Vision and Road Accident Contribution Analysis of Road Accident Contributory Factors

Relationship Between Vision and Road Safety

Inquiry into Vision's Contribution: One of the central questions in road safety is the extent to which poor vision contributes to traffic accidents.
Importance of Visual Data: Driving is predominantly a visual task. It is estimated that "90% of information received whilst driving is visual."
Evidence and Enforcement: Strong evidence linking inadequate vision to accidents is required to support the enforcement of specific "vision standards for driving."
Significance of Factors: If poor vision is established as a contributor to unsafe driving, researchers must determine the relative importance of this factor compared to others.
Pre-Licensing Medical Checks: Vision is notably the only "medical/health" check that is performed routinely on drivers in the UK before they are granted a license.

Historical Data of UK Traffic Accidents

Accident Trends (1926–2013):
- 1926:
  - Number of motor vehicles: $1.7 \times 10^6$
  - Total accidents: $124,000$
  - Total casualties: $139,000$
  - Total fatalities: $4,886$
- 1966:
  - Number of motor vehicles: $12 \times 10^6$
  - Total accidents: $292,000$
  - Total casualties: $392,000$
  - Total fatalities: $7,985$
- 2013:
  - Number of motor vehicles: $35 \times 10^6$
  - Total accidents: $139,000$
  - Total casualties: $184,000$
  - Total fatalities: $1,713$

Data Collection Methodology: Contributory Factors (CF)

Department for Transport (DfT) Reporting: Since 2005, the DfT has collected data regarding "contributory factors" (CF) alongside standard accident numbers.
The Reporting Process:
- A report form is completed by the attending police officer for each recorded accident.
- The form includes details regarding the vehicles and casualties involved.
Nature of the Data:
- The data relies on the subjective judgment of the reporting police officer.
- Officers can assign several different factors to each of the participants (drivers, pedestrians, or cyclists) involved in the accident.
Factor Classification:
- There are $77$ possible factors organized into $9$ different categories.
- Examples of factors include road conditions (e.g., spray, obstacles in the road), driver error (e.g., misjudged distance), and vehicle condition (e.g., faulty brakes).
2008 Data Overview:
- Total accident reports: $170,000$
- Vehicles involved: $310,000$
- Total casualties: $230,000$

Top 10 Reported Contributory Factors (2014–2018)

Driver/Rider failed to look properly: Represents the most common factor.
- 2014: $53,259$ ( $46\%$ )
- 2015: $49,871$ ( $46\%$ )
- 2016: $44,557$ ( $44\%$ )
- 2017: $37,896$ ( $41\%$ )
- 2018: $33,897$ ( $40\%$ )
Driver/Rider failed to judge other person’s path or speed:
- 2014: $27,551$ ( $24\%$ )
- 2015: $25,245$ ( $23\%$ )
- 2016: $22,774$ ( $23\%$ )
- 2017: $20,289$ ( $22\%$ )
- 2018: $18,047$ ( $21\%$ )
Driver/Rider careless, reckless or in a hurry:
- 2014: $20,883$ ( $18\%$ )
- 2015: $20,006$ ( $18\%$ )
- 2016: $18,175$ ( $18\%$ )
- 2017: $13,852$ ( $15\%$ )
- 2018: $13,203$ ( $16\%$ )
Poor turn or manoeuvre:
- 2014: $18,916$ ( $16\%$ )
- 2015: $18,378$ ( $17\%$ )
- 2016: $16,119$ ( $16\%$ )
- 2017: $12,768$ ( $14\%$ )
- 2018: $11,025$ ( $13\%$ )
Loss of control:
- 2014: $15,350$ ( $13\%$ )
- 2015: $13,966$ ( $13\%$ )
- 2016: $12,208$ ( $12\%$ )
- 2017: $10,830$ ( $12\%$ )
- 2018: $9,232$ ( $11\%$ )
Pedestrian failed to look properly:
- 2014: $10,888$ ( $9\%$ )
- 2015: $10,113$ ( $9\%$ )
- 2016: $8,782$ ( $9\%$ )
- 2017: $7,928$ ( $9\%$ )
- 2018: $7,182$ ( $8\%$ )
Slippery road (due to weather):
- 2014: $9,802$ ( $8\%$ )
- 2015: $8,312$ ( $8\%$ )
- 2016: $7,709$ ( $8\%$ )
- 2017: $7,501$ ( $8\%$ )
- 2018: $6,153$ ( $7\%$ )
Travelling too fast for conditions:
- 2014: $7,921$ ( $7\%$ )
- 2015: $7,531$ ( $7\%$ )
- 2016: $6,595$ ( $7\%$ )
- 2017: $6,090$ ( $7\%$ )
- 2018: $5,048$ ( $6\%$ )
Exceeding speed limit:
- 2014: $5,387$ ( $5\%$ )
- 2015: $5,351$ ( $5\%$ )
- 2016: $5,158$ ( $5\%$ )
- 2017: $4,880$ ( $5\%$ )
- 2018: $4,727$ ( $6\%$ )
Sudden braking:
- 2014: $8,768$ ( $8\%$ )
- 2015: $7,453$ ( $7\%$ )
- 2016: $6,768$ ( $7\%$ )
- 2017: $5,723$ ( $6\%$ )
- 2018: $4,606$ ( $5\%$ )
Total Number of Reported Accidents:
- 2014: $115,673$
- 2015: $108,211$
- 2016: $100,296$
- 2017: $93,125$
- 2018: $84,968$

Vision-Related and Visibility Factors (2018 Statistics)

Specific factors assigned to accidents in 2018 (Fatal / Total):
- Uncorrected/defective eyesight: $3$ fatal accidents; $196$ total accidents reported.
- Dazzling sun: $27$ fatal accidents; $2,643$ total accidents reported.
- Dazzling headlights: $8$ fatal accidents; $269$ total accidents reported.
- Visor or windscreen scratched/dirty: $3$ fatal accidents; $118$ total accidents reported.
- Pedestrian wearing dark clothes at night: $66$ fatal accidents; $697$ total accidents reported.
- Rider wearing dark clothing: $6$ fatal accidents; $408$ total accidents reported.

Analyzing the Absence of Vision in Accident Statistics

Potential Explanations for Low Vision-Related CF Reports:
- Does it mean vision is not important, implying regulations should be relaxed?
- Does it indicate that current regulations are highly effective, screening out almost everyone who is dangerous?
- Could vision be a more significant factor in less serious accidents that are not captured in these specific reports?
- Is the low prevalence due to police officers not intuitively considering vision as a possible cause of the collision?

Supplemental Evidence on Visual Impact

Diminished Vision for All Drivers: At night, every driver effectively has poor vision due to lighting constraints. This correlates with a fatality rate that is $3 \times$ greater at night than during the day.
Experimental Performance Effects: Studies have demonstrated that blurred vision negatively affects driver performance, even if this does not always culminate in a reported accident.
Reaction Times: Blurred vision is specifically linked to longer reaction times, which is exacerbated in low light or low contrast environments.

Research Perspectives: Hawley et al.

Alternative Framework: Hawley et al. utilized the same DfT CF data but reached different conclusions by redefining existing categories.
Re-categorization: They considered "Failed to Look" (FTL) and "Failed to Judge" (FTJ) to be "vision-related" factors.
Demographic Findings: Their analysis showed that older drivers are more likely to be involved in accidents related to health (ID) and vision.
Frequency Recommendations: Based on their findings, they suggested that younger drivers require an eye exam every $5$ years, while older drivers should have one every $2$ years.

Legal Powers and Licensing Revocation

Police Authority: Law enforcement officers have the power to request an urgent revocation of a person's driving license through the Driver & Vehicle Licensing Agency (DVLA).
Basis for Revocation: This occurs if an officer believes that allowing the driver to remain on the road would put the safety of other road users at risk.
Increased Awareness: The existence of this protocol may serve to make police officers more conscious of vision as a potential contributory factor during accident investigations.