Apr-2021

Study investigates public transport usage by older adults in Shizuoka, Japan, using smart card data to develop user-monthly profiles to explore seasonal variability and day-to-day variability. Research finds that older adults in the younger group (65-74) and in highly developed areas were more likely to frequently use public transport, with little seasonal variation. Additionally, day-to-day variability seems to increase with age and level of area development.  

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Sep-2020

Study addresses the applicability of the temporal segmented data identified in 18 clusters for measuring the stability of users' temporal habits as well as conducting descriptive analysis of the clusters, fare types and dates of the week to support their findings. 

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Sep-2021

Study makes use of smart card data mining to compare the urban transit movements of low income residents with middle/high income residents. Research finds that most lower income residents start their journey between 05:00 - 07:00, whilst higher income residents start between 07:00 - 09:00. Paper suggests that the empirical evidence from this paper shows the potential of smart card data to infold low employment spatial and temporal patterns.

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Aug-2021

Study develops a method to mine metro commuting mobility patterns via massive smart card data. Study found that metro commuters accounted for 41% of morning peak traffic in Chongqing, discovering three typical job-housing function patterns and three commute efficiency patterns are discovered, with the characteristics of each being mined. 

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Dec-2020

Study intended to examine how urban spatial structures evolve, specifically focusing on incentives behind movement dynamics. The study makes use of network community detection and smart card data from the years 2013, 2015, and 2017 from Greater London. Study found that London's urban structure has become more polycentric and compact, that Greater London can be clustered into five distinct communities based on characteristics of passengers' travel patterns, and that the dynamics of structural change in different urban clusters differ in both changing intensity and potential motivation. 

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Jan-2018

Study intends to develop a system of prediction for determining if a transport user will make another trip, and if so, the attributes of said trip. The researchers tested their methodology using smart card records from over 10,000 users in London, over two years. The model was able to achieve median accuracy levels of over 80%, with the study finding the first trip of the day hardest to predict. The study also found significant variations found across individuals, implying diverse travel behaviour patterns. 

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Feb-2016

Study investigates regularities in human mobility, questioning if the detected regularities are stable, explicable and sustainable. The study makes use of 1 week of smart card data from three world cities (London, Singapore and Beijing), intending to contribute to a deeper understanding of regularities in patterns of transport use, establishing a general analytical framework for comparative studies using urban mobility data. 

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Nov-2015

Study analyses data from 512 questionnaires conducted in Belfast to determine the influence that perceived bus quality has upon the perceptions of both current and potential users. Research identifies 11 significant indicators that are reported to have a significant influence on the perception of bus users. The study uses these indicators to suggest optimisations that could be made to improve quality of bus services with the perceptions of current and potential users. 

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Jun-2023

Study aiming to identify passenger profiles using smartcard in Santiago, Chile by studying their recovery post pandemic. The study identified two groups: those who returned to their pre-pandemic patterns, and those who adapted their mobility patterns. 

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May-2022

Study intends to improve short -term forecasting of public transport demand, using AI-based deep learning models for prediction of bus passenger demands based on real patronage data obtained from the smartcard ticketing system in Melbourne. Study found that the models were able to predict passenger demand with over 90% accuracy.

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May-2019

Study makes the case against policies of austerity and competition which have been applied to the UK's local public transport systems. The study urges for greater coordination within the sector to tackle social, environmental, financial and sustainability issues.  

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Apr-2016

Study exploring the ways people make use of their time whilst on buses, and how the knowledge can be used to make bus travel more appealing and drive modal shift away from private cars. Study combines a 840 person survey of bus users in Bristol, with an analysis of online discussions and focus groups. 

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Aug-2023

Study conducts systematic review into the use of smart card data for analysing mobility patterns, noting that clustering and segmentation techniques have been adapted to conduct market segmentation and analyse urban activity locations.

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Feb-2020

Study into what bus passengers consider to be important for improving buses. Key areas were: Buses arriving on time, buses going to more places, value for money, faster journey times, tackling antisocial behaviour and improving passenger information. 

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Apr-2022

Study investigates ticket inspection preferences and identified factors that may influence a user's likelihood of accepting "seamless" ticket inspection. Study found that, given the five inspection options, women and young people selected "seamless ticket inspection". The study recommends further research on aspects surrounding "seamless ticket inspection".

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Mar-2016

Study evaluating existing literature on focused on factors influencing mode changes towards an integrated public transport system. Study highlights the importance of effective transfers for integrated transport, as a smoother transfer between modes should improve the service. The study notes the lack of existing literature on the psychological aspect of transfers and cites this as a major shortcoming when attempting to improve the transfer experience. 

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Sep-2024

Study investigates the potential for electronic payment systems already used in domestic flight to be used on public transport systems. The study uses the Technology Acceptance Model to determine levels of passenger acceptance. 

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Aug-2024

Study makes use of ethnographic observation, semi-structured interviews, and the ALARA model of information search to examine consumer engagement with mobile payment apps in Lagos, Nigeria. Study finds that cultural preferences and trust in traditional payment systems significantly impact willingness to adopt mobile apps. The study recommends an inclusive technological strategy, developing accessible information channels and user-friendly design features, engaging with users to make continuous improvements to the app and adopting a nuanced understanding of socio-cultural influences on technology adoption to inform policy and business strategies. 

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Oct-2020

Paper showcases the design and implementation of a mobile payment system for public transport called IOTApass. Paper documents its experimental validation and confirms its feasibility. 

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Apr-2025

Paper introduces  a Machine Learning Influence Flow Analysis framework intended to identify key influencers of public transport usage. Study finds that easy payments, e-ticketing and mobile applications can substantially improve public transport service. Study recommends making use of smart ticketing systems and contactless payments to enable more efficient allocation of resources, resulting in a more streamlined service that encourages increased ridership and improves user satisfaction. 

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