Search

This manuscript summarizes/reviews the current trends of ride-sharing services, which can be a supplement to or replacement of conventional transit services. It argues that smartcard data can be utilized to identify low-demand transit routes, where emerging ride-sharing services such as Uber and Lyft can play a role as a supplement to, or replacement of transit services along those routes. It outlines transferrable procedures and methods that can be used to process smartcard data and to define, identify and visualize low-demand transit routes. In an empirical study, it shows that some low-demand transit routes can probably be replaced by Uber at a lower level of overall costs. But the empirical study also indicates that the replacement can be challenging in reality given issues such as unwillingness or difficulties of local transit agencies and ride-sharing service providers in sharing financial information regarding their services, latent demand for private ride-sharing services, social equity and political/legal concerns over the introduction of private ride-sharing services into the transit-service market and usage of public funding for residents/riders to use private ride-sharing services. • It summarizes/reviews the current trends of ride-sharing services. • It outlines procedures and methods to identify low-demand transit services. • It shows how Uber can replace low-demand transit services with fewer overall costs. [ABSTRACT FROM AUTHOR]

Abstract Commuting patterns where most if not all 'trips are optimised relative to a given distribution of jobs and housing can result in personal and socio-economic benefits. Excess commuting indicators provide useful information for academics and policy analysts to evaluate how the actual commuting pattern deviates from an optimal pattern where commuting costs are minimised. While actual commuting patterns vary from day-to-day, academic researchers have yet to quantify the temporal variation in these indicators over short time periods. This may be due to the lack of available longitudinal data as input for excess commuting indicators. This study shows that new, open and/or big data (NOBD) (e.g. smartcard data) can be exploited to serve as the input for such analysis. In this regard, our study uses half a year's worth of smartcard data from Brisbane, Australia to first derive/aggregate origins and destinations by small areas of the probable commuting trips by transit on all 122 weekdays over the study period. The study quantifies the day-to-day variation in excess commuting indicators for these trips and finds that excess commuting indicators vary considerably from one day to the next. Nevertheless, daily variations occur within a relatively consistent range which can be planned for. Our research suggests that more conscious and systematic utilisation of NOBD could change how commuting flows in cities are quantified, monitored and planned. In addition, with NOBD, we can more efficiently detect daily outliers in commuting patterns. [ABSTRACT FROM AUTHOR]

Emerging non-traditional data (NTD) such as transit agencies' smartcard data and Google's General Transit Feed Specifications (GTFS) have made it easier to unveil the way in which public transit remains relevant, reveal how it facilitates daily mobility, and highlight the way in which different locales across a metropolitan area are connected by public transit. Based on a 24-h period of smartcard data for Brisbane (4 March 2014) allied with GTFS data, we retrieved 205,560 distinct transit riders' trip trajectories by direction (AM/inbound vs. PM/outbound) in Brisbane, Australia. It visualises the trajectories using a waterpark metaphor, in that, like water, people flow downhill. [ABSTRACT FROM AUTHOR]

Public transport with high levels of patronage is the cornerstone of a sustainable city. Passengers' feelings of safety shapes an individuals' decision to utilise public transport that are informed in large part by the other people with which the transit stations and public transit trips are shared. Some of these people are unknown to us but considered – familiar strangers – in that we visually recognise the individuals but have never spoken to them. Our empirical understanding of this subtle yet important social phenomena known to impact sense of community, place attachment and feelings of safety remain in their infancy but with emergence of disaggregate sources of big data bourne from smart card technologies there exists new opportunities to measure and visualise potential familiar strangers in transit networks. The aim of the current study is to develop a new measurement framework that moves beyond existing work that measures familiar strangers via a single volumetric measure and introduce a suite of metrics capturing the full scope of the social phenomena within a transit network. Our measurement framework and associated metrics are operationalised using smart card data from Brisbane, Australia. Results hold implications for policy and planning, and it is hoped that the framework will be redeployed across other situational and cultural contexts forming a growing set of comparative studies. • A new measurement framework for familiar stranger encounters in transit networks. • Measure and visualise spatial-temporal dynamics of familiar stranger encounters. • Explore patterns of familiar stranger encounters across both weekdays and weekends. • Explore patterns of familiar stranger encounters by transit passenger type. [ABSTRACT FROM AUTHOR]