Calculation of Transit Performance Measures Using Smartcard Data

Smartcard automated fare collection systems (SCAFC) for transit have been considered primarily for their administrative function of controlling access to the service and for revenue management. However, it is likely that data from these systems also can be used to describe both transport supply and demand. This article illustrates the use of smartcard data to estimate various transit performance measures. Combined with well-established evaluation processes, such measures can help operators monitor their networks in greater detail. The performance of the network supply (vehicle-kilometers, vehicle-hours, commercial speed, etc.) and the statistics on passenger service (passenger-kilometers, passenger-hours, average trip length, etc.) can be calculated from these datasets for any spatial or temporal level of resolution, including route and bus stop levels. Introduction Smartcard data systems generally are implemented for administrative functions such as controlling access to a service. On a transit network, they help improve the transit user’s satisfaction, with simplified ticketing options (single card, security), while enhancing revenue collection for public authorities (reduced fraud, multilevel validation) (Conklin et al. 2004). To do their job, these systems need to record a large amount of information on the daily use of the transit network. Each transaction is recorded, along with spatio-temporal details: time, spatial location, Journal of Public Transportation, Vol. 12, No. 1, 2009 80 operational information (line, stop), and card type (fare type and privileges). Even if the resulting dataset was not designed, a priori, for analytical purposes, it can be processed to reveal information on how the network is rendered and used on a continuous basis. The relevance of smartcard data for monitoring a transit network is validated using a set of continuous data from the Société de transport de l’Outaouais (STO) outputted from the smartcard fare collection system. With these continuous data, daily, weekly, and seasonal activity cycles are identified for various transit card types (regular adults, students, and seniors, for instance) using data mining techniques (Morency et al. 2007). This confirmation of the variability of transit use during these cycles suggests that the supply might not always adequately match the real transit demand. “Measuring the performance of a transit system is the first step toward efficient and proactive management” (Bertini and El-Geneidy 2003). With this issue in mind, current research is looking into the supply side of the equation and aims to estimate some transit performance indicators using smartcard data. Actually, smartcard data offer a unique opportunity to monitor the use and supply of a transit network simultaneously on any given day. They can be used in an AVL-APC (Automated Vehicle Location, Automated Passenger Counting) system, which has proven to be useful for transportation planning (Furth et al. 2006). Hence, the purpose of the paper is to illustrate the potential of smartcard data to derive operational indicators revealing the service that was truly offered to the users on a specific day (compared with the planned service: vehicle-kilometers for instance), estimate the use of the service on that day (demand: passenger-kilometers, for instance), and observe how these two sides of the transit network evolve over time. The paper is organized as follows. First, we present work on the use of smartcard data for analytical purposes, followed by some transit performance indicators, keeping in mind that the main purpose of the paper is to evaluate the usability of smartcard data to estimate a number of these classical indicators. Then, the methodology is presented, namely the dataset used for the experiment, some imputation operations, and a description of the transit performance measures. Results of the estimation of indicators from a demonstrative dataset are then presented. A discussion concludes the paper.