1.
What Does the Recovery
of Demand for Urban
Mobility Look Like Post-
COVID-19?

2.
These exhibits depict the likely recovery of
demand in urban mobility following the COVID-19
outbreak. We analyzed the rebound in three types
of urban environments: Public Transit Metropolises
(densely populated cities where mass transit is
dominant), Bike-Friendly Burgs (where bikes are
well-established), and Car-Centric Cities (where
cars account for 75% of miles traveled).
Based on our survey of 5,000 urban residents in China, the EU,
and the US (along with other data, including real-time usage), we
looked at evolving demand over four phases: Lockdown; Phase 1,
the initial re-opening period; Phase 2, when businesses are
operating fully, but with restrictions; and Phase 3, some 12–18
months after re-opening, before a vaccine is available.
Ultimately, we expect a major shift away from public transit
toward private mobility modes, specifically private cars and
bikes. But the magnitude of the shift will differ across the three
types of cities.
Public Transit Metropolises
(e.g., New York, Paris, Madrid, Beijing)
Public transit stands to lose up to 8–10 percentage points (p.p.) in
modal share, with private cars gaining 4–5 p.p. and bikes up 2–3 p.p.
At the mode level, users will migrate from public transit (down
20%–25%) to bikes (up 30%–35%) and cars (up 5%).
Bike-Friendly Burgs
(e.g., Amsterdam, Copenhagen)
Public transit will likely lose 4–5 p.p. .in modal share, ceding 2-3
p.p. each to private cars and bikes.
At the mode level, this shift would translate into a 20%–25%
drop in public transit use and an increase in private car use
(2%) and bike use (5%–10%).
Car-Centric Cities
(e.g., Los Angeles)
Public transit could lose up to 1–2 p.p. in modal share, yielding
2–3 p.p. to private cars and 1–2 p.p. to bikes.
At the mode level, this shift would represent a drop of 30%–35%
for public transit and gains of 2%–3% in private car use and
20% in bike use.

3.
100
20–25
~30
30–35
~5
~5
~10–15
~60
25–30
~35
7–8
25–30
7–8
~5
25–30
~35
~25
~95–971
35–40
8–10
~30
~35–40
15–20
Exhibit 1 | Public Transit in Public Transit Metropolises Could Lose 8 to 10
Percentage Points of Share to Private Cars and Bikes
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: p.p. = percentage points. This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Total does not add up to 100% owing to impact of reduced international tourism and slight increase in number of employees working from home.
<5
Pre-COVID-19
baseline
Lockdown
<5
Phase 1:
Immediate
post-lockdown
Phase 2:
Full operations
(with restrictions)
Phase 3:
(12–18 months)
Public Transit WalkingPrivate cars Private bikesShared mobility
Overall mobility demand and evolution of modal share
Miles traveled by mode (%) Phase 3 vs. baseline
Private cars
+4 p.p. to +5 p.p.
Private bikes
+2 p.p. to +3 p.p.
Public transportation
–8 p.p. to –10 p.p.

4.
Exhibit 2 | Public Transit Metropolises: How the Mobility Modes Will Rebound
Relative to Each Other
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Some insignificant variations between modes were not modeled in Lockdown phase (such as walking, which was barely affected).
Baseline pre-COVID-19 = 100
Phase 3 vs. baseline
Private bikes
+30% to +35%
Private cars
+5%
Public transportation
–20% to –25%
Shared mobility
0% to –5%
Walking
NO SIGNIFICANT CHANGE
100
0
50
150
100
Pre-COVID-19
baseline
Phase 1 Phase 2 Phase 3Lockdown
Public Transit Walking AllPrivate cars Private bikesShared mobility
~75
~90
~60
~50
~40
~15–20
10–151
130–135
~105
95–97
75–80

5.
100
~95–971
~25
<5
~20
~10–15
~30
~20
~40–45
~20
30–35
~10
<5
~20
10–15
30–35
30–35
<5
<5
30–35
~20
~70
~15
25–30
35–40
Exhibit 3 | In Bike-Friendly Burgs, Covid-19’s Impact Overall Will Be More
Muted, but Public Transit Could Still Lose Share to Cars and Bikes
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: p.p. = percentage points. This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Total does not add up to 100% owing to impact of reduced international tourism and slight increase in number of employees working from home.
Lockdown Phase 1 Phase 2 Phase 3
Public Transit WalkingPrivate cars Shared mobility Private bikes
Overall mobility demand and evolution of modal share
Miles traveled by mode (%) Phase 3 vs. baseline
Private cars
+2 p.p. to +3 p.p.
Private bikes
+2 p.p. to +3 p.p.
Public transportation
–4 p.p. to –5 p.p.
Pre-COVID-19
baseline

6.
Exhibit 4 | Bike-Friendly Burgs: How the Mobility Modes Will Rebound
Relative to Each Other
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Some insignificant variations between modes were not modeled in Lockdown phase (such as walking, which was barely affected).
Baseline pre-COVID-19 = 100
Phase 3 vs. baseline
Private bikes
+5% to +10%
Private cars
+2%
Public transportation
–20% to –25%
Shared mobility
0% to –5%
Walking
NO SIGNIFICANT CHANGE
0
50
150
100
Pre-COVID-19
baseline
Phase 1 Phase 2 Phase 3Lockdown
Public Transit Walking AllPrivate cars Private bikesShared mobility
100
~75
~90
~80
~35
~50
~15–20
10–151
101–102
105–110
95–97
75–80

7.
100
Exhibit 5 | In Car-Centric Cities, Public Transit Could Cede 1–2 Percentage
Points of Share to Private Cars and Bikes
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: p.p. = percentage points. This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Total does not add up to 100% owing to impact of reduced international tourism and slight increase in number of employees working from home.
Lockdown Phase 1 Phase 2 Phase 3
Public Transit WalkingPrivate cars Shared mobility Private bikes
Overall mobility demand and evolution of modal share
Miles traveled by mode (%) Phase 3 vs. baseline
Private cars
+2 p.p. to +3 p.p.
Private bikes
+1 p.p. to +2 p.p.
Public transportation
–1 p.p. to –2 p.p.
Pre-COVID-19
baseline
~10
~75
~5
~5
<5~5
~15
<5
<5
~80
6–7<5~7–8
~10
~55
~75
~98–1001
~80
~10
<5
75–80
~5
~10
6–7

8.
Exhibit 6 | Car-Centric Cities: How the Mobility Modes Will Rebound Relative
to Each Other
Sources: BCG consumer survey, “COVID-19 Impact on Urban Mobility” (n = 5,000 urban residents in China, the EU, and the US); business press.
Note: This analysis represents only potential scenarios based on discrete data from one point in time. It is not intended as a prediction or forecast, and the situation is changing daily.
1
Some insignificant variations between modes were not modeled in Lockdown phase (such as walking, which was barely affected).
Baseline pre-COVID-19 = 100
Phase 3 vs. baseline
Private bikes
+20%
Private cars
+2% to 3%
Public transportation
–30% to –35%
Shared mobility
0% to –5%
Walking
NO SIGNIFICANT CHANGE
0
50
150
100
Pre-COVID-19
baseline
Phase 1 Phase 2 Phase 3Lockdown
Public Transit Walking AllPrivate cars Private bikesShared mobility
100
~75
~90
~80
~40
~60
~20
~151
102–103
~120
98–100
65–70