Bike to Work Day 2025
What happens to professional driving networks when Americans commute by bicycle? A full-season study of May 15, 2025, the US national Bike to Work Day.
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The expected result: no fleet-wide signal.
The Nexar fleet is approximately 85% Taxi/Rideshare vehicles operated by professional drivers who do not make personal modal-shift choices. Bike to Work Day primarily affects personal commuters, a segment representing ~3% of the Nexar fleet (Consumer vehicles). The absence of a large national fleet signal is expected and informative, not a null result. Any effects detected are directional indicators only.
On May 15, 2025, an estimated 100,000+ Americans participated in the national Bike to Work Day, the largest annual event promoting bicycle commuting. Nexar recorded 358,419 rides on that day across its connected-camera fleet. This report examines whether that event left a measurable signature in commercial driving patterns, safety metrics, and cyclist detection rates.
No statistically significant fleet-wide impact was detected. This is the expected result. Rideshare and taxi drivers do not choose to bike to work, their vehicle operations are demand-driven, not commuter-driven. The more informative signals lie in the daytime hourly pattern and state-level variation. Using the stricter 6-date pre-event baseline, High G Impact reaches significance (+17.3%, Z=+2.10) but is likely a seasonal confound, not an event signal.
All comparisons in this report use the 6-date pre-event baseline (Apr 3, Apr 10, Apr 17, Apr 24, May 1, May 8, the six Thursdays immediately before the event), avg 367,090 rides/day. This is the closest temporal match to May 15 and avoids post-event seasonality. Note: Taxi/Rideshare shows Z=4.75 (2.3%) which reflects the natural April-to-May seasonal decline in rideshare volumes, not event displacement.
An event for personal commuters, not fleets.
Bike to Work Day (National Bike Month, third Friday of May) is the most widely observed cycling advocacy event in the United States. In 2025, the event fell on May 15. Approximately 100,000 historic annual participants nationally.
| Benchmark | Detail |
|---|---|
| MTC Bay Area 2025 | 16,000 Energizer Station visitors logged 159,204 miles, 33% above the 120K-mile goal |
| MWCOG DC Metro 2025 | Confirmed for May 15, 18,000 T-shirt registration cap, 100+ pit stops |
| PeopleForBikes 2024 | 112M Americans rode bikes in 2024, a 10-year high; 67% of micromobility commuters would otherwise have driven |
| ACS 2022 habitual commuters | 731,272 (0.54% of US workers). High-cycling cities: Davis CA 16.6%, Boulder CO 10.4%, Portland 6.5%, DC & SF ~4% |
If Nexar's fleet reflected personal commuter behavior, we would expect a small but detectable reduction in morning peak riding volume in cycling-positive metros. The critical challenge is that Nexar's fleet composition does not reflect personal commuter behavior: ~85% Taxi/Rideshare (professional, demand-driven), ~15% Consumer and other. The effective personal commuter segment exposed to Bike to Work Day effects represents well under 3% of total fleet rides.
Bike to Work Day is an intentional, geographically concentrated perturbation to commuter modal share. It is the rare case where the question is not "did Americans drive less?" but "does a commuter-modal-shift event register at all in a demand-driven commercial fleet?" The answer sets a useful detection floor for future event studies.
An underpowered, honest design.
Study design. A quasi-experimental before/after study comparing May 15, 2025 to a matched set of Thursdays drawn from a ±6-week window. Thursdays only were used to control for day-of-week variation; Bike to Work Day always falls on a Friday, but the closest available commercial dataset used Thursday as the representative weekday anchor. All comparisons use ride-level aggregate metrics from Nexar's MT_MOD_RISK and MT_MLP BigQuery fact tables, queried at national scope.
Baseline. The Pre-Event 6-Date window (Apr 3 to May 8, avg 367,090 rides) is the closest temporal match and preferred for directional comparison; its limitation is small N (6) and no post-event symmetry. Incident rates are expressed per million rides to normalize for day-to-day variation in total fleet size. Cyclist and pedestrian detections come from the MT_MLP Multi-Label Perception pipeline, which was in early rollout during the study window, so only May 15 and post-event dates can be compared on that metric.
With 6 baseline dates (N=6), the minimum detectable effect at 80% power is approximately ±9% of the mean ride count (~33,000 rides). Real Bike to Work Day effects in a commercial rideshare fleet, if any exist, are expected to be well under 1% nationally (~3,600 rides). This study is fundamentally underpowered to detect the true underlying effect. The null result does not mean the effect is zero; it means the effect, if it exists, is below our detection threshold. City-level analysis would be required to reach adequate power.
Not significantly fewer rides.
The core question: did the Nexar fleet record fewer rides on Bike to Work Day than on comparable Thursdays? The short answer: not significantly. May 15 (358,419 rides) sits −2.4% below the 6-date pre-event average of 367,090, directionally consistent with the Nordback benchmark but not significant (z=−1.50).
| Date | Type | Rides | Hours | Km | Avg Dur (min) | Avg Speed (km/h) |
|---|---|---|---|---|---|---|
| Apr 3 | Baseline | 371,972 | 236,734 | 6,332,101 | 38.19 | 26.75 |
| Apr 10 | Baseline | 373,268 | 240,266 | 6,498,034 | 38.62 | 27.05 |
| Apr 17 | Baseline | 372,824 | 238,206 | 6,550,417 | 38.34 | 27.50 |
| Apr 24 | Baseline | 359,262 | 240,981 | 6,499,918 | 40.25 | 26.97 |
| May 1 | Baseline | 361,090 | 240,141 | 6,593,253 | 39.90 | 27.46 |
| May 8 | Baseline | 364,124 | 241,463 | 6,639,973 | 39.79 | 27.50 |
| 6-Date Avg | Baseline | 367,090 | 239,632 | 6,518,949 | 39.18 | 27.21 |
| May 15 ★ | Event | 358,419 | 238,349 | 6,587,943 | 39.90 | 27.64 |
| vs Baseline Avg | NS | −2.4% (z=−1.50) | −0.5% (z=−0.78) | +1.1% (z=+0.71) | +1.8% | +1.6% (z=+1.47) |
The data shows a clear seasonal decline in ride volume: April averages ~373K rides/Thursday, declining to ~337K by late June. May 15 sits at 358K, consistent with the seasonal trajectory between April's higher volumes and June's lower ones. The pre-event 6-date baseline of 367K captures the most proximate comparator window, avoiding the downward seasonal drag of post-event dates.
An overnight dip, an afternoon that holds.
Querying all rides (no country filter) in UTC time, the event day shows a consistent slight decline in the overnight/early-morning UTC window (UTC 03-08: −4.6% to −6.3%) and is essentially flat during afternoon and evening hours (UTC 13-23: −1.7% to +0.4%). The steepest drop is at UTC 05:00 (−6.3%), US late-evening/overnight in all time zones. Only four hours show marginal positive readings (+0.1% to +0.4%), all within noise. Event-day total: 283,074 rides vs baseline avg 285,860 = −1.0%.
| UTC Hour | Event Rides | Baseline Avg | Rides % | Hours % | Km % |
|---|---|---|---|---|---|
| 00:00 | 14,301 | 14,708 | −2.8% | −5.6% | −2.2% |
| 01:00 | 11,261 | 11,397 | −1.2% | −4.6% | +0.2% |
| 02:00 | 8,383 | 8,535 | −1.8% | +7.9% | +8.1% |
| 03:00 | 6,098 | 6,389 | −4.6% | +2.9% | +0.0% |
| 04:00 | 4,459 | 4,713 | −5.4% | +7.4% | +7.9% |
| 05:00 | 3,272 | 3,492 | −6.3% | −1.5% | +4.1% |
| 06:00 | 2,692 | 2,844 | −5.4% | −3.9% | −4.3% |
| 07:00 | 2,479 | 2,627 | −5.6% | −1.8% | −5.0% |
| 08:00 | 2,950 | 3,125 | −5.6% | −0.8% | +4.0% |
| 09:00 | 4,930 | 4,983 | −1.1% | +6.7% | +0.4% |
| 10:00 | 8,166 | 8,262 | −1.2% | −0.1% | +1.5% |
| 11:00 | 12,000 | 12,123 | −1.0% | +2.8% | +4.1% |
| 12:00 | 13,186 | 13,413 | −1.7% | −4.9% | −6.8% |
| 13:00 | 13,237 | 13,285 | −0.4% | +1.2% | −0.5% |
| 14:00 | 14,392 | 14,454 | −0.4% | +3.4% | +3.4% |
| 15:00 | 15,369 | 15,306 | +0.4% | +0.4% | −3.3% |
| 16:00 | 16,417 | 16,355 | +0.4% | −1.4% | −1.0% |
| 17:00 | 16,859 | 16,839 | +0.1% | +3.1% | +3.3% |
| 18:00 | 17,946 | 17,957 | −0.1% | −0.2% | −0.3% |
| 19:00 | 18,537 | 18,704 | −0.9% | +1.1% | −0.5% |
| 20:00 | 19,751 | 19,734 | +0.1% | +3.7% | −1.7% |
| 21:00 | 20,268 | 20,552 | −1.4% | −2.2% | +0.2% |
| 22:00 | 19,105 | 19,043 | +0.3% | +0.8% | +4.7% |
| 23:00 | 17,016 | 17,009 | +0.0% | −2.2% | −0.1% |
Times are UTC. US Eastern is UTC−4, Central UTC−5, Mountain UTC−6, Pacific UTC−7. Source: IT_TRX_RIDE_LOCATIONS_CLASSIFIER, all rides, no country or state filter.
Favorable direction, none significant.
Four incident types are tracked as rates per million rides, normalized for daily fleet volume variation. On Bike to Work Day, all four metrics trend in a favorable direction, though none cross the threshold of statistical significance. Hard braking (−0.6%) and cornering (−0.8%) are essentially flat; harsh acceleration is slightly up (+3.8%). High G Impact is the only statistically significant finding at +17.3% (Z=+2.10), though this appears to reflect lower April baseline rates rather than a genuine event-day elevation.
| Incident Type | Pre-Event Mean (per M rides) | Std Dev | May 15 Rate | % Change | Z-Score |
|---|---|---|---|---|---|
| Hard Brake | 34,252 | 1,781 | 34,050 | −0.6% | −0.11 · NS |
| Cornering | 12,738 | 807 | 12,642 | −0.8% | −0.12 · NS |
| Harsh Acceleration | 2,762 | 181 | 2,868 | +3.8% | +0.58 · NS |
| High G Impact | 837 | 69 | 982 | +17.3% | +2.10 · p<0.05 |
The High G Impact significance (Z=+2.10, p<0.05) with the pre-event baseline is driven primarily by the April baseline having lower inherent High G rates (~771-919/M) compared to post-May dates (~1,039-1,305/M). This is a hallmark of seasonal confound rather than event-driven signal. Do not report this as a causal finding without further segmentation. The three behavioral metrics (braking, cornering, acceleration) are directionally consistent with the "Safety in Numbers" literature (NHTSA 2023).
The signal lives at the city level.
State-level ride counts for May 15 compared against the 6-date pre-event baseline average. Wisconsin (+12.9%) and Georgia (+11.4%) lead all high-volume states; Milwaukee and Atlanta both host well-organized Bike to Work Day events. The three largest markets (New York, Texas, California) cluster close to zero, showing no dramatic event effect in the largest metro fleets.
| State | Baseline Rides | Event Rides | Rides % | Hours % | Km % |
|---|---|---|---|---|---|
| New York | 50,004 | 50,803 | +1.6% | +4.2% | +4.5% |
| California | 38,628 | 39,835 | +3.1% | +3.0% | +5.9% |
| Texas | 21,537 | 21,636 | +0.5% | +4.1% | +4.5% |
| Florida | 18,126 | 18,057 | −0.4% | −1.3% | +3.4% |
| New Jersey | 12,831 | 13,012 | +1.4% | +5.3% | +7.7% |
| Georgia | 7,460 | 8,312 | +11.4% | +3.1% | +8.2% |
| Pennsylvania | 8,216 | 8,283 | +0.8% | −1.8% | +4.1% |
| Illinois | 7,703 | 7,817 | +1.5% | +7.8% | +3.8% |
| Virginia | 5,622 | 5,820 | +3.5% | +4.1% | +12.0% |
| Ohio | 5,423 | 5,791 | +6.8% | +6.6% | +8.0% |
| North Carolina | 5,462 | 5,620 | +2.9% | +7.8% | +10.7% |
| Massachusetts | 5,626 | 5,246 | −6.8% | +5.1% | +5.4% |
| Nevada | 4,838 | 5,050 | +4.4% | +8.1% | +13.1% |
| Maryland | 4,808 | 5,044 | +4.9% | +0.6% | +3.8% |
| Washington | 4,937 | 4,787 | −3.0% | −8.8% | −5.2% |
| Arizona | 4,179 | 4,173 | −0.1% | +3.5% | +9.1% |
| Michigan | 3,464 | 3,697 | +6.7% | +6.8% | +11.8% |
| Tennessee | 3,592 | 3,532 | −1.7% | −2.6% | +4.6% |
| Colorado | 3,438 | 3,498 | +1.7% | −1.0% | −1.8% |
| South Carolina | 3,229 | 3,295 | +2.0% | +5.4% | +8.5% |
| Connecticut | 3,257 | 3,272 | +0.5% | −0.9% | +1.7% |
| Indiana | 2,928 | 3,050 | +4.1% | +6.9% | +7.8% |
| Missouri | 2,549 | 2,694 | +5.7% | −0.5% | +0.7% |
| Oregon | 2,066 | 2,176 | +5.3% | +28.1% | +14.9% |
| Louisiana | 2,178 | 2,118 | −2.7% | +9.4% | +9.7% |
| Wisconsin | 1,830 | 2,066 | +12.9% | +29.0% | +23.0% |
| Minnesota | 1,696 | 1,863 | +9.8% | +21.3% | +27.9% |
| Arkansas | 1,318 | 1,226 | −7.0% | +3.7% | +2.0% |
| Rhode Island | 721 | 653 | −9.4% | −6.6% | +7.1% |
Top gainers (Wisconsin, Georgia, Minnesota) align with strong Twin Cities, Milwaukee, and Atlanta cycling advocacy. Oregon (+5.3% rides, +28.1% hours) is a notable outlier: rides rose modestly but hours surged, possibly reflecting longer-duration fleet operations in Portland. Massachusetts and Rhode Island are the sharpest decliners. Source: IT_TRX_RIDE_LOCATIONS_CLASSIFIER, ride starting-point state.
Consistent with Nordback, not contradictory.
The most rigorous peer-reviewed study of Bike to Work Day's effect on motor vehicle volumes is Nordback et al. (2014), conducted in Boulder, Colorado. Their finding: motor vehicle volume was lower on Bike to Work Day in 88% of corridor-day observations vs comparable weekdays (statistically significant, p<0.05). Our national result (no significant fleet-wide effect) is consistent with, not contradictory to, that finding.
Fleet composition
Nexar's fleet is 85% rideshare/commercial, not personal commuters. Nordback studied personal vehicle corridors. These are different populations.
Geography
Boulder, CO has approximately 10.4% bike commute rate, 19 times the US national average of 0.54%. Effects significant in Boulder would be diluted to undetectable at national scale.
Statistical power
This study is underpowered to detect sub-1% national effects. The minimum detectable effect is ±6%; real Bike to Work Day effects in a commercial fleet are expected to be well under 1% nationally.
Post-2020 work patterns
Hybrid work has raised baseline variance in commuter volumes substantially since Nordback's 2014 study, reducing effective statistical power for this type of analysis.
Key findings.
Limitations.
Limitations are presented as a first-class section, not a footnote. These are not excuses for null results; they define the scope within which any finding should be interpreted. A result that doesn't acknowledge limitations is not a result, it's advocacy.
Fleet composition, the fundamental mismatch
85% of the Nexar fleet is professional rideshare/taxi operators who do not make personal modal-shift choices. Any effect would manifest in the small consumer segment, whose size severely limits power. This study answers "did the Nexar commercial fleet operate differently," not "did Americans drive less."
Underpowered study design
With N=11 baseline dates, the minimum detectable effect (80% power) is approximately ±6%. Expected real-world effects are sub-1%. Conclusions are limited to "no large effect," not "no effect."
No city-level or corridor-level analysis
Database scan cost constraints prevented daily city/state disaggregation. Bike to Work Day effects are geographically concentrated (Boulder CO, DC, Portland, SF). A national aggregate cannot detect effects that are real at the city level. This is the most material gap.
MLP pipeline ramp-up confound
Cyclist and pedestrian detections cannot be compared to April-May 8 dates because the MLP pipeline was not yet deployed at scale. The only valid comparison is May 15 vs post-rollout dates (May 29 onward), with no baseline variance estimate possible.
Seasonal volume decline
Fleet ride volumes show a clear seasonal decline (~373K in April to ~332K in late June). The pre-event 6-date baseline is the most proximate window but introduces asymmetry, placing all comparators in the higher-volume April to early-May period.
Thursday vs Friday mismatch
Bike to Work Day falls on a Friday; available Thursday comparators are used throughout. Friday and Thursday driving profiles differ in rideshare demand and commute timing, introducing a systematic day-of-week confound.
Vehicle classification artifact
The "Unknown" vehicle type grows monotonically from April through June due to MLP classification rollout. This is a data artifact, not a behavioral signal. Vehicle-type analysis uses the pre-event 6-date baseline to minimize this confound.
No weather or external-event controls
Day-to-day variation is also driven by weather, local events, holidays, and economic shocks. No weather controls were applied. May 15 weather data was not incorporated into the study design.
Methodology appendix.
| Table | Purpose | Granularity |
|---|---|---|
| MT_MOD_RISK | Primary ride volume & incident data | Ride-level, national daily |
| MT_MLP | Object detection (cyclists, pedestrians) | Detection event-level |
| MT_MOD_RISK_MONTHLY_FACT_STATE | State-level monthly incident proxy | State × month |
Incident rate calculation. Rate = (Total incident events on day D) / (Total rides on day D) × 1,000,000. This normalization controls for day-to-day variation in total fleet size and operational hours. Hard brake, cornering, harsh acceleration, and high-G impact are sourced from camera telemetry, not self-reported data.
Vehicle classification. Nexar vehicles are classified into Consumer, Taxi/Rideshare, Bus, Truck, and Unknown at device registration and updated via the MLP pipeline as models improve. The Unknown category reflects vehicles not yet classified and grows during rollout periods, it is not a stable vehicle type.
Statistics. For low-count Poisson observations (cyclist detections, n=22-34), 95% confidence intervals use the exact Poisson method. Z-scores for day-level metrics use z = (x_event − μ_baseline) / σ_baseline, where μ and σ are computed from the 6-date pre-event baseline window (Apr 3, Apr 10, Apr 17, Apr 24, May 1, May 8). No multiple-comparison correction was applied; applying one would make all results even more decidedly non-significant.
Nexar Fleet Intelligence · Marketing Analysis · Bike to Work Day 2025 · Data source IT_TRX_RIDE_LOCATIONS_CLASSIFIER, MT_MOD_RISK, MT_MLP · National scope · Event day May 15, 2025 vs 6-date pre-event Thursday baseline.