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Using Uber Transactional Data to Analyze Spatial Transformation and its Implications for Transportation Planning

This presentation explores the use of Uber transactional data, specifically Uber Movement, to showcase how the use of space is changing and its implications for the spatial transformation agenda. It highlights notable planning issues and discusses how Movement data can address planning gaps and assumptions. It also discusses the potential opportunities presented by Uber Movement and provides practical planning examples.

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Using Uber Transactional Data to Analyze Spatial Transformation and its Implications for Transportation Planning

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  1. USING UBER TRANSACTIONAL DATA TO SHOWCASE HOW THE USE OF SPACE IS CHANGING AND ITS IMPLICATIONS FOR THE SPATIAL TRANSFORMATION AGENDA 38th Southern African Transport Conference 8 July 2019 By Muzi Nkosi

  2. About the presentation • What we know about the use of public transport in space • Notable planning issues • Exploring Uber Movement data and implications on spatial planning • Concluding remarks

  3. About the presentation • Uber has released its transactional data to help cities to plan: “Movement”. • The presentation explores the use of Movement data to show how some of the planning gaps and assumptions could be addressed.

  4. Overview of Uber Movement • Movement shares historical, aggregated, and anonymized data. This form of data provides cities with insights, mitigates business competition and privacy concerns, and scales globally. https://movement.uber.com/

  5. Next is “Uber Speed” • Gives historical actuals of speeds at an hourly basis. • Historically expensive and difficult to collect for cities, this data is a powerful way to measure the impact of infrastructure investments. • Launched in Nairobi June 2019 and next is SA before the end of 2019

  6. What we know about the use of public transport in space

  7. Travel mode split in Gauteng Province Morning peak-period trips by travel mode

  8. Trip purposes by mode of transport

  9. Notable issues in current planning responses

  10. Network coverage required to maximise access – but does not guaranteed mode shift

  11. Space-time-cost • Public transport use is both a function of space and time. • While there could be coverage in terms of routes, the service might be such that specific times of the day there are no services. • Fare structure may also limit access.

  12. Issues raised for not using public transport Reasons for not using trains Reasons for not using taxis Reasons for not using Bus

  13. Other public transport user issues Even where there are users, issues of reliability and access feature significantly

  14. Opportunities presented by Uber movement

  15. Opportunities • Relatively inexpensive dataset. • Available for different times of the day, day of week, month or year. • Already built-in linkages with cities' transport zones, in the case of strategic transport models.

  16. Uber use in space

  17. Many-to-many, Many-to-one, Short headways SOWETO (app opens)

  18. Feedback Recalibrate Recommendations Transport masterplan study Travel demand model Performance matrix Guidelines and regulation documents

  19. Practical planning example • Quick assessment (without any model) • In this example By creating free flow conditions you are able to improve travel time by 15 min.

  20. Other examples Coping mechanisms Climate change related inputs

  21. Concluding remarks

  22. Concluding remarks • Future “integrated transport plans” will be done inexpensively using probe data such as Uber transactions. • We need to start refining our planning documents to reflect this. • Assumptions we make about how people use space, and static service designs, will be a thing of the past.

  23. End of Presentation

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