Do Protected Bike Lanes Reduce Crash Risk? Count the Riders First

TL;DR;

• More crashes after a bike lane opens can coexist with lower risk per rider if cycling grows faster.
• London’s segregated Cycle Superhighway segments increased cycling 48% without a significant increase in exposure-normalized collision risk; non-segregated segments became riskier.¹
• After CS2 was upgraded with segregation, cycling rose 25%, raw collisions rose 22%, and normalized risk fell 5%.¹
• Toronto’s six cycle tracks doubled crude cyclist–motor-vehicle collisions, yet exposure-adjusted risk fell 38%.²
• Exposure adjustment is essential, but it cannot fix poor intersection design, police under-reporting, weak controls, or pooled injury severities.

The same bike lane can look safer and more dangerous

Suppose a street records 10 bicycle crashes in a year. A protected lane opens, riding doubles, and the street records 15 crashes the next year.

Did safety get worse? The public health burden rose from 10 crashes to 15. But the hypothetical risk per rider fell by 25%. Both statements are mathematically true. Neither, by itself, tells us whether the lane caused the change.

That denominator problem haunts bike-lane evaluations. Infrastructure can draw existing riders from parallel streets, generate new trips, and attract people who would not have ridden in mixed traffic.¹² Counting crashes without counting cycling therefore penalizes successful routes for being used. Simply dividing crashes by riders is better, but it can still mislead when crash counts do not rise in direct proportion to bicycle volume—a recurring pattern known as safety in numbers.³⁴

A 2025 study of London’s Cycle Superhighways offers a more careful way through the problem. Rather than asking only whether crashes rose, Anupriya and colleagues compared observed collisions with the number their model expected at the new level of cycling. Their results do not support the slogan that every facility called a “cycle route” is safer. They support a narrower and more actionable conclusion: physical segregation helped London absorb a large increase in cycling without increasing risk beyond what that exposure predicted, while painted and shared-lane versions performed poorly.¹

“Safer” hides three different questions

Before comparing studies, decide which outcome matters:

1. Total collisions: How many police-reported crashes occurred on the corridor? This measures part of the total harm but is strongly affected by how many people use the route.
2. Risk per unit of exposure: How many crashes occurred per trip, kilometre, hour, or intersection entry? This is closer to the risk faced by an individual rider.
3. Injury severity: How many people were killed or seriously injured? A design that trades high-energy motor-vehicle impacts for occasional low-speed falls may not reduce every crash equally, yet it can still prevent the outcomes a Vision Zero policy should care about most.

These outcomes can move in different directions. They also use different data. Police databases capture reported injury collisions; hospital studies capture injured patients, including some falls without a motor vehicle; app data can estimate distance ridden but may overrepresent particular kinds of cyclists.⁵⁶ The UK Department for Transport explicitly warns that many non-fatal casualties never enter police data, even though fatalities are recorded much more completely.⁷

What the London study actually did

London began opening Cycle Superhighways in 2010. The name covered very different streets: some routes were largely blue paint, bus-lane sharing, traffic calming, and signs; CS3 was mostly a segregated two-way track; CS5 and CS6 were fully segregated; and CS2 and CS7 received substantial protective upgrades years after opening.¹ Treating all of them as one intervention would hide the design feature most likely to matter.

The researchers assembled a panel from 2000 through 2019 using Britain’s STATS19 police collision records, Department for Transport traffic counts, Transport for London cycleway geography, and neighborhood data. They classified 111 road segments within 0.5 kilometres of an operating Cycle Superhighway as treated and selected 584 potential control segments more than 1.5 kilometres from a route.¹

They then used two approaches:

• Propensity-score-matched difference-in-differences matched treated and control roads on pre-project bicycle and motor-vehicle volume, prior crashes, deprivation, accessibility, and distance, then compared how their outcomes changed over time.
• Panel outcome regression with road and year fixed effects estimated the change while accounting for stable differences between streets and citywide year-to-year shocks.

Both are attempts to estimate the counterfactual: what would likely have happened on the treated streets without the Cycle Superhighway. That is stronger than a simple before-and-after snapshot, but it is not randomization. The causal interpretation still relies on matched roads having comparable untreated trends and on there being no important, unmeasured factor that changed differently at treated sites.¹

Why dividing by bicycle count was not enough

The usual exposure rate divides annual crashes by annual average daily bicycle volume. That assumes twice as many riders should produce twice as many collisions. Yet Jacobsen’s foundational safety-in-numbers analysis and an updated meta-analysis found a sublinear relationship in many settings: as walking or cycling volume rises, collisions commonly rise by a smaller proportion.³⁴

That pattern could reflect drivers expecting riders, lower motor-vehicle speeds, infrastructure and land use that support both safety and cycling, or several mechanisms at once. It does not prove that adding riders alone causes protection. It does mean a linear denominator can make any high-volume route look artificially good.

The London team instead fitted a flexible curve between pre-treatment bicycle volume and collisions. Their normalized collision rate divided observed collisions by the number expected from that curve and other covariates. A value of 1 means the segment experienced the modeled number of collisions; above 1 means more than expected; below 1 means fewer.¹

Return to the hypothetical street. After cycling doubles, 15 crashes may look better than 20 under a linear rate. But if comparable streets at that volume are expected to have 14 crashes because of the nonlinear exposure relationship, the normalized rate is 15 ÷ 14, or 1.07: slightly worse than expected. The model asks a harder question than “did crashes grow slower than ridership?”

What happened when London separated the lanes

At the aggregate level, the Cycle Superhighways increased cycling, raw collisions, and normalized collision risk. That average is not a verdict on protected lanes because the early network included long stretches of paint and shared bus lanes. Splitting the routes by design changed the finding.¹

All percentages are the study’s propensity-score-matched difference-in-differences estimates; “no significant change” means the analysis could not distinguish the estimate from zero at the 95% confidence level.¹

This table is why a raw count is not enough. Segregated segments recorded about 36% more collisions after intervention, which sounds alarming in isolation. But bicycle volume grew about 48%, and the normalized rate did not significantly depart from what that exposure predicted. On CS2, the raw number also rose after physical protection was added, yet normalized risk fell 5%.

The researchers hypothesized that early non-segregated routes attracted inexperienced cyclists who had difficulty at junctions. They found a temporary increase in single-cycle incidents at junctions and used that as a proxy for inexperience.¹ It is an interesting explanation, not a measured fact: the dataset contained collisions, not riders’ histories. Route-switching, construction details, or unmeasured street changes could also contribute.

Other exposure-aware studies point in the same direction—with caveats

London is not an isolated result. The strongest supporting studies use different exposure measures and designs, which is useful because their weaknesses do not all line up.

The studies do not justify one universal crash-reduction percentage. They do show why “protected bike lane” is too broad a treatment label. Height, continuity, parking position, driveway frequency, direction of travel, signals, and the way the track crosses junctions can matter as much as the barrier’s presence.

They also show the repeated split between segments and intersections. Toronto found three-quarters of its cyclist–motor-vehicle collisions at intersections.² Atlanta’s point estimates favored protected lanes between junctions but not on entry to them.⁵ For the geometry and signal treatments that continue protection through the corner, see Why Your Bike Lane Ends at Every Intersection. Our article on right-hook crashes examines the specific turning conflict; this article’s point is methodological: a safe mid-block average can conceal a concentrated junction problem.

Five ways a bike-lane safety result can fool you

1. The project was built where crashes had just spiked

Cities often prioritize a street after an unusually bad run of crashes. Some decline would likely follow by chance as the count returns toward its long-run average—a phenomenon called regression to the mean. A naïve before-and-after study credits the project for all of that decline. Empirical Bayes methods and credible comparison roads help separate treatment from random fluctuation.¹⁰

2. Riders changed routes

A protected corridor may pull riders from parallel streets. The treated street’s count can rise even if citywide cycling does not; nearby crashes can fall because exposure moved. That is not failure—the safer route is doing transportation work—but a corridor-only analysis cannot tell whether total trips grew or risk merely relocated. Toronto found a 35% decline in collision rates on streets 151 to 550 metres from its new tracks, suggesting a possible area effect rather than simple displacement.²

3. “Exposure” was only a convenient proxy

The best denominator depends on the conflict. Distance ridden is useful along segments; the number of junction entries is more relevant at intersections; time may matter for surface-related falls. London used annual average daily bicycle counts at fixed points and associated nearby crashes within 0.4 kilometres.¹ That is far better than no denominator, but it is not a direct census of trips or kilometres on every segment.

4. Minor crashes and fatal crashes were pooled

The London outcome counted collisions rather than estimating separate effects on fatal and serious injuries.¹ Police data are strongest for the most severe events and incomplete for lesser ones; hospital data capture a different slice, including falls.⁷⁶ A mature evaluation should report both total injuries and killed-or-seriously-injured outcomes over enough years to handle their rarity.

5. The average erased the design

London’s all-route result mixed quiet streets, paint, bus-lane sharing, partial segregation, and continuous tracks. The useful finding appeared only after the researchers separated them. Calgary’s 18-month network pilot ran into the same time-and-exposure problem: ridership rose quickly, but one year of crash data could not establish a stable change in risk, as discussed in our Calgary cycle-track evaluation.

A better scorecard for the next project

When a city announces that crashes rose—or fell—after a protected lane opened, ask for five numbers:

1. Bicycle trips or distance before and after, collected in comparable seasons and locations.
2. A control or counterfactual, not only last year’s crash count.
3. Results split between intersections and segments, with driveway and junction designs described.
4. Fatal and serious injuries reported separately from all recorded collisions.
5. Network-wide and nearby-street effects, so route shifts are not mistaken for new trips or displaced danger.

Then ask what was actually built. Flexible posts beside a two-way lane crossing frequent driveways, a curb-separated one-way track with protected signals, and blue paint in a bus lane are not interchangeable treatments.

The honest answer

Do protected bike lanes reduce crash risk? The balance of exposure-aware evidence says well-designed, physically separated lanes can reduce injury risk or accommodate substantial ridership growth without a proportional increase in crashes.¹²⁸⁹⁵⁶ London’s strongest within-route result—the CS2 segregation upgrade—found a statistically significant but modest 5% reduction in normalized risk, not a miracle.¹

The lesson is neither “crashes went up, so remove the lane” nor “ridership went up, so every design is safe.” Count the riders. Model what comparable streets would have done. Preserve the distinction between total harm, individual risk, and severity. And follow the protection through the intersection, where an impressive corridor average can still come apart.

FAQ

Q1. Can bicycle crashes increase after a protected bike lane opens even if the lane is safer?
A. Yes. Toronto’s raw cyclist–vehicle collisions doubled after six tracks opened, but its exposure-adjusted rate fell 38%.²

Q2. What is an exposure-normalized bicycle collision rate?
A. It compares observed crashes with the number expected for the measured cycling volume, accounting for their nonlinear relationship.¹

Q3. What did the London Cycle Superhighways study find about protected lanes?
A. Segregated segments carried 48% more cycling without significantly greater normalized risk; CS2’s segregation upgrade reduced it about 5%.¹

Q4. Why can protected bike lanes still be dangerous at intersections?
A. Turning drivers and cross traffic still intersect riders; setbacks, slow turns, clear priority, and protected signal phases manage those conflicts.²⁵

Q5. Does safety in numbers prove that adding more cyclists makes each cyclist safer?
A. No. The relationship may reflect infrastructure, speeds, land use, selection, or driver behavior as well as rider numbers.³⁴

References