Big data processing and analysis on the impact of COVID-19 on public transport delay

The coronavirus disease 2019 (COVID-19) pandemic that started at the beginning of the year 2020 has significantly disrupted people's daily life around the world. Understanding and quantifying the impact of such a large-scale disruption will help people mitigate the pandemic and enhance the resilience for future preparation of similar events. In this chapter, we present a research work studying the impact of COVID-19 on public transport in terms of bus delay, which involves big data processing and analysis on multisource datasets containing COVID-19 case data, bus GTFS (General Transit Feed Specification) data, and LGA (Local Government Area) boundary data. The datasets in use are heterogeneous, arrive in large volumes and in real time, and have a spatiotemporal distribution, which brings true challenges to this research. To quantify the bus delay changes, we propose a methodology consisting of real-time data crawling, map-matching, arrival time estimation, and bus delay calculation and aggregation. The methodology is applied to a case study focusing on the Sydney metropolitan region across different stages of the COVID-19 pandemic from February to March 2020. The case study shows that during March 2020, the COVID-19 pandemic has significantly impacted people's travel behaviors in Sydney, but the influence varies in different areas. The most affected areas are the central and eastern suburbs, which recorded a drop of 9.5 min of bus delay during afternoon peak hours. The findings are helpful to understand and mitigate the restriction impact in different city areas with different conditions. The quantified delay reduction also reveals the potential of better transport performance, which could be used as a benchmark of transport performance improvement after the pandemic. The main contributions of this work include the methodology to quantify travel behavior changes under large disruptions such as COVID-19 pandemic and the case study on large-scale and long-period travel behavior shift that seldom happened before.