Model response
The table compares six major cities’ underground rail systems in terms of their year of opening, total route length (in kilometres) and annual passenger numbers (in millions).
Overall, London’s network is by far the oldest and longest, while the most recent systems (Kyoto and Los Angeles) are also the shortest and carry the fewest passengers. Tokyo handles the greatest number of riders each year despite having a shorter network than London and Paris.
In detail, London opened its subway in 1863 and now operates 394 km of track—almost twice the 199 km of Paris’s system, which dates from 1900. Tokyo’s system began in 1927 and totals 155 km, followed by Washington DC (1976) at 126 km. At the lower end, Los Angeles (opened 2001) has just 28 km, and Kyoto (1981) only 11 km of route.
Passenger figures reflect a similar hierarchy. Tokyo carries about 1 928 million passengers annually, by far the highest, with Paris second at roughly 1 191 million and London third on 775 million. In contrast, Washington DC serves around 144 million riders each year, and both Los Angeles and Kyoto handle fewer than 50 million (approximately 50 million and 45 million respectively).
How This Report Was Developed: A Step-by-Step Guide for Beginners
- Paraphrase the Task
- What to do: Restate the prompt in your own words.
- Example: Prompt: “The table shows data about underground railway systems…”
Paraphrase: “The table compares six major cities’ underground rail systems in terms of…” - Why: Demonstrates vocabulary range and shows you understand the task.
- Write a Clear Overview
- What to do: Identify 2–3 main trends or striking features—no numbers yet.
- Example: “London’s network is the oldest and longest, while Kyoto and Los Angeles have the smallest systems and lowest passenger numbers. Tokyo carries the most riders each year.”
- Why: Examiners look for a concise summary of the “big picture.”
- Organize the Detail Paragraphs
- Grouping data: Split your details into logical groups to make it easy to follow.
- Early vs. Recent Systems: London/Paris/Tokyo vs. Washington/Kyoto/LA
- High vs. Low Figures: Top three cities vs. bottom three
- Example Paragraph 1 (Age & Length): “London opened in 1863 with 394 km of track—almost double Paris’s 199 km (opened 1900). Tokyo (1927) follows at 155 km, then Washington DC (1976) at 126 km. Kyoto (1981) and Los Angeles (2001) have only 11 km and 28 km respectively.”
- Example Paragraph 2 (Passenger Numbers): “Tokyo handles about 1 928 million passengers annually, with Paris at 1 191 million and London at 775 million. By comparison, Washington DC serves 144 million, and Kyoto and Los Angeles carry around 45 million and 50 million.”
- Why: Grouping prevents a “list” of six cities and makes comparisons clearer.
- Grouping data: Split your details into logical groups to make it easy to follow.
- Use Comparative Language and Approximation
- Comparatives & Contrast Words: almost twice, by comparison, in contrast, followed by
- Approximate Language: about, roughly, approximately
- Example: “London’s 394 km is almost twice Paris’s 199 km.”
“Tokyo carries about 1 928 million passengers, far more than Washington DC’s 144 million.” - Why: Shows grammatical range and keeps writing smooth.
- Employ Varied Vocabulary
- Swap simple verbs for more formal choices:
- “carries” or “serves” instead of “has” passengers
- “operates” instead of “runs” routes
- Use nouns/adjectives: “hierarchy”, “striking”, “majority”, “at the lower end”
- Why: Strong “Lexical Resource” score.
- Swap simple verbs for more formal choices:
- Stay Objective and Concise
- No opinions or reasons beyond the data.
- Keep sentences to the point—stick to what the table shows.
Quick Self-Check Before You Submit
- Have you paraphrased the prompt?
- Is there a clear overview with no data?
- Are the details organized into logical groups?
- Did you use comparative language and round your numbers?
- Is your vocabulary varied and your tone objective?
Following these steps will help you craft a concise, well-structured Task 1 report every time. Good luck!
How to Analyze and Compare Underground Railway Systems
A Comprehensive Guide to Tabular Data Analysis
Tabular data offers rich opportunities for comparison and analysis. This presentation will guide you through analyzing a data table about underground railway systems in six major cities, showing you how to identify patterns, make meaningful comparisons, and draw informed conclusions.
| City | Date opened | Kilometres of route | Passengers per year (in millions) |
|---|---|---|---|
| London | 1863 | 394 | 775 |
| Paris | 1900 | 199 | 1191 |
| Tokyo | 1927 | 155 | 1928 |
| Washington DC | 1976 | 126 | 144 |
| Kyoto | 1981 | 11 | 45 |
| Los Angeles | 2001 | 28 | 50 |
In this presentation, you will learn:
- How to structure your analysis of tabular data
- Key vocabulary for comparing and describing data
- Techniques for identifying meaningful patterns
- How to calculate and interpret derived metrics
- Methods for drawing insightful conclusions
Navigate using the buttons below
Understanding Tabular Data
Tabular data presents information in rows and columns, allowing for systematic comparison across multiple dimensions.
Key Components of Tabular Data:
Essential Elements:
- Headers: Define the variables/dimensions
- Rows: Represent individual items or entities
- Columns: Contain values for specific variables
- Data units: Indicate how values are measured
- Data types: Numerical, categorical, or temporal
What to Look For:
- Patterns: Recurring relationships in the data
- Extremes: Highest and lowest values
- Outliers: Values that differ significantly
- Correlations: Relationships between variables
- Groups/clusters: Items with similar characteristics
In Our Underground Railway Data:
- Entities: Six major cities (London, Paris, Tokyo, Washington DC, Kyoto, LA)
- Variables:
- Date opened (temporal data – year)
- Kilometers of route (numerical data – distance)
- Passengers per year (numerical data – millions of people)
- Potential patterns: Relationships between system age, size, and usage
Remember:
When analyzing tabular data, first understand what each column represents and identify the overall purpose of the comparison before diving into detailed analysis.
How to Structure Your Tabular Data Analysis
A well-organized analysis helps readers understand the key information and relationships presented in the data.
Four-Part Structure:
-
Introduction:
- State what the table shows (the topic and scope)
- Identify the main variables being measured
- Mention the entities being compared
- Briefly explain why this data is significant
-
Overview:
- Summarize the general patterns or notable features
- Highlight extremes or significant contrasts
- Identify any immediate groupings or clusters
- State the range or scope of the data
-
Detailed Analysis:
- Examine relationships between variables
- Calculate and discuss derived metrics
- Make specific comparisons between entities
- Identify anomalies and possible explanations
- Group entities based on meaningful criteria
-
Conclusion/Summary:
- Summarize the most significant findings
- Draw conclusions about patterns and relationships
- Suggest implications or broader context
- Identify limitations or areas for further analysis
Note on Organization:
For the detailed analysis section, you can organize information either by variable (discussing each column separately), by entity (focusing on each city in turn), or by pattern (grouping similar items). Choose the approach that best highlights the key insights in your specific data.
Writing an Effective Introduction
Your introduction should clearly establish what information the table presents and why it’s worth analyzing.
Components of a Good Introduction:
- Data type: Identify that it’s tabular data
- Subject matter: State what is being measured
- Entities: Mention which items are being compared
- Variables: Note the key dimensions of comparison
- Context: Briefly explain why this comparison matters
Example Introductions:
Simple Introduction:
The table presents data about underground railway systems in six major cities, showing when they were established, their network size, and annual passenger numbers.
More Detailed Introduction:
The table provides comparative information on underground railway systems across six major global cities, including their opening dates (ranging from 1863 to 2001), the extent of their networks in kilometers, and their annual ridership in millions of passengers. This data allows for analysis of how these transportation systems have developed over different time periods and how they currently serve their urban populations.
Things to Avoid:
- Don’t simply repeat the table title word-for-word
- Don’t use phrases like “As shown in the table…” or “This table presents…”
- Don’t include specific data points (save these for later sections)
- Don’t start analyzing patterns before establishing what the data represents
- Don’t make subjective judgments about the data in the introduction
Writing an Effective Overview
The overview provides a general summary of the main patterns and notable features in the data.
What to Include in the Overview:
- The range or scope of values for key variables
- Identification of extremes (highest and lowest values)
- General groupings or clusters among the entities
- Broad patterns or relationships between variables
- Notable anomalies or exceptions
Example Overviews:
Overview for Railway Data:
The data shows significant variation across all three variables. The underground railway systems span nearly 140 years of development, from London’s pioneering network in 1863 to Los Angeles’ relatively recent system in 2001. Network sizes range dramatically from Kyoto’s modest 11 kilometers to London’s extensive 394 kilometers. Perhaps most striking is the passenger volume disparity, with Tokyo serving nearly 2 billion riders annually while smaller systems like Kyoto handle just 45 million passengers per year.
Alternative Overview Approach:
The data reveals three distinct groups of underground systems: older, extensive networks with high ridership (London, Paris, Tokyo); mid-age systems with moderate size and usage (Washington DC); and newer, smaller systems with lower passenger numbers (Kyoto, Los Angeles). Generally, older systems tend to be larger and serve more passengers, though some newer Asian systems like Tokyo achieve remarkable efficiency despite their later development.
Key Features of a Good Overview:
- Keep it concise (2-3 sentences)
- Focus on broad patterns rather than specific figures
- Include the range of values for key variables
- Highlight the most significant contrasts
- Identify any preliminary groupings or clusters
Tip:
The overview is essential for orienting your reader to the scope and general patterns in the data. It demonstrates your ability to identify key features before delving into detailed analysis.
Essential Vocabulary for Making Comparisons
Using precise vocabulary helps you accurately describe similarities, differences, and relationships in tabular data.
Describing Similarities:
- Similar to / Resembles
- Comparable with / In line with
- Matches / Corresponds to
- Consistent with
- Parallels / Mirrors
- Equally / Similarly
- Also / Likewise
- As well as / Along with
Describing Differences:
- Different from / Differs from
- In contrast to / Unlike
- Whereas / While
- However / Conversely
- Diverges from / Deviates from
- Exceeds / Falls short of
- Outperforms / Underperforms
- Significantly less/more than
Describing Magnitude:
- Twice as large as / Half the size of
- Substantially higher/lower than
- Marginally above/below
- X times more/less than
- A fraction of / A multiple of
- Dramatically exceeds / Slightly less than
Describing Rankings:
- The highest / The lowest
- The oldest / The most recent
- Ranks first/second/third in
- Among the top/bottom
- Surpasses / Trails behind
- Leads in / Lags in
Describing Relationships Between Variables
Identifying connections between different variables is a key part of meaningful data analysis.
Types of Relationships:
Positive Relationships:
- Correlates with
- Increases with
- Grows alongside
- Is associated with
- Corresponds to rises in
Negative Relationships:
- Inversely related to
- Decreases as X increases
- Declines with rising
- Is negatively associated with
- Falls as X grows
Relationship Strength:
Strong Relationships:
- Strongly correlated
- Clearly linked to
- Closely follows
- Consistently associated with
- Demonstrably connected to
Weak/No Relationships:
- Weakly correlated
- Shows little relation to
- No clear connection with
- Independent of
- Not significantly linked to
Example Sentences Using Relationship Vocabulary:
In the railway data, system age generally correlates with network size, as older systems tend to have more extensive routes.
Passenger numbers are not consistently related to the age of the system, as Tokyo’s relatively newer network carries the most passengers.
For Asian systems, passenger volume is strongly associated with network size, with Tokyo’s larger network serving significantly more passengers than Kyoto’s smaller one.
The efficiency ratio (passengers per kilometer) is inversely related to network size in some cases, with smaller networks sometimes showing higher efficiency.
The opening date appears weakly correlated with geographical location, as systems opened in different eras across various continents.
Tip:
Be careful not to confuse correlation with causation. When you identify relationships between variables, you’re observing patterns in the data, not necessarily proving that one variable causes changes in another.
Creating and Using Derived Metrics
Derived metrics are calculations that combine existing data to reveal new insights not immediately apparent in the original table.
Common Derived Metrics:
- Ratios: Compare two related values (e.g., passengers per kilometer)
- Percentages: Express a value as a portion of the whole
- Growth/Change: Calculate how much a value has increased or decreased
- Rankings: Order entities based on specific variables
- Averages: Calculate means, medians, or modes for a set of values
Useful Derived Metrics for Railway Data:
System Efficiency:
Passengers per kilometer: Measure of how intensively each kilometer of track is used
| City | Passengers/km (millions) |
|---|---|
| Tokyo | 12.4 |
| Paris | 6.0 |
| Kyoto | 4.1 |
| London | 2.0 |
| Los Angeles | 1.8 |
| Washington DC | 1.1 |
System Maturity:
System age (years): Time since opening until present
| City | System Age (years) |
|---|---|
| London | 162 |
| Paris | 125 |
| Tokyo | 98 |
| Washington DC | 49 |
| Kyoto | 44 |
| Los Angeles | 24 |
Example Analysis Using Derived Metrics:
By calculating the number of annual passengers per kilometer of track, we can measure each system’s efficiency. Tokyo emerges as the most efficient system by a significant margin, carrying an average of 12.4 million passengers per kilometer annually. This is more than double Paris’s 6.0 million passengers per kilometer, despite Paris having a larger network. Interestingly, the oldest systems are not necessarily the most efficient, as London’s extensive network achieves only 2.0 million passengers per kilometer, comparable to much newer systems like Los Angeles (1.8 million). This suggests that factors beyond system age, such as urban density, public transit culture, and network design, play crucial roles in determining system efficiency.
Using Visual Comparisons
Visual representations can highlight differences and patterns more clearly than text alone.
Network Size Comparison:
Annual Passengers Comparison:
System Efficiency (Passengers per km):
Visual Analysis Tips:
When using visual representations, highlight the most significant contrasts. Notice how reordering the cities by different metrics reveals different patterns and relationships. In written analysis, refer to these visual patterns to support your observations.
Grouping and Categorizing Data
Organizing data into meaningful groups can reveal patterns and simplify complex comparisons.
Grouping by Geographic Region:
European Systems:
- London: Oldest system (1863), largest network (394 km)
- Paris: Second oldest (1900), second largest network (199 km)
- Characteristics: Extensive networks, high passenger volumes, developed over many decades
Asian Systems:
- Tokyo: Opened 1927, moderate size (155 km), highest passenger volume
- Kyoto: Opened 1981, smallest network (11 km), low passenger volume
- Characteristics: Vary significantly in size and usage, generally high efficiency
North American Systems:
- Washington DC: Opened 1976, moderate size (126 km), moderate passenger volume
- Los Angeles: Newest system (2001), small network (28 km), low passenger volume
- Characteristics: Developed later than European counterparts, lower ridership relative to network size
Grouping by System Age:
Historical Systems (pre-1925):
- London (1863)
- Paris (1900)
- Characteristics: Pioneering systems, developed during industrial revolution and early 20th century
Mid-Century Systems (1925-1975):
- Tokyo (1927)
- Characteristics: Developed during interwar period, influenced by early systems but with more modern technology
Modern Systems (post-1975):
- Washington DC (1976)
- Kyoto (1981)
- Los Angeles (2001)
- Characteristics: Built with contemporary technology, developed amidst established urban environments, smaller networks
Grouping the underground systems by age reveals distinct patterns in development. The historical systems (London and Paris) established extensive networks and set precedents for urban railway design. Tokyo represents a transitional case, developed using knowledge from earlier systems but adapted to different urban conditions. The modern systems (Washington DC, Kyoto, and Los Angeles) were built in already-established urban environments, resulting in generally smaller networks. This grouping demonstrates how the historical context of development influences system characteristics.
Conducting Detailed Feature Analysis
A thorough analysis examines specific aspects of the data in depth, drawing connections between different variables.
Sample Detailed Analysis – Historical Development:
The underground railway systems in the dataset span nearly 140 years of development, revealing distinct waves of urban transport evolution. London’s pioneering system opened in 1863 during the industrial revolution, when steam-powered technology first made underground railways possible. Paris followed nearly four decades later in 1900, representing a second wave of development influenced by London’s success but incorporating improved electric traction technology. Tokyo’s system emerged in 1927 during the interwar modernization period, while the remaining systems (Washington DC, Kyoto, and Los Angeles) represent post-World War II development, with significant gaps between openings. This chronology suggests that underground railway technology spread globally in distinct phases, with later systems benefiting from established engineering practices but often facing greater challenges in implementation due to existing urban density.
Sample Detailed Analysis – Efficiency Patterns:
Analyzing the relationship between network size and passenger volume reveals significant efficiency variations. Tokyo stands out with exceptional efficiency, carrying approximately 12.4 million passengers per kilometer annually, far exceeding all other systems. This efficiency likely reflects Tokyo’s high population density, transit-oriented development, and strong public transportation culture. Paris follows with 6.0 million passengers per kilometer, while London—despite having the most extensive network—achieves only 2.0 million passengers per kilometer. North American systems show the lowest efficiency, with Washington DC at just 1.1 million passengers per kilometer, suggesting greater reliance on private transportation and different urban development patterns. Interestingly, Kyoto’s small system achieves relatively high efficiency (4.1 million passengers per kilometer), indicating that system size alone does not determine efficiency. These patterns suggest that cultural attitudes toward public transportation and urban planning approaches significantly influence system usage regardless of network extent.
Key Analysis Approaches:
- Look for patterns and anomalies: Identify both consistent trends and exceptions to those trends
- Connect multiple variables: Explore how different dimensions of the data relate to each other
- Consider context: Think about external factors that might explain the patterns you observe
- Use derived metrics: Calculate relevant ratios or other metrics to deepen your analysis
- Avoid overgeneralizing: Acknowledge limitations in the data and variation within groups
Writing an Effective Conclusion
A good conclusion summarizes key findings and draws meaningful insights from your analysis.
Components of a Good Conclusion:
- Summary of key patterns: Recap the most significant trends or relationships
- Main insights: Highlight the most important findings from your analysis
- Context or implications: Suggest what the data might mean in a broader context
- Limitations: Acknowledge any constraints in your analysis or the data itself
Example Conclusions:
Basic Conclusion:
In conclusion, the data reveals significant variation in underground railway systems across all dimensions analyzed. Older systems tend to have more extensive networks, while Asian systems generally demonstrate the highest efficiency in terms of passengers per kilometer. The contrast between Tokyo’s high efficiency and the relatively low efficiency of North American systems highlights the importance of urban planning and transit culture in system utilization.
More Detailed Conclusion:
The analysis of underground railway systems across six cities reveals several key insights. First, system development occurred in distinct historical waves, with pioneering systems generally achieving larger networks over time. Second, network size does not directly correlate with efficiency, as demonstrated by Tokyo’s exceptional passenger volume despite its moderate network size. Third, regional patterns are evident, with Asian systems showing high efficiency, European systems having extensive networks with moderate to high usage, and North American systems exhibiting lower efficiency. These patterns reflect the influence of urban density, cultural attitudes toward public transportation, and historical development contexts. Future analysis could benefit from additional data on population density, funding models, and expansion patterns to further understand the factors driving these differences.
Things to Avoid in Conclusions:
- Introducing entirely new data or concepts not mentioned in your analysis
- Making definitive claims about causation without sufficient evidence
- Focusing on minor details rather than significant patterns
- Offering prescriptive recommendations without a clear basis in the data
- Failing to acknowledge limitations or potential alternative explanations
Remember:
Your conclusion should reflect the depth of your analysis. If you’ve conducted a detailed examination of the data, your conclusion should synthesize multiple insights rather than simply restating obvious facts.
Common Mistakes to Avoid
Being aware of these common errors will help you produce more effective data analyses.
Structure Issues:
- Lack of organization: Random discussion without clear structure
- Missing overview: Diving into details without establishing context
- Imbalanced coverage: Focusing too much on one aspect of the data
- No clear progression: Jumping between points without logical flow
Language Issues:
- Vague descriptions: “A lot” instead of specific figures
- Repetitive vocabulary: Using the same comparison terms
- Inconsistent units: Switching between different measurements
- Imprecise comparisons: “Better” without specifying criteria
Content Issues:
- Data regurgitation: Listing figures without analysis
- Overlooking anomalies: Ignoring data that doesn’t fit patterns
- Missing derived metrics: Not calculating useful ratios
- Failure to group: Not organizing data into meaningful categories
Analytical Issues:
- Confusing correlation with causation: Assuming relationships are causal
- Overgeneralizing: Making broad claims from limited data
- Ignoring context: Not considering external factors
- Unsupported conclusions: Making claims not based on the data
A crucial pitfall to avoid:
One of the most common mistakes is simply describing the data without analyzing relationships between variables or identifying patterns. Remember that effective analysis goes beyond observation to include interpretation, comparison, and the identification of meaningful connections within the data.
Sample Analysis Structure
Let’s examine the structure of a complete analysis of the underground railway systems data.
Introduction:
- Identifies the topic (underground railway systems)
- Notes the six cities being compared
- Lists the three key variables (age, size, usage)
- Mentions the purpose of comparison
Overview:
- Notes the historical span (1863-2001)
- Highlights the range in network sizes
- Emphasizes the variation in passenger volumes
- Identifies initial groupings (by age/region)
Detailed Analysis:
- Historical development analysis (chronological)
- Network size comparison (from largest to smallest)
- Efficiency analysis (derived metric: passengers/km)
- Regional comparison (grouped by continent)
- Age-based pattern analysis (correlation with other variables)
Conclusion:
- Summarizes key patterns across all variables
- Highlights the relationship between age, size, and efficiency
- Notes regional differences in system characteristics
- Suggests cultural and historical factors affecting development
Analysis Approach:
This analysis uses multiple perspectives to examine the data: chronological analysis traces development over time; comparative analysis contrasts different systems directly; categorical analysis groups similar systems; and relationship analysis explores connections between variables. By approaching the data from these different angles, the analysis reveals both obvious and subtle patterns in the development and characteristics of underground railway systems.
Complete Sample Analysis
The table presents comparative data on underground railway systems across six major cities worldwide, showing when each system was established, the extent of its network in kilometers, and its annual passenger volume in millions. The data spans systems from three continents and covers nearly 140 years of urban transportation development.
Overall, the data reveals significant variation across all dimensions. The systems span from London’s pioneering network in 1863 to Los Angeles’ relatively recent system established in 2001. Network sizes range dramatically from Kyoto’s modest 11 kilometers to London’s extensive 394 kilometers. Perhaps most striking is the passenger volume disparity, with Tokyo serving nearly 2 billion riders annually while smaller systems like Kyoto and Los Angeles handle fewer than 50 million passengers per year. The data suggests three distinct groups: older, extensive European networks; high-capacity Asian systems; and newer, smaller North American systems.
Looking at historical development, the underground systems were established in distinct waves that reflect technological and urban development patterns. London’s system, opened in 1863, was the world’s first underground railway, developed during the industrial revolution when steam-powered technology made such systems possible. Paris followed nearly four decades later in 1900, representing a second wave that incorporated improved electric traction. Tokyo’s system, established in 1927, emerged during interwar modernization efforts. The remaining systems represent post-World War II development, with Washington DC opening in 1976, Kyoto in 1981, and Los Angeles much later in 2001. This chronology shows how underground railway technology spread globally in distinct phases, with later systems benefiting from established engineering but facing greater challenges due to existing urban development.
In terms of network size, London has by far the most extensive system at 394 kilometers, more than twice the size of Paris (199 km) and over 35 times larger than Kyoto’s compact system (11 km). This variation partly reflects system age, as older networks have had more time for expansion. However, the relationship is not perfect: Tokyo’s system, despite being 60+ years younger than London’s, has developed a substantial network of 155 km, while Washington DC (opened 1976) has already achieved 126 km. This suggests that factors beyond age, such as urban geography, population density, and transportation policy, significantly influence network development.
The most revealing insights emerge when analyzing system efficiency, calculated as passengers per kilometer of track. Tokyo stands out with exceptional efficiency, carrying approximately 12.4 million passengers per kilometer annually, more than double Paris’s 6.0 million passengers per kilometer and over six times higher than London’s 2.0 million. This remarkable efficiency likely reflects Tokyo’s high population density, transit-oriented development, and strong public transportation culture. Interestingly, Kyoto’s small system achieves relatively high efficiency (4.1 million passengers per kilometer), outperforming much larger networks like London. North American systems show the lowest efficiency, with Washington DC at just 1.1 million passengers per kilometer, suggesting greater reliance on private transportation and different urban planning approaches.
When grouping the systems by geographic region, distinct patterns emerge. European systems (London and Paris) are characterized by their historical development, extensive networks, and high absolute passenger numbers, though with moderate efficiency. Asian systems (Tokyo and Kyoto) display the highest efficiency metrics despite vast differences in network size, suggesting regional factors such as urban density and transit culture significantly influence usage patterns. North American systems (Washington DC and Los Angeles) represent more recent development with lower efficiency ratios, reflecting different urban planning traditions that often prioritized automobile transport during their critical development periods.
In conclusion, this analysis reveals several key insights about underground railway systems. First, system development occurred in distinct historical waves, with pioneering systems generally achieving larger networks over time. Second, network size does not directly correlate with efficiency, as demonstrated by Tokyo’s exceptional passenger volume despite its moderate network size. Third, regional patterns are evident, with Asian systems showing high efficiency, European systems having extensive networks with moderate to high usage, and North American systems exhibiting lower efficiency. These patterns suggest that cultural attitudes toward public transportation, urban density, and historical development contexts significantly influence system characteristics. Future analysis could benefit from additional data on population density, funding models, and expansion patterns to further understand the factors driving these differences.