Planning and Transport Research Centre Pulse of Perth: SmartRider Data Visualisation Submitted to RACWA Holdings Pty Ltd (“RAC”) Prepared by Tristan Reed, Yan Ji, Chao Sun and Sharon Biermann Date 21 March 2019 Version Final Report to Client (Revision 2) TABLE OF CONTENTS 1 Introduction ................................................................................................................................................ 3 1.1 Report Purpose ................................................................................................................................... 3 1.2 Project Brief ........................................................................................................................................ 3 1.3 Project Tasks ....................................................................................................................................... 3 2 Methodology ............................................................................................................................................... 4 2.1 Datasets Analysed ............................................................................................................................... 4 2.1.1 SmartRider Transactional Dataset .................................................................................................. 4 2.1.2 Bus ‘GPS Log’ Dataset .................................................................................................................... 5 2.1.3 Public Transport Schedule Dataset ................................................................................................ 5 2.1.4 OpenStreetMap ............................................................................................................................. 6 2.1.5 Bus and Train Capacity Data........................................................................................................... 6 2.2 Software ToolIng ................................................................................................................................. 7 2.2.1 Python / Pandas / GeoPandas ........................................................................................................ 7 2.2.2 JavaScript / D3.js ............................................................................................................................ 7 2.2.3 Processing Toolkit .......................................................................................................................... 8 2.2.4 QGIS ............................................................................................................................................... 8 2.2.5 Adobe Creative Cloud .................................................................................................................... 8 2.3 Assumptions........................................................................................................................................ 8 2.3.1 Animated Visualisation (Task 1&2) ................................................................................................ 8 2.3.2 Static Map and Interactive Visualisation (Tasks 3&4) .................................................................. 11 2.4 Aesthetic Considerations ................................................................................................................. 12 3 Results ....................................................................................................................................................... 13 3.1 System-Wide Metrics ........................................................................................................................ 13 3.2 Boardings and Alightings at Stops and Stations ................................................................................ 16 3.3 Patronage per Individual PT service .................................................................................................. 17 3.4 Journeys by Origin and Destination .................................................................................................. 21 3.5 Bus Schedule Adherence ................................................................................................................... 24 4 Conclusion ................................................................................................................................................. 30 5 Acknowledgements ................................................................................................................................... 30 Appendix Data Tables ............................................................................................................................ 31 Pulse of Perth: SmartRider Data Visualisation Page | 2 1 INTRODUCTION 1.1 REPORT PURPOSE This explanatory report accompanies the ‘Pulse of Perth’ animated visualisations, interactive visualisation and static maps commissioned by RAC. The report summarises the methodologies used, key findings and the results of analyses completed in the process of generating the visualisations and maps. 1.2 PROJECT BRIEF The ‘Pulse of Perth’ project aims to visualise and summarise complex public transport (PT) datasets, including the SmartRider transactional dataset, in order to better understand PT travel patterns and network constraints within the Perth metropolitan and Peel regions. RAC identified four specific objectives of the project: 1. How demand varies across the PT networks (bus, train and ferry) during a typical weekday; 2. Areas across the PT networks where demand is approaching or is at capacity of the line, route or service (“patronage congestion hotspots”) – focusing on routes with high demand and/or high frequency corridors (those with bus services every 15 minutes throughout the day); 3. Constraints on the bus network, where bus-bunching, delays or reliability issues are occurring and bus priority measures may be beneficial – focusing on high frequency corridors; and 4. Catchments for train stations to help inform planning for bus feeder services, etc. (this needed to consider patrons’ registered home postcode and whether or not bus services have been used to access train stations). The visualisation tasks detailed below accomplish the first three objectives above. The fourth objective of this project was unable to be completed, as the data required to complete the analysis was not available to the project team. 1.3 PROJECT TASKS To accomplish these objectives, the following four visualisation tasks were undertaken: Task 1: Animated visualisation of boardings and alightings at stops and stations; Task 2: Animated visualisation of patronage per individual public transport service; Pulse of Perth: SmartRider Data Visualisation Page | 3 Task 3: Interactive visualisation of journeys by origin and destination; and Task 4: Static map of bus schedule adherence. 2 METHODOLOGY The methodology used in this project is detailed below. Broadly, the methodology consists of acquiring and processing a range of PT datasets using a range of software tools. Most of the software tools required custom programming or configuration to enable the analysis of each dataset to achieve the objectives of the project. Specific issues relating to each task identified above will be discussed below. 2.1 DATASETS ANALYSED This section documents the datasets used by the project team. Data has been aggregated and averaged to derive patterns of a ‘typical’ weekday, then analysed and presented using static maps, interactive visualisations and animated visualisations, thereby allowing easier interpretation of PT movements across the PT networks. 2.1.1 SMARTRIDER TRANSACTIONAL DATASET To be consistent with Public Transport Authority (PTA) reporting periods, the SmartRider transactional data for the last week of October 2017 (October 23 – October 27) was used for visualisation. The client has also been provided with analyses completed for the last week of October 2016 (October 24 – October 28) where possible. In this report, a trip is defined as boarding and alighting of a PT service. The dataset details each ‘tag on’ transaction (where a user taps their SmartRider card against a reader) and its associated ‘tag off’ transaction – which usually defines a ‘trip’. However, if the user has transferred rail services between ‘tag on’ and ‘tag off’, multiple trips have been made in between. This has been interpolated in the source dataset from the PTA to recover those individual trips so that one trip always corresponds to the boarding and alighting of a service. This interpolation assumes an optimal journey between two railway stations; in some cases, the passenger may not take the quickest or most direct route and as such the interpolated ‘trips’ in the dataset may not be truly accurate to reality. However, it would not be logical for the passenger to do so, hence it is estimated that only a very small number of journeys are affected. For each trip, a variety of attributes are provided. Of note are those used in this project, including the ‘tag on’ location and time, as well as those for the ‘tag off’. A unique identifier is used to group trips into ‘journeys’ – that is, a set of trips within two hours (for journeys consisting of trips within Zones 1-4) or three hours (for Zones 5-9). These journeys are roughly comparable to the sum of people travelling between an origin and destination on the PT system, but not always so (such as Pulse of Perth: SmartRider Data Visualisation Page | 4 when a person returns to their