Yearly Traffic Safety Analysis

3,798 CRASHES IN
NEW BEDFORD, MA
2023

All metrics benchmarked against2022

In 2023, New Bedford recorded 3,798 traffic crashes, a slight decrease of 1.0% from the 3,836 crashes reported in 2022. While overall crashes remained relatively stable, the most significant year-over-year change was a 67.5% increase in hit-and-run incidents, which rose from 354 in 2022 to 593 in 2023.

3,798

-1.0%was 3,836

Total Crash Events

5

25.0%was 4

Persons Killed

1,060

-5.9%was 1,126

Persons Injured

593

67.5%was 354

Hit-and-Run Crashes

Note: "Persons Killed" (5) counts individual fatalities across all crash events. "Fatal" in the severity table below (5) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities. 445 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

Overall traffic crashes in New Bedford saw a slight year-over-year decline, falling by 1.0% from 3,836 in 2022 to 3,798 in 2023. This trend included a 5.9% decrease in the total number of injuries reported, from 1,126 to 1,060. However, the number of fatalities increased from 4 in the prior period to 5 in the current period.

593

Hit-and-Run Crashes — 2023

67.5% vs prior (354)

The number of hit-and-run crashes increased substantially in 2023 compared to the previous year. The count rose by 67.5%, from 354 incidents in 2022 to 593 in 2023. Consequently, the hit-and-run rate, which measures the proportion of total crashes that are hit-and-runs, grew from 9.2% in 2022 to 15.6% in 2023.

Vulnerable Road User Casualties

1

Pedestrians Killed

Prior: 2-50.0%

0

Cyclists Killed

Prior: 00.0%

4

Motorists Killed

Prior: 2100.0%

0

Other Killed

Prior: 00.0%

57

Pedestrians Injured

Prior: 62-8.1%

23

Cyclists Injured

Prior: 24-4.2%

977

Motorists Injured

Prior: 1,034-5.5%

3

Other Injured

Prior: 6-50.0%

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

The temporal patterns of crashes in New Bedford remained consistent year-over-year. In both 2023 and 2022, Friday was the day with the highest number of crashes, recording 583 and 621 incidents, respectively. Similarly, the 3 p.m. hour was the peak time for crashes in both periods, with 293 crashes in 2023 and 332 in 2022.

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 severity of crashes shifted slightly between the two periods. The fatal crash rate increased from 0.10% in 2022 to 0.13% in 2023, corresponding to a rise from 4 to 5 fatal crashes. Crashes resulting in serious injuries also saw a modest increase in both count (from 47 to 52) and proportion (from 1.2% to 1.4% of total crashes). Conversely, crashes involving minor or possible injuries decreased as a share of the total.

Outcome by Severity (Crash Events)

Fatal5fatal crashes0.1%
25.0%prior 4
Serious Injury52serious injury crashes1.4%
10.6%prior 47
Minor Injury465minor injury crashes12.2%
-2.1%prior 475
Possible Injury269possible injury crashes7.1%
-8.2%prior 293
No Injury2,562no injury crashes67.5%
0.7%prior 2,544

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 for crashes remained largely consistent year-over-year, with 'No improper driving' cited in 1,090 crashes in 2023 versus 1,034 in 2022. 'Inattention' remained the second-most cited factor, though its count decreased by 4.0% from 380 to 365. A notable change was observed in crashes attributed to 'Failed to yield right of way,' which increased in count by 22.2% from 243 incidents in 2022 to 297 in 2023, moving it to the third-ranked factor.

Officer-Reported Primary Contributing Cause

No improper driving1,090 (28.7%)5.4%prior 1,034
Inattention365 (9.6%)-3.9%prior 380
Failed to yield right of way297 (7.8%)22.2%prior 243
Other improper action183 (4.8%)-3.7%prior 190
Operating vehicle in erratic, reckless, careless, negligent or aggressive manner134 (3.5%)0.8%prior 133
Disregarded traffic signs, signals, road markings105 (2.8%)-8.7%prior 115
Followed too closely98 (2.6%)-11.7%prior 111
Failure to keep in proper lane or running off road96 (2.5%)7.9%prior 89
Over-correcting/over-steering57 (1.5%)-10.9%prior 64
Distracted57 (1.5%)-28.7%prior 80

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

Environmental conditions at the time of crashes were broadly similar across both years. In 2023, 70.3% of crashes occurred in clear weather, compared to 71.7% in 2022. Crashes on dry roads accounted for 83.2% of the total in 2023 and 81.6% in 2022. Similarly, the majority of incidents in both periods happened during daylight hours (66.0% in 2023 vs. 67.5% in 2022), indicating no significant shift in crash conditions year-over-year.

Weather

Clear2,672 (71.5%)
-2.8%prior 2,749
Cloudy282 (7.6%)
17.0%prior 241
Rain254 (6.8%)
14.4%prior 222
Clear/Unknown133 (3.6%)
23.1%prior 108
Cloudy/Rain92 (2.5%)
24.3%prior 74
Clear/Cloudy85 (2.3%)
-22.0%prior 109
Clear/Other64 (1.7%)
-21.0%prior 81
Cloudy/Unknown23 (0.6%)
187.5%prior 8
Rain/Cloudy20 (0.5%)
-23.1%prior 26
Snow20 (0.5%)
-64.9%prior 57

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Weather condition at time of crash

Lighting

Daylight2,506 (67.6%)
-3.2%prior 2,588
Dark - lighted roadway918 (24.8%)
7.0%prior 858
Dark - roadway not lighted129 (3.5%)
3.2%prior 125
Dusk73 (2.0%)
-8.8%prior 80
Dawn54 (1.5%)
20.0%prior 45
Dark - unknown roadway lighting22 (0.6%)
-21.4%prior 28
Other7 (0.2%)
-22.2%prior 9

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Lighting condition field

Road Surface

Dry3,159 (84.2%)
0.9%prior 3,132
Wet548 (14.6%)
8.3%prior 506
Snow20 (0.5%)
-75.0%prior 80
Ice14 (0.4%)
-72.0%prior 50
Other6 (0.2%)
Water (standing, moving)3 (0.1%)
Slush1 (0.0%)
-90.9%prior 11
Sand, mud, dirt, oil, gravel1 (0.0%)

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Road surface condition field

Vehicles & Demographics

The top five vehicle makes involved in crashes—Toyota, Honda, Ford, Nissan, and Chevrolet—remained unchanged in their ranking between 2022 and 2023. Regarding the age of persons involved, there was a shift between the two largest cohorts; the number of individuals aged 26-34 decreased from 1,509 to 1,326, while those aged 35-44 increased from 1,171 to 1,305. Other demographic distributions showed minimal change.

Top Vehicle Makes (7,521 vehicles)

1
TOYOTA1,125 (15%)
2.2%prior 1,101
2
HONDA958 (12.7%)
-6.2%prior 1,021
3
FORD788 (10.5%)
9.7%prior 718
4
NISSAN555 (7.4%)
-13.3%prior 640
5
CHEVROLET531 (7.1%)
0.8%prior 527
6
HYUNDAI329 (4.4%)
3.1%prior 319
7
KIA305 (4.1%)
8.2%prior 282
8
JEEP274 (3.6%)
-2.1%prior 280
9
GMC177 (2.4%)
8.6%prior 163
10
DODGE155 (2.1%)
-28.6%prior 217

Source: Massachusetts Crash Data (MassDOT CDV) · Arcgis_yearly Open Data · 2023-01-01 to 2023-12-31 · Vehicle unit records

2,018 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (6,748 persons with recorded sex)

Male3,634 (53.9%)
-4.9%prior 3,822
Female3,106 (46.0%)
-4.1%prior 3,239
X / Unspecified8 (0.1%)
300.0%prior 2

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

The distribution of crashes across speed zones remained similar year-over-year, with the 30 mph zone accounting for the most incidents in both 2023 (2,297 crashes) and 2022 (2,400 crashes). However, the location of fatal crashes shifted; in 2023, three of the five fatal crashes occurred in 65 mph zones, compared to one of four in 2022. Conversely, the two fatal crashes that occurred in 50 mph zones in 2022 were not repeated in 2023, which saw zero fatalities in that speed zone.

Fatal crashes by zone: 30 mph: 1 of 2,297 (0.044%) · 35 mph: 1 of 118 (0.847%) · 65 mph: 3 of 127 (2.362%)

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: NEW BEDFORD, MA
  • Total crash records analyzed: 3,798
  • Total persons involved: 9,036
  • Total vehicles involved: 7,521

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). "NEW BEDFORD, 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/new-bedford/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

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New Bedford, MA Crash Report — 2023 | ThatCarHitMe.com