4,196
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

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

ORCID Icon, ORCID Icon & ORCID Icon
Pages 134-159 | Received 14 Jan 2020, Accepted 14 Apr 2020, Published online: 08 Jul 2020

References

  • Adnan, M., Longley, P. A., Singleton, A. D., & Brunsdon, C. (2010). Towards real-time geodemographics: Clustering algorithm performance for large multidimensional spatial databases. Transactions in GIS, 14(3), 283–297.
  • Adrienko, N., & Andrienko, G. (2011). Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics, 17(2), 205–219.
  • Andrienko, G., Andrienko, N., Bak, P., Keim, D., & Wrobel, S. (2013). Visual analytics of movement. Berlin, Heidelberg: Springer Science & Business Media.
  • Andrienko, G., Andrienko, N., Chen, W., Maciejewski, R., & Zhao, Y. (2017). Visual analytics of mobility and transportation: State of the art and further research directions. IEEE Transactions on Intelligent Transportation Systems, 18(8), 2232–2249.
  • Andrienko, N., & Andrienko, G. (2007). Designing visual analytics methods for massive collections of movement data. Cartographica, 42(2), 117–138.
  • Anwar, A., Odoni, A., & Toh, N. (2016). BusViz. Transportation Research Record: Journal of the Transportation Research Board, 2544(2544), 102–109.
  • Atluri, G., Karpatne, A., & Kumar, V. (2018). Spatio-temporal data mining: A survey of problems and methods. ACM Computing Surveys, 51(4), 1–37.
  • AUSTLII. (2011). Transport Legislation Amendment Act 2011 No 41. Retrieved from http://classic.austlii.edu.au/au/legis/nsw/num_act/tlaa2011n41372.pdf
  • Australian Bureau of Statistics. (2018). 3222.0 - Population Projections, Australia, 2017 (base) - 2066. Canberra. Retrieved from https://www.abs.gov.au/
  • Bast, H. (2014). Real-time movement visualization of public transit data. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, Texas.
  • Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279.
  • Birant, D., & Kut, A. (2007). ST-DBSCAN: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering, 60(1), 208–221.
  • Bureau of Meteorology. (2018). December 2018 - Weather Archive. Sydney. Retrieved from http://www.bom.gov.au/climate/current/month/nsw/archive/201812.sydney.shtml
  • Cao, N., Lin, C., Zhu, Q., Lin, Y. R., Teng, X., & Wen, X. (2018). Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data. IEEE Transactions on Visualization and Computer Graphics, 24(1), 23–33.
  • Cerreto, F., Nielsen, B. F., Nielsen, O. A., & Harrod, S. S. (2018). Application of data clustering to railway delay pattern recognition. Journal of Advanced Transportation, (2018, 1–18.
  • Cheng, T., & Adepeju, M. (2013). Detecting emerging space-time crime patterns by prospective STSS. Geocomputation, (77), 4.
  • Conveyal. (2019). Conveyal Access Analyst. Retrieved from https://www.conveyal.com/
  • Crane, J., & Rucks, G. (2016). A consortium approach to transit data interoperability. Retrieved from http://www.rmi.org/Consortium_Approach_ITD
  • DOIRDaC. (2019). Delivering the right infrastructure for a growing nation. Canberra. Retrieved from https://investment.infrastructure.gov.au/files/budget-2019-20/Building-Our-Future-Delivering-the-Right-Infrastructure-for-a-Growing-Nation-2019.pdf
  • Engin, Z., Dijk, J. V., Lan, T., Longley, P. A., Treleaven, P., Batty, M., & Penn, A. (2019). Data-driven urban management : Mapping the landscape. Journal of Urban Management, (May), 1. doi:10.1016/j.jum.2019.12.001
  • Erhardt, G. D. (2016). Fusion of large continuously collected data sources : Understanding travel demand trends and measuring transport project impacts. Retrieved from http://discovery.ucl.ac.uk/1505994/1/Erhardt_Thesis-Final.pdf
  • Erhardt, G. D., Lock, O., Arcaute, E., & Batty, M. (2017). A big data mashing tool for measuring transit system performance. Seeing cities through big data (pp. 257–278). doi:10.1007/978-3-319-40902-3_15
  • Faroqi, H., Mesbah, M., Kim, J., & Tavassoli, A. (2018). A model for measuring activity similarity between public transit passengers using smart card data. Travel Behaviour and Society, 13, 11–25.
  • Ferreira, N., Poco, J., Vo, H. T., Freire, J., & Silva, C. T. (2013). Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2149–2158.
  • Filho, J. A. W., Stuerzlinger, W., & Nedel, L. (2019). Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration. IEEE Transactions on Visualization and Computer Graphics, 1. doi:10.1109/tvcg.2019.2934415
  • Fredrikson, A., North, C., Plaisant, C., & Shneiderman, B. (1999). Temporal, geographical and categorical aggregations viewed through coordinated displays: A case study with highway incident data. Proceedings of the 1999 Workshop on New Paradigms in Information Visualization and Manipulation in Conjunction with the 8th ACM Internation Conference on Information and Knowledge Management, NPIVM 1999, 26–34. doi:10.1145/331770.331780
  • Gray, S., O’Brien, O., & Hügel, S. (2016). Collecting and visualizing real-time urban data through city dashboards. Built Environment, 42(3), 498–509.
  • Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258–268.
  • Khronos Group. (2019). WebGL - OpenGL ES for the Web. Retrieved from https://www.khronos.org/webgl/
  • Kitchin, R., Lauriault, T. P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2(1), 6–28.
  • Kraak, M. J., & Kveladze, I. (2017). Narrative of the annotated Space–Time Cube – Revisiting a historical event. Journal of Maps, 13(1), 56–61.
  • Kristensson, P. O., Dahlback, N., Anundi, D., Bjornstad, M., Gillberg, H., Haraldsson, J., … Stahl, J. (2007). The trade-offs with space time cube representation of spatiotemporal patterns, 1–15. Retrieved from http://arxiv.org/abs/0707.1618
  • Lee, S., Kim, S. H., & Kwon, B. C. (2017). VLAT: development of a visualization literacy assessment test. IEEE Transactions on Visualization and Computer Graphics, 23(1), 551–560.
  • Lloyd, S. P. (1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
  • Lock, O. (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. 10.17632/gstfpzg339
  • Lock, O., Bednarz, T., Leao, S. Z., & Pettit, C. (2019). A review and reframing of participatory urban dashboards. City, Culture and Society. doi:10.1016/j.ccs.2019.100294
  • Lock, O., Bednarz, T., & Pettit, C. (2019). HoloCity–exploring the use of augmented reality cityscapes for collaborative understanding of high-volume urban sensor data. In The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry. doi:10.1145/3359997.3365734
  • Lock, O., & Erhardt, G. D. (2015). Keeping track—The fusion of large, automatically collected transport data in capturing long-term system change. In Australian Institute of Traffic Planning and Management (AITPM) National Conference, 2015, Brisbane, Queensland, Australia. https://trid.trb.org/view/1371463
  • Lock, O., Pinnegar, S., Leao, S. Z., & Pettit, C. (2020). The making of a mega-region: evaluating and proposing long-term transport planning strategies with open-source data and transport accessibility tools. In Handbook of Planning Support Science. doi:10.4337/9781788971089.00039
  • Marsden, G., & Bonsall, P. (2006). Performance targets in transport policy. Transport Policy, 13(3), 191–203.
  • MBTA. (2018). MBTA performance dashboard. Retrieved from http://www.mbtabackontrack.com/performance/index.html#/home
  • McKinney, W. (2010). Data structures for statistical computing in Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 51–56). Austin, Texas.
  • Mesbah, M., Currie, G., Lennon, C., & Northcott, T. (2012). Spatial and temporal visualization of transit operations performance data at a network level. Journal of Transport Geography, 25, 15–26.
  • Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223–239.
  • NYC Bus Turnaround Coalition. (2018). Bus Turnaround NYC - Bus report cards. Retrieved from http://busturnaround.nyc/#bus-report-cards
  • Pettit, C., Lieske, S. N., & Jamal, M. (2017). CityDash: visualising a changing city using open data. In S. Geertman, A. Allan, C. Pettit, & J. Stillwell (Eds.), Planning support science for smarter Urban futures (pp. 337–353). Cham: Springer International Publishing. doi:10.1007/978-3-319-57819-4_19
  • Pettit, C., Widjaja, I., Russo, P., Sinnott, R., Stimson, R., & Tomko, M. (2012). Visualisation support for exploring urban space and place. International Society for Photogrammetry and Remote Sensing.
  • Pettit, C. J., Lieske, S. N., & Leao, S. Z. (2016). Big bicycle data processing: From personal data to urban applications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(July), 173–179.
  • Remix. (2018). Remix: How today’s cities design their transportation future. Retrieved from https://www.remix.com/
  • Russo, P., Lanzilotti, R., Costabile, M. F., & Pettit, C. J. (2018). Towards satisfying practitioners in using Planning Support Systems. Computers, Environment and Urban Systems, 67, 9–20.
  • Shneiderman, B. (2003). The eyes have it: A task by data type taxonomy for information visualizations. The craft of information visualization (pp. 364–371). doi:10.1016/B978-155860915-0/50046-9
  • Suchkov, B., Boguslavsky, M., & Reddy, A. (2015). Development of a real-time stringlines tool to visualize subway operations and manage service at New York City transit. Transportation Research Record (Vol. 2538). doi:10.3141/2538-03
  • Thomas, J. J., & Cook, K. A. (2006). Visualization Viewpoints: A Visual Analytics Agenda. IEEE Computer Graphics and Applications, 26(February), 10–13.
  • Tominski, C., Schumann, H., Andrienko, G., & Andrienko, N. (2012). Stacking-Based Visualization of Trajectory Attribute Data.
  • Transport for New South Wales. (2019). TfNSW open data hub and developer portal. Retrieved from https://opendata.transport.nsw.gov.au/
  • Transport for NSW. (2018a). December 2018 - Fleet update. Retrieved from https://www.transport.nsw.gov.au/system/files/media/documents/2018/Fleet-Update-Newsletter-December-2018.pdf
  • Transport for NSW. (2018b). Transit systems boosts Inner West bus services. Retrieved from https://www.transport.nsw.gov.au/news-and-events/media-releases/transit-systems-boosts-inner-west-bus-services
  • Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal Human-Computer Studies Schnotz & Kulhavy, 57, 247–262.
  • Uber. (2019a). deck.gl. Retrieved from https://deck.gl/#/
  • Uber. (2019b). Kepler.GL. Retrieved from https://kepler.gl/#/
  • Waskom, M. (2019). seaborn: Statistical data visualization. Retrieved from https://seaborn.pydata.org/
  • Welch, T. F., & Widita, A. (2019). Big data in public transportation: A review of sources and methods. Transport Reviews, 54–63.