Estimating the influence of crowding and travel time variability on accessibility to jobs in a large public transport network using smart card big data

• Published in: Journal of transport geography 2020-05, Vol.85, p.102671-17, Article 102671
• Main Authors: Arbex, Renato, Cunha, Claudio B.
• Format: Article
• Language: English
• Subjects: Access; Accessibility; Automatic vehicle location; Big Data; Case studies; Crowding; Data quality; Decision making; Discomfort; In-vehicle crowding; Indicators; Policy making; Public transport; Public transportation; Smart card data; Smart cards; Time; Transportation networks; Transportation planning; Transportation policy; Travel; Travel time; Travel time reliability
• ISSN: 0966-6923; 1873-1236
• DOI: 10.1016/j.jtrangeo.2020.102671

Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. São Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions. •Smart card big data is explored to reveal spatiotemporal crowding patterns;•Multiple days of data are analyzed to infer travel time variability;•Longitudinal and cross-sectional analyses are done to find regularity in travel patterns;•Crowding discomfort and travel time variability on accessibility metrics are considered;•Policy implications of crowding valuation in accessibility are discussed.