 87 weekly OD matrices and 20 monthly OD matrices. The analysis is performed at a station and a line level, with an addition of passenger behavioral pattern.