Monthly Traffic Safety Analysis

184 CRASHES IN
TAUNTON, MA
MAY 2023

All metrics benchmarked againstMay 2022

In May 2023, Taunton experienced 184 total crashes, an increase of 24.32% compared to the 148 crashes reported in May 2022. A notable shift is the absence of fatalities in May 2023, down from one fatality in May 2022. Additionally, hit-and-run crashes increased significantly by 54.55% year-over-year.

184

24.3%was 148

Total Crash Events

0

-100.0%was 1

Persons Killed

45

2.3%was 44

Persons Injured

17

54.5%was 11

Hit-and-Run Crashes

Note: "Persons Killed" (0) counts individual fatalities across all crash events. "Fatal" in the severity table below (0) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 10 crashes with unreported severity are not shown in the severity breakdown.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

The overall trend indicates an increase in crashes year-over-year, with total crashes rising from 148 in May 2022 to 184 in May 2023. This represents a 24.32% increase in crash incidents for the month.

17

Hit-and-Run Crashes — May 2023

54.5% vs prior (11)

Hit-and-run crashes increased from 11 in May 2022 to 17 in May 2023, representing a 54.55% increase in count. The hit-and-run crash rate also rose from 7.4% to 9.2% of all crashes year-over-year.

Vulnerable Road User Casualties

0

Motorists Killed

Prior: 00.0%

45

Motorists Injured

Prior: 427.1%

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The peak day for crashes remained Monday in both periods, with counts increasing from 30 in May 2022 to 37 in May 2023. The peak hour also remained consistent at 4 PM, seeing an increase from 18 crashes in May 2022 to 27 crashes in May 2023.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash date field aggregated by weekday

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Fatalities decreased from 1 in May 2022 to 0 in May 2023. Total injuries saw a slight increase from 44 to 45 year-over-year. While May 2022 recorded 1 fatal crash, May 2023 reported 1 serious injury crash.

Outcome by Severity (Crash Events)

Serious Injury1serious injury crashes0.5%
Minor Injury19minor injury crashes10.3%
-5.0%prior 20
Possible Injury13possible injury crashes7.1%
8.3%prior 12
No Injury141no injury crashes76.6%
48.4%prior 95

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factor shifted from 'No improper driving' in May 2022 (31 crashes) to 'Inattention' in May 2023 (33 crashes), representing a 26.92% increase in inattention-related crashes. Crashes attributed to 'Failure to keep in proper lane or running off road' saw a substantial increase from 3 to 15, a 400% change in count. Conversely, crashes with 'No improper driving' decreased by 32.26% in count, from 31 to 21.

Officer-Reported Primary Contributing Cause

Inattention33 (17.9%)26.9%prior 26
Failed to yield right of way29 (15.8%)38.1%prior 21
No improper driving21 (11.4%)-32.3%prior 31
Followed too closely20 (10.9%)11.1%prior 18
Failure to keep in proper lane or running off road15 (8.2%)
Other improper action14 (7.6%)100.0%prior 7
Distracted5 (2.7%)
Made an improper turn4 (2.2%)
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner3 (1.6%)
Swerving or avoiding due to wind, slippery surface, vehicle, object, vulnerable user in roadway3 (1.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

Crashes occurring in clear weather conditions (including Clear/Clear) increased from 92 in May 2022 to 158 in May 2023. Crashes during daylight hours also increased from 115 to 154 year-over-year. Incidents on dry road surfaces rose from 140 to 167, while those on wet surfaces increased from 8 to 14.

Weather

Clear89 (48.6%)
17.1%prior 76
Clear/Clear69 (37.7%)
331.3%prior 16
Cloudy8 (4.4%)
-52.9%prior 17
Rain7 (3.8%)
Rain/Rain5 (2.7%)
Clear/Cloudy3 (1.6%)
Rain/Severe crosswinds1 (0.5%)
Unknown/Unknown1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Weather condition at time of crash

Lighting

Daylight154 (85.1%)
33.9%prior 115
Dark - lighted roadway18 (9.9%)
-21.7%prior 23
Dawn4 (2.2%)
Dark - roadway not lighted3 (1.7%)
Dark - unknown roadway lighting1 (0.6%)
Dusk1 (0.6%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Lighting condition field

Road Surface

Dry167 (91.8%)
19.3%prior 140
Wet14 (7.7%)
75.0%prior 8
Sand, mud, dirt, oil, gravel1 (0.5%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Road surface condition field

Vehicles & Demographics

The total number of vehicles involved in crashes increased from 278 in May 2022 to 336 in May 2023. While TOYOTA remained a top make, its count decreased from 53 to 44. FORD saw a significant increase in involvement, from 21 vehicles in May 2022 to 41 in May 2023, and HONDA increased from 32 to 41.

Top Vehicle Makes (336 vehicles)

1
TOYOTA44 (13.1%)
-17.0%prior 53
2
HONDA41 (12.2%)
28.1%prior 32
3
FORD41 (12.2%)
95.2%prior 21
4
CHEVROLET38 (11.3%)
46.2%prior 26
5
NISSAN25 (7.4%)
0.0%prior 25
6
HYUNDAI15 (4.5%)
-11.8%prior 17
7
JEEP13 (3.9%)
18.2%prior 11
8
SUBARU12 (3.6%)
100.0%prior 6
9
DODGE10 (3%)
-9.1%prior 11
10
KIA10 (3%)
-9.1%prior 11

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Vehicle unit records

34 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (357 persons with recorded sex)

Female184 (51.5%)
40.5%prior 131
Male173 (48.5%)
-0.6%prior 174

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · Person-level records linked to crash events

Speed Limit Zones

Crashes in the 30 mph speed zone increased from 70 in May 2022 to 85 in May 2023. Conversely, crashes in the 35 mph zone decreased from 27 to 20. There were no fatalities recorded in any speed zone in May 2023, compared to one fatality in the 35 mph zone in May 2022.

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-05-01 to 2023-05-31 · 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: 2023-05-01 through 2023-05-31
  • Report generated: June 21, 2026

Data Coverage

  • Reporting period: 2023-05-01 through 2023-05-31 (31 days)
  • Geographic scope: TAUNTON, MA
  • Total crash records analyzed: 184
  • Total persons involved: 397
  • Total vehicles involved: 336

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). "TAUNTON, MA Crash Intelligence Report: May 2023." Published June 21, 2026. Reporting period: 2023-05-01 to 2023-05-31. Data source: Massachusetts Crash Data (MassDOT CDV), Arcgis_yearly Open Data. Available at: https://thatcarhitme.com/crash-data/massachusetts/taunton/may-2023-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

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Taunton, MA Crash Report — May 2023 | ThatCarHitMe.com