Let's analyze the data provided in the tables about road accidents in Delhi during the year 2020. We are given that there were a total of 10,000 road accidents, and on average, three people were involved in each accident. This means the total number of people involved in these accidents is:

Total people involved = Total accidents × Average people per accident

Total people involved = $10,000 \times 3 = 30,000$ people

Analyzing Road Accident Severity Data (Table I)

Table I breaks down the 30,000 people involved based on the severity of the outcome:

Based on these percentages, we can calculate the number of people in each category:

• Uninjured: $47\%$ of $30,000 = 0.47 \times 30,000 = 14,100$ people
• First-Aid Required: $6\%$ of $30,000 = 0.06 \times 30,000 = 1,800$ people
• Mild Bruises: $9\%$ of $30,000 = 0.09 \times 30,000 = 2,700$ people
• Hospitalization: $19\%$ of $30,000 = 0.19 \times 30,000 = 5,700$ people
• Fatalities: $19\%$ of $30,000 = 0.19 \times 30,000 = 5,700$ people

Let's quickly verify the total number of people: $14,100 + 1,800 + 2,700 + 5,700 + 5,700 = 30,000$. This matches the total number of people involved.

Understanding Road Accident Fatalities (Table II)

Table II provides an analysis of the causes of the fatalities (the 19% or 5,700 people mentioned above):

This table tells us why people died, but it is based on the percentage of people who were fatalities, not the total number of people involved in accidents.

Evaluating Conclusions from the Data

Now let's evaluate each of the given conclusions based on the data in Tables I and II.

Conclusion 1: drunken driving is dangerous.

The provided tables contain information about the severity of outcomes and causes of fatalities. They do not provide any data about the causes of the accidents themselves, such as drunken driving. Therefore, we cannot conclude this statement from the given data.

Conclusion 2: more accidents happen at night because of fatigue and poor visibility.

The tables do not contain any information about the time of day the accidents occurred or factors like fatigue and visibility. This conclusion cannot be drawn based on the data provided.

Conclusion 3: motorcycle drivers are at a greater risk of being involved in an accident than automobile drivers.

The data in the tables is aggregated for all people involved in road accidents. It does not differentiate between types of vehicles or the roles of the people involved (driver, passenger, pedestrian, etc.). Therefore, we cannot conclude anything about the relative risk for different types of drivers from this data.

Conclusion 4: the likelihood of surviving an accident is greater than that of dying in an accident.

From Table I, we know the percentage of people who were fatalities is 19%. The people who survived are everyone who was not a fatality. This includes those who were Uninjured, required First-Aid, had Mild Bruises, or required Hospitalization.

Percentage of people who survived = Percentage Uninjured + Percentage First-Aid Required + Percentage Mild Bruises + Percentage Hospitalization

Percentage of people who survived = $47\% + 6\% + 9\% + 19\%$

Percentage of people who survived = $81\%$

Comparing the likelihood (percentage) of surviving to the likelihood (percentage) of dying:

• Likelihood of surviving = $81\%$
• Likelihood of dying (fatalities) = $19\%$

Since $81\%$ is much greater than $19\%$, the likelihood of surviving an accident is indeed greater than that of dying in an accident, according to the data in Table I.

Based on the analysis of the options against the provided data, only the fourth conclusion can be supported by the information in Table I.

Revision Table: Key Road Accident Data Points

Additional Information on Road Accident Analysis

Analyzing road accident data is crucial for improving traffic safety. Data can be categorized in many ways, including:

• Severity of Injury: As seen in Table I, outcomes can range from uninjured to fatal. This helps understand the impact of accidents.
• Type of Vehicle: Data can show if certain vehicles (cars, motorcycles, trucks) are more frequently involved or have higher fatality rates. This was not provided in the question's tables.
• Cause of Accident: Factors like drunken driving, speeding, distracted driving, poor road conditions, or weather are often recorded. This information was missing from the provided data.
• Time and Location: Analyzing when (time of day, day of week) and where (specific roads, intersections) accidents occur helps identify high-risk areas and times. This was also missing here.
• Demographics: Age and experience of drivers involved can be important factors.

The tables provided focus specifically on the outcomes for people involved (severity) and the medical reasons for death among fatalities. While useful, a complete picture for devising safety measures would require data on causes, locations, and vehicle types.