Where does this data come from?

The California Crash Reporting System, published by the California Highway Patrol on the California Open Data Portal. It is in the public domain and needs no account to download. Nothing here is modelled or estimated — every figure is a count of rows or a sum of a column in those files.

Why not use SWITRS or the TIMS tool from UC Berkeley?

SWITRS closed to public access and was replaced by the California Crash Reporting System. SafeTREC at UC Berkeley built the Transportation Injury Mapping System on the older SWITRS data, and it remains the best mapping tool for California crash data. This site works from the currently published CCRS files instead, so that what you see here can be reproduced from a public download.

Are these numbers a guide to what my own claim is worth?

No, and they are not offered as one. They are counts of reported crashes across a county or the state. They say nothing about fault, insurance, injuries, or outcome in any particular case, and no figure here predicts anything about yours.

Can I download and reuse the data?

Yes. The county table is available as a CSV with no form, no email address, and no account. The underlying data is public domain. A citation is requested but not required.

The figures cover calendar years 2023 and 2024, the two most recent complete years available at the time of compilation. Crash records are amended after the fact, so counts for a given year can shift slightly as reports are revised.

How this crash data was assembled

Reviewed by Slick Nick, California licensed attorney · State Bar #324789 · Last updated September 1, 2026

Everything on the crash-data pages comes from one public file set. No figure is modelled, estimated, or filled in. If a number appears on this site, it is a count of rows or a sum of a column in the files named below, and you can download those files yourself and get the same answer.

The source

The California Crash Reporting System (CCRS), published by the California Highway Patrol on the California Open Data Portal. It is public domain, and it needs no account, no key, and no form.

Dataset: California Crash Reporting System (CCRS)
Publisher: California Highway Patrol
Licence: Public domain (as published on data.ca.gov)
Field definitions: CCRS Raw Data Export Layout, 9/27/2024, California Highway Patrol Information Technology Section
Retrieved: 2026-09-01

Files used, as downloaded:

crashes_2023.csv — 193,732,146 bytes
parties_2023.csv — 211,652,130 bytes
crashes_2024.csv — 197,631,820 bytes
parties_2024.csv — 217,102,627 bytes

SafeTREC, TIMS and SWITRS

Anyone working with California crash data runs into three names, and they are not interchangeable.

SWITRS — the Statewide Integrated Traffic Records System, the long-running CHP collision database. It closed to public access and CCRS replaced it.
SafeTREC — the Safe Transportation Research and Education Center at UC Berkeley.
TIMS — the Transportation Injury Mapping System, built by SafeTREC on SWITRS data. It geocodes collisions and offers free query and mapping tools, and it is the better instrument if you want to map crashes rather than count them.

This site cites SWITRS and TIMS because that is the lineage of California crash data and because TIMS is worth your time. It does not draw its numbers from them. The figures here come from the currently published CCRS files, so that anything on these pages can be reproduced from a download that anyone can make without an account.

Sources: California Crash Reporting System, California Open Data Portal; Safe Transportation Research and Education Center (SafeTREC), UC Berkeley; Transportation Injury Mapping System (TIMS); California Secretary of State, California County Codes.

What each figure means

A crash: One record in the CCRS Crashes file. Rows flagged IsDeleted are excluded.
People killed: Sum of the NumberKilled column over those records.
People injured: Sum of the NumberInjured column over those records.
Pedestrian-involved: Crashes where MotorVehicleInvolvedWith is B — Pedestrian, the crash-level field on the CHP report.
Bicycle-involved: Crashes where MotorVehicleInvolvedWith is G — Bicycle.
Motorcycle-involved: Crashes with at least one party in the CCRS Parties file whose Vehicle1TypeDesc is exactly "Motorcycle". Motor-driven cycles and scooters of 15hp or less are a separate CCRS category and are not included.
A road pairing: A crash record names a primary road and a secondary road, the latter defined by CHP as "the nearest crossed road where the crash occurred". A pairing counts crashes naming the same two roads in the same city, in either order.
At the crossing: The subset of a pairing where SecondaryDistance is exactly 0 — the crash was at the crossing rather than a stated distance from it. A blank distance is not counted, because the CHP layout does not define what blank means.

County codes

CCRS records a county as a number. The export layout defines the field as "the unique code of the county in which the crash occurred" but does not publish the list, so the mapping used here is the state's own published list of California county codes from the Secretary of State. It was checked against the data before use: records coded 19 carry Los Angeles County city names and records coded 37 carry San Diego County city names.

Records whose county code is outside 1–58 are left out of the county tables and counted separately, rather than being assigned to a county. In 2023 that was 2 record(s); in 2024, 18. They remain in the statewide totals.

How the intersection locations were established

The junction ranking needs a point on a map, and the crash records do not contain one. Those coordinates are geocoded, not surveyed. A report names a primary road, the nearest crossed road, and a city; turning that into a location is an inference made after the fact, and an inference can be wrong in ways that look entirely convincing.

Locations come from the US Census geocoder. Each is published only if it survives three checks: the geocoder returns a point; the address it echoes back names the same city and both roads; and a second, independent source agrees to within 50 metres. The second source is the coordinate carried on the crash records themselves, or where those are missing, an OpenStreetMap-derived geocoder. Where the two disagree, the junction is dropped rather than guessed at — never averaged, and never settled in favour of whichever source has more records behind it.

Of 3,047 junctions attempted, 2,370 were confirmed and published. The rest were rejected: 42 because the two sources disagreed, 333 because no location was found, 290 because the returned address did not match what was asked for, and 12 because no second source was available. Where the sources did agree, the median gap between them is 9 metres.

Two cases show why the rule is strict. In Moreno Valley, 48 crash records agreed on a coordinate 3.3 kilometres from the real junction, with no internal disagreement to give it away. In San Jose, the coordinates carried on the crash records are displaced by roughly 33 kilometres — consistently, so they look reliable until you check them against something else. Both would have passed any test that trusted a single source.

Freeway and ramp locations are excluded

Any pairing naming a freeway, ramp, state route, or carriageway direction is excluded from mapping entirely — 147,064 crash records in the covered cities. Two reasons. A freeway pairing gathers crashes along a length of road rather than at a junction, so a single point misrepresents it however accurate that point is. And every geocoder tested resolved at most one in fifteen of them correctly, so the points would not be accurate either.

Why a city may be absent or thin

The obvious reading of a short city list is that the place is safe. That reading is usually wrong, and there are three separate reasons a city can be missing or thin, none of which is safety.

Its coordinates could not be confirmed. Where the crash records' own coordinates disagree with the geocoder, the junction is dropped. San Jose's are displaced by roughly 33 kilometres, so nothing there survives the check.
Its agency records cross streets very precisely. This is the one that looks most like a safety signal and is not. A report names "the nearest crossed road", and agencies differ in how finely they interpret that. San Mateo records 873 surface crashes spread across 797 distinct named pairings — 1.10 crashes per pairing — so no junction ever accumulates a count. Fontana records 2.85 crashes per pairing on the same definition. San Mateo is not safer than Fontana; it is written down differently, and a threshold that counts crashes per named junction is measuring that difference as much as anything on the road.
It genuinely has few concentrated junctions. Smaller places spread the same number of crashes over more locations, so no single junction reaches the floor.

How precise the counts are

Less precise than a table makes them look. A crash count at one junction is a count of rare events, and such counts vary from one period to the next without anything changing on the ground. The spread scales with the square root of the count, which means small numbers are very noisy:

Ordinary variation in a two-year crash count
Crashes recorded	Ordinary variation	Range you would expect
5	±45%	1 to 9
10	±32%	4 to 16
15	±26%	7 to 23
25	±20%	15 to 35
40	±16%	28 to 52

Two consequences. A junction recorded at 12 and one recorded at 15 are not meaningfully different, so small gaps between neighbouring rows should be read as ties. And a floor of five crashes cannot support a ranking at all, which is why junctions below ten are not published: at five, the count could as easily have been one or nine.

The ranking is not adjusted for anything

The junction ranking counts reported crashes. It is not adjusted for traffic volume, population, road length, or time of day, and this data cannot support such an adjustment. A junction carrying ten times the traffic will tend to record more crashes without being more dangerous per trip. Treat the ranking as a map of where the traffic is before you treat it as a map of where the risk is.

And as everywhere on this site: it says nothing about any individual claim. A junction's position in a statewide table has no bearing on fault, insurance, or outcome in a particular crash.

What this data cannot tell you

The limits matter more than the totals. In rough order of importance:

It is reported crashes, not all crashes. A collision nobody reported to police does not exist in this data. Minor crashes are under-represented by an unknown amount.
Counts are not rates. Los Angeles County leads every table on this site because roughly a quarter of Californians live there and drive there. Nothing on these pages is adjusted for population, vehicle miles travelled, or road mileage, so a ranking by count is a ranking by size as much as by risk.
Records change after the fact. Reports are amended and resubmitted; a year's totals can move slightly after the fact. The figures here are a snapshot taken on 2026-09-01.
Nothing here says who was at fault. The dataset carries a "primary collision factor" and an at-fault marker on parties, and this site does not use either. Fault in a specific crash is a legal question decided on that crash's evidence, not something to be read off a statewide table.
Severity is counted in people, not in outcomes. "Injured" is the number of people recorded injured on the report. It does not describe how badly, how long, or what followed.
Road names are as typed. The same junction can appear under several spellings. Pairings are grouped by exact road-name text within a city, so a single physical location may be split across more than one row.
Motorcycle counts are narrow on purpose. Only the "Motorcycle" vehicle type is counted. Motor-driven cycles and scooters of 15hp or less are a separate CCRS category and are excluded, so the real two-wheeled total is higher than the motorcycle figure shown.

These numbers say nothing about your claim

This needs saying plainly, because the whole point of a page like this is that it looks authoritative. A county's crash count tells you how many collisions were reported there. It tells you nothing about whether anyone was at fault in yours, what insurance was in place, how an injury will be treated, or how any claim will end. No figure on this site predicts anything about an individual case, and it should not be read as if it did.

Reuse

The underlying CCRS data is public domain. The county table compiled from it is available as a CSV download — no form, no email address, no account. If you use it, a citation is requested but not required: Slick Nick Law, from CHP CCRS data. If you find an error in the compilation, say so and it will be corrected.

Common questions

Where does this data come from?: The California Crash Reporting System, published by the California Highway Patrol on the California Open Data Portal. It is in the public domain and needs no account to download. Nothing here is modelled or estimated — every figure is a count of rows or a sum of a column in those files.
Why not use SWITRS or the TIMS tool from UC Berkeley?: SWITRS closed to public access and was replaced by the California Crash Reporting System. SafeTREC at UC Berkeley built the Transportation Injury Mapping System on the older SWITRS data, and it remains the best mapping tool for California crash data. This site works from the currently published CCRS files instead, so that what you see here can be reproduced from a public download.
Are these numbers a guide to what my own claim is worth?: No, and they are not offered as one. They are counts of reported crashes across a county or the state. They say nothing about fault, insurance, injuries, or outcome in any particular case, and no figure here predicts anything about yours.
Can I download and reuse the data?: Yes. The county table is available as a CSV with no form, no email address, and no account. The underlying data is public domain. A citation is requested but not required.
How current is it?: The figures cover calendar years 2023 and 2024, the two most recent complete years available at the time of compilation. Crash records are amended after the fact, so counts for a given year can shift slightly as reports are revised.