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

Based on a survey of 5,000 residents in china, the EU, and the US, BCG analyzed the likely recovery of demand in urban mobility following the COVID-19
outbreak. Ultimately—until a cure emerges—we expect 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 varied type of cities.

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

  1. 1. What Does the Recovery of Demand for Urban Mobility Look Like Post- COVID-19?
  2. 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. 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. 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. 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. 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. 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. 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

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