Toronto rideshare drivers spend half of their time on the road without a passenger, data show

Toronto rideshare drivers spent 50% of their time without passengers in 2025, up from 40% in 2022, implying a roughly 17% hit to driver wages as pay is tied to engaged time. The number of rideshare vehicles more than doubled from 17,500 to over 35,000 between 2022 and 2025, with 87 million trips and 1.1 billion kilometres driven in 2025. New Ontario platform-worker rules guarantee a $17.60 minimum hourly wage, but only on engaged time, keeping pressure on driver economics and potentially on platform operating dynamics.

Analysis

The important signal is not the headline utilization metric itself, but the operating leverage it implies for the platform model. When drivers spend more time chasing supply than completing trips, the marketplace is effectively taxing labor with empty miles, which raises gross bookings without creating proportional take-rate quality; that is supportive of revenue growth near term, but structurally weakens driver retention and can force higher incentives just to keep supply online. Over months, that usually shows up first in lower marketplace efficiency and then in rising churn costs, not in a clean immediate earnings hit.

For Uber, the more interesting second-order effect is that a worsening driver economics backdrop can widen the gap between urban demand growth and fulfilled supply, especially during peak periods. That tends to improve pricing power at the margin, but only until riders start substituting to transit, personal vehicles, or competing mobility modes if ETA reliability degrades. Lyft has less geographic diversification and less category breadth, so any sustained deterioration in driver economics is a more direct threat to its ability to defend share without margin-sacrificing incentives.

The regulatory backdrop matters more than the wage headline. Pro-rated minimum pay tied to engaged time creates an incentive for platforms to optimize matching algorithms toward denser utilization, but it also embeds a political argument for reclassification and broader labor-cost pass-through if deadheading remains high. The risk is a slow-burn margin compression over 2-4 quarters as cities and labor groups use these data to justify tighter rules, while the upside catalyst would be any app-level improvement in dispatch efficiency or evidence that higher utilization can be achieved without incremental subsidies.

Consensus may be underestimating how much of this is a network-quality issue rather than a pure cost issue. If driver earnings are visibly decaying while driver counts remain elevated, supply can look plentiful right up until it abruptly is not, creating a nonlinear risk to wait times and surge pricing. That makes the near-term earnings risk modest, but the medium-term risk to pricing stability and customer retention more material than the market may be reflecting.

Key Decisions for Investors

• Short LYFT vs long UBER over 1-3 months: Lyft has less route diversity and less ability to absorb utilization shocks; target 10-15% relative underperformance if labor optics keep worsening.
• Buy UBER puts or put spreads 3-6 months out around the next earnings cycle: thesis is not an immediate revenue miss, but rising incentive spend and margin pressure as the company defends supply quality.
• Avoid initiating fresh long exposure to LYFT until driver retention and incentive trends stabilize: the risk/reward is poor if cities keep using utilization data to justify tighter enforcement over the next 2 quarters.
• If already long UBER, hedge with a short-dated call overwrite or collar into policy headlines: the data creates headline risk without necessarily changing same-quarter GMV, so implied vol may be an attractive monetization point.
• Watch for a reversal signal in reported ETA/fulfillment metrics; if platform efficiency improves without higher incentives, cover shorts quickly because the market will rerate the operating leverage story within days.