1. 38th Southern African Transport Conference 8 July 2019 By Muzi Nkosi
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.

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
Bicycle
Bus
Car
taxi/minibus taxi
Company transport
Train
Lift club
Metered taxi
eHailing/
Motorcycle
Other
Walk all the way
Not given
Education
43%
81%
21%
30% 2%
39%
52%
56%
29%
47%
78%
12%
Work at usual place
53%
16%
62%
92%
45%
40%
34%
65%
35%
13%
54%
In course of work but not the usual workplace 1% 0%
Looking for work 6%
Medical/health purposes 3%
Recreational
Shopping 4% 5%
To drop someone off/to pick someone up
To go home
10%
Visiting friends/relatives
Worship
28%
Welfare office
Not Given
Grand Total
100%

9. Notable issues in current planning responses

10. Network coverage required to maximise access – but does not guaranteed mode shift
Land use
Percentage
Residential dwelling units
74%
Schooling and work
83%
Medical Facilities
91%
Police station
87%

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 Unreliability Unroadworthy vehicles Crime/Security
Analysis Area
Unreliability
Unroadworthy vehicles
Crime/Security
Reckless driving
Rude drivers and / or passengers
Lack of comfort/crowding/overloading
Expense
Insufficient service on the weekends/at night
Long walk/wait to the nearest stop/station
Other
Ennerdale/Orange Farm
21%
12% 2% 8% 4% 7%
13%
28% 1%
3 699
Soweto
25% 9%
16% 3%
4 495
Beyers Naude Corridor
24% 0%
11% 5%
14 230
Oxford/Rivonia Corridor
32% 6%
19%
12 728
Alexandra
10%
15%
20%
4 374
Ivory Park
17%
35%
4 089
City of Johannesburg
18%
6 860

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

18. Feedback
Recalibrate
Transport masterplan study
Travel demand model
Performance matrix
Recommendations
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
As we bring Movement to more cities around the world, we’re learning more and more ways this data can come in handy. From Cincinnati, to DC, to London, to Nairobi, we’re working with local partners to find new ways to leverage Movement’s data to produce insights that help solve real challenges.

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