The visual analytics of big, open public transport data – a framework and pipeline for monitoring system performance in Greater Sydney

Figures & data

Table 1. Visual analytics tasks and use case

Task	Use case
Highlight when (time) and where (space) areas are affected by extreme instances of low levels of transport performance.	Designing and improving the transportation network to better respond to extreme events and disruptions
Describe existing patterns in the performance of public transport across spatial and temporal scales	Maintaining a high level of passenger (customer) level of service.
Compare the performance of specific services before and after certain dates.	Understand the effects of previous policies/plans being implemented – track improvements as a result of changes.
Apply clustering techniques to group high-dimensional data into salient categories	To classify services which have similar levels of performance.

Figure 1. Data processing workflow

Figure 2. Illustration of features of a long-term performance monitoring dashboard integrating current and continuously collected real-time data

Figure 3. Outline of visualisation methods employed in the work

Figure 4. Visualisation concept of PPC

Table 2. Summary of performance variables used for cluster analysis, calculated for every route

Variable	Measurement	Dimensions
Delay	Number of trips: [“Delayed 20 minutes or over”, “Not delayed 20 minutes”]	2
Crowdedness	Number of trips [“Standing room only”, “Few Seats Available”,’ Many Seats Available.’]. 3 variables.	3
Day of week	[Monday – Sunday]. 7 variables.	7
Hour of day	[0–24]. 24 variables.	24
	Total number of dimensions	1008
		(24×7×3×2)

Figure 5. Heatmap – Rail delays (defined as per cent of trips delayed 15 minutes at any part of the journey). Additional heatmap example found in Appendix 2, Figure A2

Figure 6. Heatmap – Bus delays overview (defined as per cent of trips delayed 15 minutes or more at any part of the journey)

Figure 7. Heatmap – Individual average route delays, detailed daily view for Route 370

Figure 8. (top-left) Unfiltered performance point cloud – bus route 370 (top-right) Filtered performance point cloud – delayed trips on bus route 370 (bottom) Performance point cloud – annual rail data

Figure 9. Screenshot of individual day’s rail flow animations developed in DeckGL

Figure 10. View of clustered point cloud

Figure A1. Distribution of delayed trips over 12-month sample – highlighting extreme events

Figure A2. Heat map view – bus crowdedness; Metrobus “M” Routes

Data availability statement

Real-time data used for this study is available as open data through the Transport for NSW open dataportal for future replications. For historically-collected big data sets, the author has published this data as open source through the following reference with documentation. See: Lock, Oliver (2020), “High-volume public transport vehicle locations(rail, bus, ferry and light rail) and performance metrics for Sydney dated from March 2018 to April 2019 (GTFS Real-time)”, Mendeley Data, v1http://dx.doi.org/10.17632/gstfpzg339.1.