Menlo Park City Council votes on the Slow Streets Program this week

WhenTuesday, April 28 · 6:00 p.m.

In personCity Council Chambers · 751 Laurel St, Menlo Park

Dear neighbors — on Tuesday, April 28, the Menlo Park City Council will conduct its final review and vote on the proposed citywide Slow Streets Program. After years in development with no action being taken to improve safety on our streets, the proposed framework would disqualify more than 80% of city streets from ever receiving safety attention when residents request it. Show up and ask City Council not to approve the inadequate proposed program. Read the analysis of qualifying streets →

Despite its name, the program's eligibility criteria are structured in ways that disqualify streets with documented safety problems — and the data and consultant reports underpinning the program design contain meaningful errors that do not match the stated intent.

The dashboard below presents ground-truth measurements from College Avenue in Allied Arts. By any reasonable standard, this street demonstrates urgent need for safety attention — and yet, under the proposed program, it would not qualify.

Residents on Partridge Avenue and Elder Avenue requested safety improvements before cyclists were struck on those streets. Getting the policy right today prevents the next preventable crash.

Ground Truth Traffic Dashboard

College Ave, Menlo Park · Telraam segment 9000010927 · Mar 31 – Apr 24, 2026 · all times PDT

1 · Who uses this street?

Mode mix

Total counts across the period. Vehicles dominate; pedestrians and bikes share the same right-of-way.

Hourly conflict — vehicles vs vulnerable users

Vehicles per hour (cars + heavy stacked) and bikes + pedestrians per hour. Hover any hour for the full speed breakdown.

2 · How fast are vehicles going?

Speed distribution

Vehicles in each speed bin. Numbers above each bar show share of all vehicles, plus per-day average.

Vehicle exposure vs pedestrian fatality risk

Cyan: share of College Ave vehicles traveling at or above each speed (falls right — fewer cars at higher speeds). Red: probability a struck pedestrian dies at that impact speed (Tefft 2011, rises right — lethality climbs sharply with speed). Read any speed on the x-axis to see both at once: how many cars travel that fast, and how deadly a pedestrian strike at that speed would be.

Speed percentiles by hour-of-day

Shaded band = 15th–85th percentile. Solid line = median. Magenta dashed = 95th. Red dashed = 25 mph residential limit. Magenta band at top = the 3 mph–wide speed bucket where the fastest cars (≥2 cars in the sample) actually fell — the real peak is somewhere inside that bucket, not exactly at any single number.

Speed percentiles by day

Same metric — shaded band = 15th–85th percentile, solid line = median, magenta dashed = 95th, magenta band at top = the 3 mph–wide bucket where the fastest cars actually fell — but plotted across each observed day instead of by hour. Weekend dates in cyan on the x-axis.

Speeders by hour-of-day

Share of vehicles exceeding 25 mph (≈40 km/h)

Egregious speeders by hour

Share of vehicles exceeding 31 mph (≈50 km/h) — 6+ over the limit

3 · When? — volume detail

Daily vehicle volume

Vehicles per day, with cars (cyan) and heavy vehicles (navy) stacked. Hover any day for the cars/heavy split.

Hourly profile

Average vehicles per hour-of-day, with cars and heavy stacked. Hover for the breakdown.

Day of week

Average vehicles per day by weekday. Weekends in cyan; cars and heavy stacked.

Conflict heatmap — when do vehicles and vulnerable users overlap?

Each cell's intensity = vehicles × (pedestrians + bikes) for that hour of that weekday. The darker the cell, the more vehicle–vulnerable interactions can happen. Hover any cell for the underlying counts.

low conflict

peak

4 · The heavy-vehicle multiplier

Why heavy vehicles are uniquely dangerous

Kinetic energy ≈ ½ × mass × velocity²

A vehicle's destructive energy in a crash scales linearly with mass and quadratically with speed. Heavy vehicles compound danger from both directions.

3.8×

A 5,400 lb pickup at 35 mph carries roughly 3.8× the kinetic energy of a 2,800 lb sedan at 25 mph — and vastly more than that of a child or pedestrian.

Pedestrian fatality risk rises sharply with impact speed

For an average-aged adult struck by a passenger car: at 20 mph the chance of death is ~10%. At 30 mph, ~25%. At 40 mph, ~57%. At 45 mph, ~73%.

Heavy vehicles add a further multiplier. Pedestrians struck by SUVs and pickups are 2–3× more likely to die than those struck by cars at the same speed — higher hood profiles strike the torso and head rather than the legs, and greater mass transfers more energy.

Sources: Tefft, B.C. (2011), "Impact Speed and a Pedestrian's Risk of Severe Injury or Death," AAA Foundation for Traffic Safety. IIHS (2020) pedestrian crash analysis.

Fatality risk vs. College Ave exposure

Red: pedestrian fatality probability if struck at this speed (Tefft 2011). Blue dashed: share of vehicles on this street traveling at this speed or above. Where the two lines meet, you can read the local exposure to each level of risk.

Heavy vehicles vs vulnerable users by hour

When heavy vehicles share the street with bikes and pedestrians.

Heavy share of vehicle traffic by hour

% of vehicles that are heavy in each hour-of-day. Spikes mark commercial-delivery windows.

For daily and day-of-week breakdowns of heavy vehicles, switch the toggle in Section 3 above to "Heavy only".

5 · Is College Ave actually functioning as a bike boulevard?

NACTO bike boulevard compliance, day by day

College Ave is designated as a bicycle boulevard in the Menlo Park General Plan. The NACTO Urban Bikeway Design Guide sets the criteria for what that designation actually requires. Each row is one NACTO target criterion; each column is one observed day. ✔ = day met the target. ✘ = did not.

Source: NACTO, Urban Bikeway Design Guide — Bike Boulevards. NACTO uses the 95th percentile speed (which captures high-end speeders) rather than the 85th. NACTO does not specify a heavy-vehicle threshold.

Methodology. Volume counts ("vehicles") are cars + heavy vehicles, as reported by the Telraam sensor. Speed metrics use the per-hour passenger-car speed histogram that the sensor reports — heavy-vehicle speeds aren't classified separately, so they're counted toward volume but not toward speed distributions. Percentiles are computed by linear interpolation within each km/h bin (converted to mph). The 25 mph reference reflects the standard California residential speed limit (CVC §22352). The pedestrian-fatality curve in Section 4 is interpolated from Tefft (AAA Foundation, 2011), modeling an average-aged adult struck by a passenger car. Heavy vehicles add a further 2–3× lethality multiplier per IIHS research, not reflected in the curve. Hours with zero recorded traffic are excluded from speed-percentile calculations.

Sample. The dashboard reflects . use representative sample (recommended)