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

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

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.

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/

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

What we know about the use of public transport in space

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

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%

Notable issues in current planning responses

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%

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.

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

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

Opportunities presented by Uber movement

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.

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

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

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

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.

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.

Concluding remarks

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.

End of Presentation