Alejandro Molnar, World Bank

1. Mobility, data & regulation
in urban transportation
Alejandro Molnar
Infra4Dev @ Toulouse
December 11, 2019

2. New transportation modes, data and policy choices
Transportation alternatives
Rideshare. Cars, vans, motorcycles
(e.g., Grab, Angkas), tuk-tuk.
Carshare
Rideshare w/ car pooling. (e.g., Via,
UberPool, LyftLine, GrabHitch) or
bus (e.g. UberBus, Swvl).
“New mobility,” docked or dock-
less. Motorcycles, bikes, e-bikes,
scooters.
E-bikes. Electric vehicles.
Smart parking meters.
Traditional: BRTs, rail, metro.
Future (Sci Fi?): Autonomous
vehicles.
Data
“Big” data on individual location
- Call Detail Record (CDR),
- App-based location services,
- New mobility, private data.
Stats from probe/telemetry data.
e.g. Uber Street Speeds, Waze,
Google Maps, processed taxi data.
Old data + “Deep Learning.”
- CCTV: count cars, trucks, pedestr.
- Satellite: car counts (e.g., Orbital
Insights). Car radar.
Vehicle ID or sensor. RFID (NYC
EZ-pass, Singapore ERP), Bluetooth
Cheaper IT: e.g., on data from
turnstiles, bus probes, sensors on
highways, and administrative data,
e.g., from toll agencies.
Policy choices
Road dieting. How many bus
lanes, BRT lanes, bike lanes.
Parking spots and curb zoning.
Pricing congestion. Road by
segment and time of day (e.g.
Singapore ERP2). Curb access.
Pricing of transit, parking,
carbon, and local pollution.
Regulation. Entry, wage,
licensing, infrastructure use,
transit quality and frequency.
Mobility as a Service. A pricing
and behavioral tool.
Evaluate existing quota rents.

3. Traffic congestion & the role of ridehail in New York City
1. New York City traffic slowed down by 15-20% over three years, 2013-2016
We provide new descriptive evidence from taxi speed and EZ-Pass data.
2. Provide causal estimates of an empirical congestion externality curve
• Build retrospective street speed data 2009-2016 from taxi trip records
• Exploit quasi-experimental shock to quantity of vehicles on the road, from a taxi
deregulation episode
3. Accounting exercise on the impact of ridehail
• Measure change in vehicles on the road
• Use aerial imagery plus car counting
• For-hire-vehicle activity grew 223% in Midtown
• We find ridehail can account for (at least) 62% of slowdown in Midtown NYC.

4. #3. E-commerce delivery: effect of online sale events on
traffic speed
Note: CCTV feeds are not fully digitized yet, but we
anecdotally find:
• Double parking regularly exceeds 30 minutes,
parking events lasting up to 4 hours
• Delivery vehicle using the curb to deliver from the
same location for over 7 hours.
Demand for infrastructure, e.g. in New York City
#2. Bike lane infrastructure: effects of new lanes
on traffic speed and cycling
As bike lane infrastructure is deployed,
measure impact at a local scale.
Requires: long run time series of traffic speed,
transportation mode choices with spatial
resolution.

5. #3. E-commerce delivery: effect of online sale events on
traffic speed.
Note: CCTV feeds are not fully digitized yet, but we
anecdotally find:
• Double parking regularly exceeds 30 minutes,
parking events lasting up to 4 hours
• Delivery vehicle using the curb to deliver from the
same location for over 7 hours.
Demand for infrastructure, e.g. in New York City
#2. Bike lane infrastructure: effects of new lanes
on traffic speed and cycling.
As bike lane infrastructure is deployed,
measure impact at a local scale.
Requires: long run time series of traffic speed,
transportation mode choices with spatial
resolution.

6. Policy options. What do we need to know?
Congestion pricing
• Deadweight loss may be low (Kreindler, 2018; Akbar and Duranton, 2017).
• Transferability across cities depends on substitution alternatives, how infrastructure congests.
• Demand for alternatives, e.g. i) transit, ii) technology-enabled pooling, iii) new mobility.
Second-best policies
• Quotas: zero profit condition for the ridehail entrant/platform do not internalize business-stealing,
fixed cost includes congestion externalities from search.
• Public transit attributes: enforced pooling, “coarsening” of stops on the city grid.
How are cities different, and what lessons will transfer?
- Cost of capital vs. labor on supply side
- Value of time, existing infrastructure, density
- Transit vs. car oriented development goals
- Governance and enforcement capabilities

7. Regulation challenges
• New York City: ridehail campaigned directly to consumers and against regulators to prevent
caps and data disclosure).
Uber lobbyist: “How much money do I have to spend and is there anything I can’t do?”
Former Uber CEO Travis Kalanick: “Whatever you need. And do whatever you need to do.”
Uber lobbyist: “And he meant it. We ran $4 million in TV spots. We did radio ads. We did direct mail. We had digital
ads. We mobilized our customers, over 100,000 of them, either e-mailed or tweeted at City Hall or the city council.
We had five different lobbying firms. We worked every single editorial board in the city. We worked the columnists
and the pundits. We met with community leaders, clergy. We had rallies with our drivers. It was just an all-in, 24-
hour-a-day campaign.”
• Austin, TX: ridehail sponsored a proposition to prevent driver fingerprinting. The proposition
was rejected by Austin voters. Lyft & Uber withdrew from market, and only returned after
successfully lobbying state government to prevent city regulation. Driver ID at issue in London.
• Taxi is a public transportation mode: e.g., in NYC fare is regulated and there is a statutory
requirement to transport to all boroughs.
• NYC regulators still do not know the price at which ridehail is provided, or payment to drivers.
• Technology is in place to approach first best congestion pricing on rideshare; Second best tools
required to equate taxation on private drivers (e.g., a parking tax).

8. • Data feeds often provided live, aimed to developers, but not archived.
• NYC DOT camera feeds, EZ-Pass traffic feeds.
• Storage cost is not prohibitive, but cities need to see the “option value” of storage.
• Data has privacy implications, both for passengers and drivers.
• NYC taxi regulator (TLC) has breadcrumb data, but does not release any version due to privacy concerns.
• Current anonymization solution discards valuable spatial coordinates and vehicle IDs.
• On road infrastructure: “curbside inventory,” routable and inter-operable with open standards.
Some private-public development of standards is underway.
Suggestion: urban transportation research requires a restricted data access model, for cities to retain
control and grant access to new sources of urban data (i.e., a Census RDC for city-level data).
Data requirements, governance and infrastructure

9. Thanks!
alejandromolnar.org
@molnaralejandro
aimolnar@worldbank.org
Taxi paper: bit.ly/am_taxi_paper
Short slides: bit.ly/am_taxi_short
Long slides: bit.ly/am_taxi_long
Delivery paper: bit.ly/am_parcel_paper
Delivery slides: bit.ly/am_parcel_short
Preliminary!