ThatCarHitMe.com
An Injuria.ai Company
YEAR-OVER-YEAR CRASH REPORT · LONGMEADOW, MA · 2023
Purpose: Machine-readable JSON endpoint for AI agents, LLMs, researchers, and programmatic consumers. Returns all underlying crash data and AI-generated commentary without HTML.
Authentication: None required. Public endpoint.
GET: https://thatcarhitme.com/api/crash-data/reports/data/massachusetts/longmeadow/2023-annual-report
Yearly Traffic Safety Analysis
325 CRASHES IN
LONGMEADOW, MA
2023
In 2023, Longmeadow recorded 325 total crashes, an 11.3% increase from the 292 crashes reported in 2022. While fatalities remained stable with one death recorded in each period, the total number of people injured rose by 37.2% from 78 to 107 year-over-year.
325
▲ 11.3%was 292
Total Crash Events
1
Persons Killed
107
▲ 37.2%was 78
Persons Injured
31
▲ 6.9%was 29
Hit-and-Run Crashes
Note: "Persons Killed" (1) counts individual fatalities across all crash events. "Fatal" in the severity table below (1) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 9 crashes with unreported severity are not shown in the severity breakdown.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Aggregate counts from crash, person, and vehicle records
Trend Summary
The overall trend in traffic collisions in Longmeadow shows an increase from 2022 to 2023. Total crashes rose by 11.3%, from 292 to 325. This was accompanied by a more significant rise in total injuries, which climbed by 37.2% from 78 to 107, while fatalities remained unchanged at one in each year.
31
Hit-and-Run Crashes — 2023
▲ 6.9% vs prior (29)
The number of hit-and-run crashes increased slightly from 29 incidents in 2022 to 31 in 2023. However, due to the overall increase in total crashes, the hit-and-run rate as a percentage of all crashes saw a slight decrease, moving from 9.9% in 2022 to 9.5% in 2023.
Vulnerable Road User Casualties
0
Cyclists Killed
1
Motorists Killed
2
Cyclists Injured
105
Motorists Injured
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)
When Crashes Happen
Temporal analysis shows a shift in crash patterns between the two periods. The most frequent day for crashes moved from Thursday (55 crashes) in 2022 to Friday (75 crashes) in 2023. Similarly, the peak hour for collisions shifted earlier in the day, from 5 PM (26 crashes) in 2022 to 2 PM (36 crashes) in 2023.
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash date field aggregated by weekday
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Crash time field aggregated by hour (0-23)
Crash Severity Breakdown
The number of fatal crashes remained stable with one incident in both 2023 and 2022, resulting in a slight decrease in the fatal crash rate from 0.34% to 0.31%. The distribution of injury severity shifted, with crashes involving minor injuries increasing from 22 to 33, representing a rise in share from 7.5% to 10.2% of all crashes. Conversely, crashes with possible injuries decreased in count from 37 to 32 and in proportion from 12.7% to 9.8%.
Outcome by Severity (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · KABCO injury classification scale
Severity Distribution (Crash Events)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Most severe injury per crash record
Top Contributing Factors
The leading contributing factors to crashes remained consistent year-over-year, with 'Inattention' being the most cited factor in both 2023 (86 crashes) and 2022 (65 crashes). The count of crashes attributed to inattention increased by 32.3%. Similarly, the count for crashes involving 'Followed too closely' rose by 33.3% from 39 to 52 incidents, while 'Driving too fast for conditions' saw a 47.4% decrease in count from 19 to 10.
Officer-Reported Primary Contributing Cause
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Officer-reported primary contributory cause per crash
Road & Environmental Conditions
The majority of crashes in both years occurred in clear weather and on dry roads, which remained the most common conditions. However, there was a notable increase in crashes on wet road surfaces, which rose from 37 incidents in 2022 to 55 in 2023. Crashes during daylight hours increased from 201 to 215, with the overall distribution of lighting conditions remaining largely proportional to the prior year.
Weather
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash
Lighting
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field
Road Surface
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field
Vehicles & Demographics
The makes of vehicles involved in crashes saw a shift at the top, with Toyota becoming the most frequent make in 2023 (79 vehicles), up from second place in 2022 (61 vehicles). Honda, the top make in 2022 with 73 vehicles, dropped to second in 2023 with 63. Examining the age of persons involved in crashes, there was a notable increase in the 65+ age group, which grew from 66 individuals in 2022 to 107 in 2023. Conversely, the number of persons in the 16-20 age group decreased from 99 to 73.
Top Vehicle Makes (607 vehicles)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records
61 persons with unknown or unrecorded age excluded from age chart.
Sex Distribution (669 persons with recorded sex)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Person-level records linked to crash events
Speed Limit Zones
Crashes in 35 mph zones remained the most frequent, increasing from 156 to 167 incidents year-over-year. A significant percentage increase occurred in 30 mph zones, where crashes nearly doubled from 20 in 2022 to 39 in 2023. The location of the single fatal crash in each period shifted; in 2022 it occurred in a 30 mph zone, while in 2023 it was recorded in a 65 mph zone.
Fatal crashes by zone: 65 mph: 1 of 59 (1.695%)
Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Posted speed limit at crash location
Data Sources & Methodology
Primary Data Source
All crash data in this report is sourced from Massachusetts Crash Data (MassDOT CDV), accessed programmatically via the Arcgis_yearly Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.
Data Retrieval
- Access method: Arcgis_yearly Open Data API (SoQL queries)
- Data format: Structured JSON via REST API
- Record types queried: Crash events, person records, and vehicle unit records
- Date filter applied: 2023-01-01 through 2023-12-31
- Report generated: June 21, 2026
Data Coverage
- Reporting period: 2023-01-01 through 2023-12-31 (365 days)
- Geographic scope: LONGMEADOW, MA
- Total crash records analyzed: 325
- Total persons involved: 741
- Total vehicles involved: 607
Analytical Methodology
- Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
- Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
- Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
- Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
- Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
- Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
- AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.
Limitations & Disclaimers
- Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
- Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
- Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
- AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
- Percentages are calculated from reported data and are subject to rounding.
Non-Affiliation Disclosure
This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.
Data License
The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.
Corrections & Feedback
If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.
Suggested Citation
ThatCarHitMe.com (Injuria.ai). "LONGMEADOW, MA Crash Intelligence Report: 2023." Published June 21, 2026. Reporting period: 2023-01-01 to 2023-12-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/longmeadow/2023-annual-report
About the Publisher
ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.
Questions about this report's data or methodology: data@injuria.ai
ThatCarHitMe.com · An Injuria.ai Company
ThatCarHitMe.com
An Injuria.ai Company
Crash Data Intelligence
Data: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly
Period: 2023-01-01 – 2023-12-31
Generated: June 21, 2026 · All rights reserved