Time Series Data on Road Transport Passenger and Freight Movement (1951-1991)

IRC SP 45: Background - Key Formulas & Tables

1. Freight Movement by Agricultural Tractors (Clause 34.1)

• Freight in billion tonne-km (AGRT BTKn) for year n (from 1961):
  [ AGRT_{BTK_n} = 34.1 \times (1 + 0.1299)^{(n-1961)} ]

• Population of Agricultural Tractors (in thousands) for year n (from 1951):
  [ AGRT_n = 9.56 \times (1 + 0.131)^{(n-1951)} ]

• Average annual growth rate of tonne-km by agricultural tractors:

  Period	Growth Rate (%)
  1951-60	12.4
  1961-70	12.6
  1971-80	12.0
  1981-91	11.9
  1951-91	13.0

2. Total Freight Movement by All Modes (Clause 9.73)

• Annual growth rate: ~9.7% (1951-91)

• Growth rates by decade:

  Period	Growth Rate (%)
  1951-60	9.6
  1961-70	10.5
  1971-80	6.6
  1981-91	12.3
  1951-91	9.7

• Example Freight Data (Billion Tonne-km):

  Year	Freight (Billion T-km)
  1951	12.09
  1961	32.53
  1971	82.36
  1981	178.36
  1991	566.66

3. Passenger-km by Buses (Formula)

Key Specifications & Data for Road Transport Passenger Movement (IRC SP 45)

Modes Considered:

• Buses
• Cars
• Two-wheelers
• Three-wheelers
• Cycles
• Cycle-rickshaws

1. Bus Passenger Movement (Key Table Extract)

• Population: Number of buses in thousands.
• Passenger-km: Total passenger kilometers traveled by buses.
• Utilisation: Average kilometers run per bus annually.
• Occupancy Ratio: Percentage of seating capacity utilized.

2. Regression Equation for Bus Passenger-km:

The bus passenger kilometers (in billion) for year n can be estimated using a regression fit (specific formula not provided in excerpt but typically of form):

[ P_n = a \times e^{b(n - n_0)} ]

Where:

• (P_n) = passenger-km in year (n)
• (a, b) = regression constants from data fitting
• (n_0) = base year (e.g., 1951)

3. Usage Notes:

• Data spans 1951-1991.
• Missing data computed via regression.
• Useful for planners, researchers, and transport operators.

flowchart LR
    A[Road Transport Passenger Movement] --> B[Buses]
    A --> C[Cars]
    A --> D[Two-Wheelers]
    A --> E[Three-Wheelers]
    A --> F[Cycles]
    A --> G[Cycle Rickshaws]

    B --> H[Population]
    B --> I[Passenger-km]
    B --> J[Utilisation]
    B --> K[Occupancy Ratio]

**For detailed tables on other modes and

IRC SP 45: Road Transport Freight Movement

Key Points from Clause 3 & Table 13

• Total Freight Movement is expressed in million tonne-kilometers (mt-km).
• The table provides time series data (1951-1991) showing growth trends in freight movement by road.
• Freight movement data helps in traffic forecasting and pavement design.

Typical Use of Data:

• Freight Volume (Q) and Distance (D) are multiplied to get tonne-km:

  [ \text{Freight Movement (mt-km)} = Q \times D ]

• Growth trends from the table are used to estimate future freight traffic for design life.

Example Table Format (simplified):

Application:

• Use growth rates from the table for traffic projections.
• Essential for pavement thickness design as freight loads cause significant pavement damage.

flowchart LR
    A[Freight Quantity (Q)] --> B[Multiply by Distance (D)]
    B --> C[Freight Movement (mt-km)]
    C --> D[Traffic Forecasting]
    D --> E[Pavement Design]

For detailed values, refer to Table 13 in IRC SP 45.

Use of Data - IRC SP 45 (Summary & Key Formulas)

The "Use of Data" section compiles time-series data (1951-1991) on road transport passenger and freight movement, crucial for planners and researchers. The data is from multiple sources, with missing values estimated via regression.

Key Specifications & Formulas

1. Passenger-Km by Buses

Calculated as:
[ \text{BPK}_n = B_n \times U_n \times OR_n \times 52 \times 10% ]

• BPKₙ = Bus passenger km in billion (year n)
• Bₙ = Bus population in thousands (year n)
• Uₙ = Utilisation per bus (km/year)
• ORₙ = Occupancy ratio (%)
• 52 = Assumed seating capacity factor (weeks/year)
• 10% = Adjustment factor (possibly for average occupancy)

2. Freight Movement Growth Rates

Annual growth rates for total freight movement (1951-1991):

3. Freight Movement Data

Total freight movement in Billion T-km increased from 12.09 (1951) to 566.66 (1991), showing rapid growth.

Notes on Data Use

• Data is historical; future projections require economic context.
• Missing data were estimated by regression.
• Use immediate past trends for short-term forecasts; long-term needs scenario analysis.

Visualization: Passenger-Km by Buses Trend

graph LR
A[Bus Population] --> B[Utilisation (km/year)]
B --> C[Occupancy Ratio]
C --> D[Passenger-Km Calculation]
D --> E[Transport Planning]

This data supports transport demand forecasting and infrastructure planning aligned with economic growth.

IRC SP 45 - Notes Section: Key Highlights

The "Notes" section (Page 27) in IRC SP 45 primarily provides clarifications and explanations related to the data and tables presented in the document, which cover road transport passenger and freight movement.

Important Tables Referenced:

• Table 1 to Table 7: Passenger movement data by vehicle type (Buses, Cars, Two/Three Wheelers, Cycles, Rickshaws).
• Table 8 to Table 13: Freight movement data by vehicle type (HCV, LCV, Three Wheelers goods, Agricultural Tractors, Animal Drawn Vehicles).

Key Specifications:

• Passenger-km and Freight-km are the main metrics used to quantify transport movement.
• Population data of vehicles combined with average trip lengths yield these metrics.
• The notes clarify assumptions, data sources, and calculation methods.

Typical Formula for Passenger-km or Freight-km:

[ \text{Passenger-km} = \text{Number of Vehicles} \times \text{Average Passengers per Vehicle} \times \text{Average Distance Travelled (km)} ]

[ \text{Freight-km} = \text{Number of Vehicles} \times \text{Average Load per Vehicle (tons)} \times \text{Average Distance Travelled (km)} ]

Summary Table Example (Passenger Movement):

If you need specific data values or detailed notes, please specify the vehicle type or table number.

IRC SP 45 - Bibliography & Key Formulas Summary

Bibliography Highlights

• Reports from Planning Commission (1980-1997), Indian Roads Congress, Central Road Research Institute.
• Key studies on road transport statistics, axle loads, and road user costs.
• Important for planners, researchers, and transport operators.

Key Freight Movement Formulas

Passenger Movement Table Example (Buses)

Notes:

• Data covers 1951-1991 with regression fits for missing values.
• Utilisation and occupancy ratios indicate efficiency trends.

flowchart LR
    A[Vehicle Population] --> B[Average Load & Trips]
    B --> C[Annual Tonne-Km Calculation]
    C --> D[Freight Movement Estimation]

This summary aids in understanding road transport data compilation and freight/passenger movement estimation per IRC SP 45.

Key Data & Formulas from IRC SP 45: Bus Population & Passenger-km

Table 1: Bus Population and Passenger-km by Buses (Sample data)

Important Specifications:

• Bus Population: Number of registered buses (in thousands).
• Passenger-km: Total passenger kilometers travelled by buses (in billions).
• Utilisation per bus: Average kilometers run per bus annually (in thousands).
• Occupancy Ratio: Average percentage of seats occupied.

Formula for Bus Passenger Kilometers (Regression Fit):

[ \text{Passenger-km}_n = f(\text{Year}_n) ]

Note: The exact regression formula is not provided in the excerpt. Typically, it is a linear or exponential fit based on historical data.

Usage:

• Passenger-km helps estimate total passenger traffic for planning.
• Utilisation & occupancy ratios are key for operational efficiency.

flowchart LR
    A[Bus Population] --> B[Utilisation per Bus (km/year)]
    B --> C[Total Bus Kilometers]
    C --> D[Occupancy Ratio]
    D --> E[Passenger-km]

This diagram shows how bus population and utilization combine with occupancy to yield passenger-km.

For detailed planning, refer to the full Table 1 in IRC SP 45 for year-wise trends and use regression fits to forecast future values.

IRC SP 45: Car Population & Passenger-km by Cars

Key Table: Car Population and Passenger-km (1951-1991)

(Complete data from 1951 to 1991 available in Table 2 of IRC SP 45)

Important Notes:

• Population ('000): Number of cars in thousands.
• Passenger-km (Billion): Total passenger kilometers traveled by cars.

Growth Trends & Equations (Derived from Data):

While explicit formulae for cars are not given, similar to buses, growth can be approximated by exponential or linear regression for forecasting.

For buses (as example):

• Utilisation (km/year):
  [ U = 37,086 \times (1 + 0.024)^n ] where ( n = \text{year} - 1951 )

• Occupancy Ratio (%):
  [ OR = 54 + 0.8n ]

For cars, use the tabulated data for trend analysis or fit curves for population and passenger-km growth.

Practical Use:

• Use the population data to estimate vehicle density.
• Use passenger-km to estimate travel demand by cars.
• Combine with occupancy and utilization factors for traffic and infrastructure planning.

graph LR
A[Car Population] --> B[Passenger Movement]
B --> C[Passenger-km]
C --> D[Traffic Demand Analysis]

For detailed design or planning, refer to Table 2 of IRC SP 45 for year-wise data and use regression for projections.

IRC SP 45: Two Wheelers Population & Passenger-km

Key Data (Table 3)

Growth Rates (Clause 17.7)

Regression Formula for Passenger-km (Eqn. 7)

[ \text{TW PK}_n = 0.15 \times (1 + 0.177)^n ]

• (n = \text{year} - 1951)
• TW PK(_n) = Two-wheeler passenger kilometers in billion for year (n).

Summary:

• Two-wheeler population and passenger-km grew at ~17.7% annually (1951-91).
• Use the regression formula to estimate passenger-km for any year after 1951.
• Population data and passenger-km are provided in Table 3 for detailed analysis.

graph LR
A[Year 1951] --> B[Population: 27,000]
B --> C[Passenger-km: 0.2 Billion]
C --> D[Growth @ 17.7% annually]
D --> E[Year 1991: Population 13,845,000]
E --> F[Passenger-km: 102.3 Billion]

This data aids traffic forecasting and infrastructure planning for two-wheelers.

IRC SP 45: Three Wheelers — Population & Passenger-km

Key Table: Three Wheelers Population & Passenger Movement (Table 4)

Regression Formula for Passenger-km (Eqn. 9)

[ \text{THW PK}_n = 0.37 \times (1 + 0.169)^n ]

• THW PK_n = Three-wheeler passenger kilometers in year (n) (in billion)
• (n = \text{year} - 1961)
• Growth rate = 16.9% per annum

Notes:

• Population data for three-wheelers can be extrapolated similarly using growth rates if needed.
• Passenger-km indicates total passenger movement, useful for traffic and pavement design.
• For comparison, two-wheelers grow at ~17.7% annually (Table 3 & Clause 17.7).

Summary:

This formula helps estimate future passenger movement by three-wheelers for traffic forecasting and infrastructure planning.

Passenger-km by Cycles (IRC SP 45)

Key Formula:

[ \text{CPK}_n = \frac{C_n \times 5 \times 365}{10^6} ]

• CPKₙ = Passenger-km by cycles in billion for year n
• Cₙ = Cycle population in thousands for year n
• 5 km = Average daily trip length per cycle
• 365 = Days per year
• 10^6 = Conversion factor to billion

Passenger-km by Cycle Rickshaws

• Calculated similarly, with occupancy and utilization factors specific to cycle rickshaws.
• Refer to Table 6 for population and passenger-km data.

Multiplying Factor for Three-Wheelers (Clause 1.76)

• 1.76 accounts for average occupancy in three-wheelers.

Summary Tables (from IRC SP 45)

This approach captures the annual passenger-km by multiplying population, daily trip length, and days per year, adjusted by occupancy factors where applicable.

Passenger-km by Cycle Rickshaws (IRC SP 45)

Key Formula:

Passenger-km by cycle rickshaws is derived considering the average occupancy factor (1.76) for three-wheelers.

For cycles (non-rickshaw):

[ \text{CPKn} = \frac{C_n \times 5 \times 365}{10^6} ]

• ( CPKn ) = Passenger km by cycles in billion in year ( n )
• ( C_n ) = Cycle population in thousands in year ( n )
• Factor 5 × 365 = daily utilization of cycles up to 5 km

For cycle rickshaws, multiply by 1.76 to account for average occupancy.

Tables Summary

Table 4: Three Wheelers Population & Passenger-km

Table 5: Passenger-km by Cycles

Notes:

• Use Table 4 for three-wheeler (cycle rickshaw) population and passenger-km data.
• Use Table 5 for cycles passenger-km data.
• Multiply cycle rickshaw passenger-km by 1.76 for occupancy factor.
• Daily average trip length for cycles is assumed as 5 km.

flowchart LR
    Cycle_Population["Cycle Population (Cₙ)"]
    Daily_Utilization["Daily Utilization (5 km × 365 days)"]
    Passenger_km_Cycles["Passenger-km by Cycles (CPKn)"]
    Occupancy_Factor["Occupancy Factor (1.76)"]
    Passenger_km_Rickshaw["Passenger-km by Cycle Rickshaws"]

    Cycle_Population -->|Multiply| Passenger_km_Cycles
    Daily_Utilization -->|Multiply| Passenger_km

IRC SP 45: Total Passenger-km by All Modes

The total passenger-kilometers (Passenger-km) is a key metric for traffic and transport analysis, representing the product of the number of passengers and the distance traveled.

Key Tables and Formulas from IRC SP 45:

General Formula for Passenger-km:

[ \text{Passenger-km} = \sum (\text{Number of vehicles} \times \text{Average passengers per vehicle} \times \text{Average distance traveled}) ]

Example:

• For buses:
  [ \text{Passenger-km}_{bus} = \text{Bus population} \times \text{Average passengers per bus} \times \text{Average km traveled per bus} ]

• Similarly for cars, cycles, and other modes.

Specifications:

• Use average occupancy rates from respective tables.
• Use average trip lengths as per local surveys or IRC recommendations.
• Total Passenger-km is the sum over all modes:

[ \text{Total Passenger-km} = \text{Passenger-km}{bus} + \text{Passenger-km}{car} + \text{Passenger-km}_{cycle} + \ldots ]

flowchart LR
    A[Vehicle Population] --> B[Average Passengers per Vehicle]
    B --> C[Average Distance Travelled]
    C --> D[Passenger-km per Mode]
    D --> E[Sum over all Modes]
    E --> F[Total Passenger-km]

This approach helps in transport planning, capacity assessment, and infrastructure design.

Key Formulas and Tables from IRC SP 45: HCV Population & Freight

1. HCV Population and Freight (Table 8)

(Full table available in code for years 1951-1991)

2. Growth Rate of HCV Population (Clause 6.1)

3. Freight Movement Calculation (HCV)

[ \text{Freight Movement} = HCV_n \times U_n ]

Where:

• (HCV_n) = Population of HCVs in thousands in year (n)
• (U_n) = Average annual utilization of trucks in year (n)

[ U_n = 28,910 \times (1.019)^{(n - 1951)} ]

4. Freight Movement by LCVs (Table 9 & Eqn.)

[ \text{LCV Freight (Billion Tonne-km)} = 0.73 \times (1 + 0.149)^{(n - 1961)} ]

Where (n) = year

Summary:

• HCV population and freight data is tabulated for 1951-1991.
• Annual utilization of trucks grows at 1.9% per year.
• Freight movement is proportional to the product of HCV population and utilization.
• LCV freight follows an exponential growth with base 1.149 from 1961.

Key Formulas & Tables for LCV Population and Freight (IRC SP 45)

1. LCV Freight Movement Regression Equation

Freight movement by LCVs (in Billion Tonne-km) for year n (where n = year - 1961):
[ \boxed{ \text{LCV BTKn} = 0.73 \times (1 + 0.149)^n } ]

• This shows exponential growth at 14.9% annually.

2. Average Load Carried by Trucks (Wn)

• For 1951-1981:
  [ W_n = 4.12 \times (1.019)^n \quad \text{where } n = \text{year} - 1951 ]
• For 1981-1991:
  [ W_n' = 7.25 \times (1.0045)^{n'} \quad \text{where } n' = \text{year} - 1981 ]

3. LCV Population and Utilisation

• Population of LCVs in year n (in thousands): (LCV_n)
• Utilisation in year (n') (km/year):
  [ U_{n'} = 134 \times 365 \times (1 + 0.019)^{n'} ] where (n' = 1990 - \text{year})

4. Table 9 (Excerpt) - LCV Population and Freight

(Refer to IRC SP 45 Table 9 for full data)

Summary:

• Use Eqn. 16 for freight growth by LCVs.
• Average load and utilization increase gradually with time, modeled by exponential growth.
• Population data combined with load and utilization gives total freight movement.

flowchart LR
    A[Year (n)] --> B{Calculate n = year-1961}
    B