Monitoring transit-served areas with smartcard data: A Brisbane case study

Abstract

A city can be divided into areas that are served by transit and those that are not. In this study, the former is referred to as "transit-served areas (TSAs)". To quantify, monitor and visualise the TSAs of the Southeast Queensland (SEQ), this study analyses half-year smartcard data between 2012 and 2013 from TransLink, the transit agency for SEQR For scenarios are prescribed and four corresponding metrics (the minimum, actual, random and maximum travels) are calculated, which reflect transit riders' different levels of elasticity of distance travelled (EDT) relative to the cost of travel within or between TSAs and how transit riders could possibly travel as EDT varies. The total trips generated by or attracted to TSA and the temporal and spatial variations of these metrics across days are used to monitor TSAs, especially transit trips within or between them. The results indicate that transit trips attracted to, and generated by TSA and transit trips between TSAs vary significantly over time and across space. Across the scenarios, the temporal variance tends to be larger as EDT becomes more inelastic. The above results provide useful references for decision-makers to understand better the ranges of transit demand (by TSA) across the space and time when EDT is a variable.

Spatial-interaction network analysis of built environmental influence on daily public transport demand

“…For instance, Pan et al (2017) used integrated circuit card data and cellular signaling data to empirically examine the effects of rail transit station-based public transport on daily station passenger volume. Using a large dataset crowdsourced from smart cards, Zhou et al (2019) explored how to quantify, monitor, and visualize the transit-served areas (TSAs) of southeast Queensland, Australia. A TSA is a type of transit-oriented development.…”

Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility

“…Transactions are recorded only when the traveller swipes their card to board a vehicle or access a station. SCD have been used by researchers to investigate patterns of urban flows, including commuting, mobility and travel areas [11][12][13][14][15]. These studies have focussed on identifying the spatiotemporal patterns within the SCD in order to inform and support transportation planning.…”

“…These studies have focussed on identifying the spatiotemporal patterns within the SCD in order to inform and support transportation planning. Such studies are plentiful, and typically they evaluate the spatiotemporal patterns of trips through the transit system [14] to quantify and predict individual mobility [14] to examine route choices [14], the scales of regular and explicable travel behaviours [16] and temporal changes in the spatial structure of urban movement [12]. Comprehensive reviews of the technologies, applications and methodologies of SCD analyses and the evolution of thinking in this area are provided by Bagchi and White [17], Pelletier et al [7] and by Li et al [18].…”

Inferring Social Functions Available in the Metro Station Area from Passengers’ Staying Activities in Smart Card Data

“…Ma et al,Mahrsi et al,and Kieu et al [13,22,23] measured passengers' travel habits and regularity over long periods of time to cluster cardholders. Zhou et al [24] monitored and quantified the elasticity of distance travelled as transit feasibility in transit-served areas. Ma et al, Long et al,Long & Thill, studied the commuting patterns anchored in the public transportation fabric.…”