public transportation notes by Dr. Ravi Raj M Sir NITK

1. 1

2.  Performance is defined as the levels of success of the service with respect to
different parameters such as cost effectiveness, quality of service and safety.
 Operational Efficiency of an organization is the ability to utilize its available
resources to the maximum extent (output / input).
 Financial Efficiency is a measure of the organization’s ability to translate its
financial resources into revenue (mission related activities)
 Non-parametric method is used when the researcher does not know anything
about the parameters chosen. Hence, this method is referred as parameter-free
or distribution-free method.
 Multivariate analysis (MVA) involves observation and analysis of more than
one statistical outcome variable at a time.
2
Operational (Physical) Parameters* Financial Parameters*
1. Number of Schedules 6. Fuel Consumed 11. Rate of Breakdown 1.Traffic Revenue
2. Fleet Held 7. KMPL 12. Rate of Accidents 2. Revenue per Vehicle
3. Effective Kilometers 8. Total employees 13. Accuracy of
Departures
3. Staff cost
4. Vehicle Utilization 9. Staff per schedule 14. Accuracy of Arrival 4. EPKM
5. Fleet Utilization 10. Staff productivity 5. CPKM
6. EPKM/CPKM
* - As per ASRTU

3. Motivation for the study
 Lot of investment is being made on transport sector for providing
better mobility for public through State Road Transport Units
(SRTUs) like, JNNURM, World bank etc.
 However, all the state Governments are looking at these
corporations to perform better both providing excellent service
and make profits
 Evaluation of these SRTUs are becoming mandatory. But, there
are very few performance evaluation techniques which can
quantify the performance of these SRTUs.
 In this work we will look at more scientific analysis of the
performance of the SRTUs, considering physical and financial
parameters through non-parametric and multivariate techniques.
The number of passengers
carried per day has
increased 5.5 times when
compared to investments
with 151 times over period
of 5 decades.
3

4. Data collection
• Quantitative data
 physical and
 financial parameters
• Qualitative data
 User perception data
 Operator perception data
Statistical departments of
(depots/divisions of SRTUs)
Questionnaire
Survey
4
Note :depots/divisions of SRTUs are called as Decision Making Units (DMUs)

5. 5
Physical Parameters Financial Parameters
1. Number of Schedules 6. Fuel Consumed 11. Rate of Breakdown 1.Traffic Revenue
2. Fleet Held 7. KMPL 12. Rate of Accidents 2. Revenue per Vehicle
3. Effective Kilometers 8. Total employees 13. Accuracy of
Departures
3. Staff cost
4. Vehicle Utilization 9. Staff per schedule 14. Accuracy of Arrival 4. EPKM
5. Fleet Utilization 10. Staff productivity 5. CPKM
6.EPKM/CPKM
(Profitability)

6. 6
Definition
Number of vehicles 1 Number of buses available in the depot.
Number of employees 2 Total number of employees working in the depot
Fuel consumed 3 Total fuel consumed in lakh liters
Number of schedules 4 Total number of schedules operated by the depot
Effective Kilometers 5 Total kilometers operated by the depot from bus stations
Revenue (Rs)
6 It is defined as the traffic revenue generated per depot in
a year.
Staff productivity
(Km)
7 It is defined as the ratio of the total number of
kilometers operated by all the vehicles assigned to the
depot to the total number of staff assigned to the depot
Breakdown rate
8 It is defined as the number of vehicle breakdown per
10000 vehicle kilometers
Accident rate
9 It is defined as the number of vehicle accidents per
100000 vehicle Kilometers
Fuel efficiency (Kmpl)
10 It represents fuel efficiency of vehicle operation
measured in Kilometer per litre of fuel consumption.
Earning per kilometer
(Paise)
11 It is calculated as the ratio of total revenue generated to
the average passenger kilometers.

7. 7
Definitions
Cost per kilometer
(Paise)
12 It is calculated as the total expenditure to the average
passenger kilometres.
Profitability
13 It is defined as the ratio of earning per kilometer (EPKM)
to the cost per kilometer(CPKM).
Vehicle utilisation
(Kms)
14
It is the ratio of total kilometers operated by all the vehicles
on a given day to the total number of vehicles on road.
Fleet utilisation (%) 15 It is defined as the ratio of total number of vehicles on road
to the total number of vehicles held by the depot.
Staff per schedule
16
It is the ratio of the number of staff members assigned to the
depot to the number of schedules operated by the depot.
Revenue per vehicle
(Rs)
17 Ratio of total revenue generated by all the vehicles to the
total vehicles on road that day
Staff cost (Paise)
18 It is calculated as total expenditure on the staff to the
average passenger kilometers.
Accuracy of departure
(%)
19 It is the ratio of total schedules departed late to the total
number of schedules
Accuracy of arrival
(%)
20 It is the ratio of total schedules arrived late to the total
number of schedules

8. 8
1000.00
1300.00
1600.00
1900.00
2200.00
2500.00
2800.00
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Earnings
Per
Kilo
Meter
in
Paise
Financial Year
Earning per kilometer
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet

9. 9
0.75
0.80
0.85
0.90
0.95
1.00
1.05
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
EPKM
/
CPKM
Financial Year
EPKM/CPKM
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar

10. 10
80.0
82.0
84.0
86.0
88.0
90.0
92.0
94.0
96.0
98.0
100.0
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Fleet
utilisation
in
% Fleet utilisation
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir

11. 11
220
300
380
460
540
620
700
780
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Number
of
Schedules
Financial Year
Number of Schedules
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet

12. 12
1000
1400
1800
2200
2600
3000
3400
3800
4200
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Total
Number
of
Employees
Financial Year
Total number of employees Bangalore
central
Bangalore
rural
Tumkur
Kolar
Chikkaballa
pur
Hassan
Chikamaglo
re
Mangalore
Davanagere
0.000
0.200
0.400
0.600
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Break
down
rate
per
10000Kms
Financial Year
Breakdown rate per 10000 Kms
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
0.000
0.100
0.200
0.300
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Accident
rate
per
Lakh
Kms
Financial Year
Accident rate per 100000Kms Bangalore
central
Bangalore
rural
Tumkur
Kolar
Chikkaballa
pur
Hassan
Chikamaglo
re
Mangalore
Davanagere
Bagalkote
4.20
4.70
5.20
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Kilo
meter
per
liter
o
f
Diesel
Financial Year
KMPL
Bangalore
central
Bangalore
rural
Tumkur
Kolar
Chikkaballap
ur
Hassan
Chikamaglore
Mangalore
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Staff
per
Schedule
Financial Year
Staff per schedule
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Hassan
Chikamaglore
Mangalore
Davanagere
1000
1400
1800
2200
2600
3000
3400
3800
4200
2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Total
Number
of
Employees
Financial Year
Total number of employees Bangalore
central
Bangalore
rural
Tumkur
Kolar
Chikkaballa
pur
Hassan
Chikamaglo
re
Mangalore
Davanagere

13. Ratio and Benchmarking analysis
 Ratio Analysis is an analytical tool that can be used to compare the
performance of one DMU with the other DMU of State Transport
Corporation.
 The following ratios can be considered in this study.
5. Fuel Efficiency
6. Profitability (EPKM/CPKM)
7. Earnings per kilometer
8. Cost per kilometer
1. Fleet Utilization
2. Vehicle Utilization
3. Staff per schedule
4. Staff productivity
 Benchmarking involves comparing properly computed ratios for
similar DMUs across the organization. The main objective of the
benchmarking is to compare the performance of the DMUs with
respect to the top performing entity in its peer group. The slack or
surplus that a DMU incurs in comparison to the top performing DMU
helps us in setting targets for improvement.
13

14. 14
The fuel savings computed as:
The total savings = Savings from fuel + Savings from staff cost + Savings from
vehicle utilisation (capital expenditure).

15. 15
The savings from staff related expenditure is computed as:
The additional revenue that can be generated by increasing the vehicle utilization:

16. 2010-11
Vehicle
utilisation
Fleet
utilisation
Staff per
schedule
Staff
productivity KMPL EPKM CPKM profitability
Bangalore central 455 90 5.92 74 4.38 2641.50 2641.70 1.00
Bangalore rural 330 92 4.81 66 5.04 1988.60 2195.60 0.91
Tumkur 358 92 4.38 79 5.16 1848.10 2044.30 0.90
Kolar 357 94 5.13 68 5.23 1809.60 2047.90 0.88
Chikkaballapur 355 92 5.04 68 5.13 1808.70 1965.50 0.92
Mysore urban 241 96 5.31 46 4.34 2318.20 2862.90 0.81
Mysore rural 380 92 4.82 77 4.80 2094.90 2211.40 0.95
Mandya 326 93 4.47 69 5.19 1920.40 1975.90 0.97
Chamaraj Nagar 325 91 4.44 67 5.06 1728.10 1999.10 0.86
Hassan 354 96 4.41 82 5.11 1865.00 2080.20 0.90
Chikamaglore 379 94 4.59 83 5.05 1830.20 2009.50 0.91
Mangalore 385 90 4.30 86 4.30 2213.90 2394.40 0.92
Davanagere 368 93 5.53 69 5.17 1852.20 2185.90 0.85
Bagalkote 348 91 5.26 65 5.02 2188.00 2187.75 1.00
Belgaum 333 93 5.04 64 5.04 1866.78 2179.33 0.86
Chikkodi 357 91 5.15 68 5.19 1828.85 2054.39 0.89
Haveri 343 93 4.91 70 5.12 1697.31 2041.08 0.83
gadag 332 92 5.38 60 5.00 1813.28 2211.99 0.82
Hubli 301 92 5.57 52 4.94 2154.71 2478.58 0.87
Sirsi 349 92 4.90 69 4.96 1890.98 2232.64 0.85
Gulbarga 331 91 5.30 58 5.10 1853.60 2140.10 0.87
Yadgiri 337 91 4.20 75 5.27 1803.90 1955.90 0.92
Raichur 350 92 4.56 72 5.29 1744.80 1998.90 0.87
Bidar 349 94 5.07 66 5.28 1780.50 1974.60 0.90
Koppal 349 96 4.08 84 5.33 1714.00 1877.60 0.91
Bellary 325 90 5.58 55 5.24 1849.70 2158.70 0.86
Bijapur 351 88 5.77 59 5.26 1814.00 2067.30 0.88
Hospet 331 91 4.90 61 5.21 1765.20 2021.10 0.87
16

17. 2010-11
Savings from
fuel (million
Rs.)
Savings from
staff (million Rs.)
Capital
Expenditure
(million Rs.) Total (million Rs.)
Bangalore central 181.28 106.75 0.00 288.02
Bangalore rural 25.31 84.10 461.82 571.23
Tumkur 16.29 29.89 354.64 400.83
Kolar 10.06 91.50 356.66 458.21
chikkaballapur 19.39 68.74 353.85 441.98
Mysore urban 60.81 185.56 722.49 968.86
Mysore rural 80.59 55.06 432.98 568.63
Mandya 8.78 42.57 354.45 405.80
Chamaraj Nagar 14.32 38.95 283.71 336.98
Hassan 24.74 21.48 411.14 457.36
Chikamaglore 31.99 12.86 292.43 337.28
Mangalore 205.54 0.00 497.21 702.75
Davanagere 17.42 93.93 329.37 440.73
Bagalkote 33.99 122.28 517.84 674.11
Belgaum 38.00 150.81 598.86 787.67
Chikkodi 15.65 95.65 381.39 492.68
Haveri 17.97 72.93 329.58 420.49
Gadag 29.93 88.74 411.92 530.59
Hubli 49.13 289.95 942.29 1281.37
Sirsi 39.54 110.79 420.68 571.01
Gulabarga 23.49 141.37 504.20 669.07
Yadagiri 3.73 26.08 271.93 301.75
Raichur 4.10 60.97 404.56 469.63
Bidar 5.06 90.91 360.97 456.94
Koppal 0.00 6.75 253.32 260.07
Bellary 5.08 93.66 302.80 401.53
Bijapur 8.06 181.51 496.48 686.06 17

18. 0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
Bangalore
central Bangalore
rural
Tumkur
Kolar
Chikkaballa
pur
Mysore
urban
Mysore
rural
Mandya
Chamaraj
Nagar
Hassan
Chikamaglo
re
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
Total savings for the division from fuel, staff and vehicle from benchmarking
analysis in Million(Rs)
18

19. • KSRTC
 Bannimantapa - 593.92 million Rs
 Harapanhalli - 30.61 million Rs
• NWKRTC
 Hubli urban
transport 1 - 403.67 million Rs
 Belgaum-1 - 16.73 million Rs
• NEKRTC
 Alanda - 178.67 million Rs
 Gangavati - 8.61 million Rs
Summary
• KSRTC
 Mysore Urban - ~ 1000 million Rs
 Bangalore Central is the least
• NWKRTC
 Hubli - ~ 700 million Rs
 Chikkodi is the least
• NEKRTC
 Bellary - ~ 300 million Rs
 Koppal is the least
Depot wise Division wise
19

20.  Dharwad - 610.15 million Rs
 Gangavati - 24.10 million Rs
• Among the Three SRTUs NWKRTC has the highest total savings value of the order
1400 Million Rupees KSRTC is the best performing with least savings value.
 Hubli - ~ 1200 million Rs
 Koppal is the least
•Overall Depot wise Overall Division wise
• This method gives only financial statement analysis,
• Can not handle multiple inputs and multiple outputs
• Non-parametric techniques
20

21. Data envelopment analysis
 The Data Envelopment Analysis (DEA) developed by Charnes et al. [1978] .
 The units that obtain a greater quantity of outputs with the lowest quantity of
inputs will be the most efficient and therefore will achieve the highest scores.
Where vi is the weight assigned to its corresponding input xi during the aggregation,
uj is the weight assigned to its corresponding output yj during the aggregation, and ui
,vj  0.
Efficiency = output/ Input





 I
i
i
i
J
j
j
j
x
v
y
u
1
1
Input
Virtual
Output
Virtual
Efficiency
21

22. After DEA, many models were developed by the researchers. We discuss the
two main models frequently used in performance evaluation study.
1. Constant returns to scale (CRS)model.
2. Variable returns to scale (VRS)model.
22

23. Linear programming problem
Fractional programming problem
=
Subjected to
Subjected to
≤ 1 ; j=1, 2,…….n,
ur,vi≥0; r=1,2,………s; i=1,2,………m
where
h0: Efficiency in DEA terms, xj: Input indicators of the jth DMU, yj: Output
indicators of the jth DMU, u1, u2, ........, um= output weights, v1, v2, ........, vs =
input weights.
Constant return to scale model (CRS)
23

24. Dual problem with the slack and surplus terms attached to it
24

25. Variable return to scale (VRS) model.
The BCC model developed by Banker, Charles and Cooper (Banker et.al., 1984)
for variable return to scale as a linear programming problem can be written as,
25

26. 26
Input &
Output
Variables
data file
DEA Options
Data conversion
DEA result Report
Linear
Programming
Files of
Efficiency
Basic Solution
Generating
DEA Loop
DATA Stata /DEA RESULT
• Diagram of Data flow in DEA program

27. 27
Illustration
DMU Input 1 Input 2 Output 1 Output 2 Output 3
1 5 14 9 4 16
2 8 15 5 7 10
3 7 12 4 9 13
0
,
,
,
,
0
12
7
13
9
4
0
15
8
10
7
5
0
14
5
16
4
9
1
14
5
.
.
16
4
9
2
1
3
2
1
2
1
3
2
1
2
1
3
2
1
2
1
3
2
1
2
1
3
2
1




















u
u
v
v
v
u
u
v
v
v
u
u
v
v
v
u
u
v
v
v
u
u
t
s
v
v
v
Maximize

28. 28
Illustration Results
• DMU 1 and DMU 3 are efficient (efficiency of
1.00 with no slacks)
• DMU 2 is inefficient (efficiency < 1.00)
• DMU 2 can utilize DMU 1 and DMU 3 as
benchmarks for improvement
• See DEA

29. 29
Selection of Inputs, Outputs, and units in
DEA
• Inputs: resources (examples: workers, machines,
operating expenses, budget, etc.)
• Outputs: actual number of products produced to a
host of performance and activity measures
(examples: quality levels, throughput rates, lead-
time, etc.)
• If there are m inputs and s outputs then potentially
ms DMUs can be efficient. Thus, to achieve
discrimination we need substantially more units than
ms

30. Principal component analysis.
The purpose of PCA is to explain/summarize the underlying variance-covariance
structure of a large set of variables through a few linear combinations of these
variables called Principal components. Principal axis 1 has the highest variance,
axis 2 has the next highest variance,...., and axis p has the lowest variance. The
mathematical form of PCA is given by,
such that: yk's are uncorrelated (orthogonal);
y1 explains as much as possible of original
variance in data set; y2 explains as much as
possible of remaining variance etc.
{a11,a12,...,a1k} is 1st Eigenvector of
correlation/covariance matrix, and
coefficients of first principal component;
{a21,a22,...,a2k} is 2nd Eigenvector of
correlation/covariance matrix, and
coefficients of 2nd principal component;
…........
{ak1,ak2,...,akk} is kth Eigenvector of
correlation/covariance matrix, and
coefficients of pth principal component
From k original variables: x1, x2,...,xk
Produce p new variables: y1, y2, ...,yp
y1 = a11x1 + a12x2 + ... + a1kxk
y2 = a21x1 + a22x2 + ... + a2kxk
...
Yp = ak1x1 + ak2x2 + ... + akkxk
30

31. 1st Principal
Component, y1
2d Principal
Component, y2
The principal axes are uncorrelated which is schematically
shown in figure.
31
PCA
axis
2
PCA axis 1

32. Description of PCA-DEA
Portela, et.al.,(2004) has used an approach based on the directional distance
model ( Portuguese bank) to deal with the negative data which is given by,
to find the efficiency of the decision making units (DMUs) by PCA-DEA and
the model is
32
(5)
(5)
Where, =is the inefficiency measure of DMU, therefore 1- is the efficiency measure.

33. Bootstrapped-DEA
When the number of DMUs are less compared to the input and output
parameters used (Joseph Sarkis), the efficiency obtained from the
DEA, will have an upward bias. In order to obtain the accurate
efficiency values we use the bootstrapping technique for the DEA
which will remove the upward bias.
The following algorithm is used to calculate bootstrapped DEA.
• Using efficiency scores of DEA a best fit curve is obtained to generate pseudo
efficiencies.
• Pseudo outputs are generated using above obtained pseudo efficiencies by keeping
the inputs constant.
• DEA is applied to these new data set(pseudo data) along with the original data set to
calculate the bootstrapped efficiency.
33

34. Input and output indicators for DEA
 4 input parameters and 8 output parameters were selected to
determine efficiency of depots or divisions (DMUs). The input
parameter and output parameters include both the physical and
financial parameters of performance indicators.
Output indicators
Revenue
Effective Kilometers
Breakdown rate
Accident rate
Fuel efficiency
Profitability
Fleet utilisation
Staff per schedule
Input indicators
Number of vehicles
Number of employees
Fuel consumed
Number of schedules
Used GAMS and DEAP to get
confidence on the results.
34

35. Efficiency score of the 13 divisions of KSRTC (CRS model)
DIVISIONS 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore Central 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Bangalore Rural 1.000 1.000 1.000 1.000 1.000 0.970 0.940
Tumkur 1.000 1.000 1.000 1.000 1.000 1.000 0.990
Kolar 0.800 0.900 1.000 0.970 0.990 0.930 0.910
Chikballapur --- --- 1.000 0.970 1.000 0.890 0.890
Mysore Urban 0.960 0.980 1.000 1.000 0.970 1.000 1.000
Mysore Rural 0.860 0.900 1.000 0.990 0.920 0.940 0.910
Mandya --- --- 1.000 1.000 1.000 1.000 1.000
Chamraj Nagar --- --- --- --- --- 1.000 1.000
Hassan 0.940 0.950 1.000 1.000 1.000 0.950 0.970
Chikamaglore 1.000 1.000 0.990 1.000 1.000 0.950 0.990
Mangalore 0.810 0.830 0.940 1.000 1.000 1.000 1.000
Davanagere 1.000 1.000 0.950 0.920 1.000 1.000 1.000
Note: --- Divisions not established. 35

36. Efficiency of KSRTC using DEA(CRS model)
36
0.700
0.750
0.800
0.850
0.900
0.950
1.000
Bangalore Central
Bangalore Rural
Tumkur
Kolar
Chikballapur
Mysore Urban
Mysore Rural
Mandya
Chamraj Nagar
Hassan
Chikamaglore
Mangalore
Davanagere
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11

37. Efficiency score of the 13 divisions of KSRTC (VRS model)
Divisions 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore
Central 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bangalore Rural 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Tumkur 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Kolar 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Mysore Rural 1.00 1.00 1.00 1.00 0.99 1.00 0.999
Mysore Urban 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Chikballapur --- --- 1.00 1.00 1.00 1.00 1.00
Mandya --- --- 1.00 1.00 1.00 1.00 1.00
Chamraj Nagar --- --- --- --- --- 1.00 1.00
Hassan 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Chikamaglore 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Mangalore 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Davanagere 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Note: ---Divisions not established. 37

38. Efficiency of KSRTC using DEA (VRS model)
0.98
0.98
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
Bangalore Central
Bangalore Rural
Tumkur
Kolar
Chikballapur
Mysore Urban
Mysore Rural
Mandya
Chamraj Nagar
Hassan
Chikamaglore
Mangalore
Davanagere
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
38

39. -8.00
-6.00
-4.00
-2.00
0.00
2.00
4.00
6.00
0 2 4 6 8 10 12 14
Slack
value
in
percentage
DMUs
Slack and surplus value by DEA (VRS) for KSRTC (2010 -11)
Revenue
fleet utilisation
staff per schedule
breakdown rate
accident rate
KMPL
Eff.kms
EPKM/CPKM
Total number of vehicles
Total employees
Fuel consumed(in lakh
litres)
Number of schedules
39

40. Note : --- Divisions not established
DIVISIONS 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore central 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bangalore rural 1.00 1.00 1.00 1.00 1.00 0.98 1.00
Tumkur 1.00 1.00 1.00 1.00 0.98 1.00 1.00
Kolar 1.00 1.00 1.00 0.99 1.00 1.00 1.00
Chikkaballapur --- --- 1.00 0.99 0.99 1.00 1.00
Mysore urban 0.99 1.00 1.00 1.00 1.00 0.98 1.00
Mysore rural 1.00 1.00 1.00 0.99 1.00 1.00 1.00
Mandya --- --- 1.00 1.00 0.99 1.00 1.00
ChamarajNagar --- --- --- --- --- 1.00 1.00
Hassan 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Chikamaglore 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Mangalore 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Davanagere 1.00 1.00 1.00 0.99 1.00 1.00 1.00
Bagalkote 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Belgaum 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bijapur 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Chikkodi 0.98 0.98 0.99 0.99 1.00 0.98 0.99
Haveri 0.99 1.00 0.97 0.99 0.99 0.98 1.00
Gadag 0.99 1.00 1.00 1.00 1.00 1.00 1.00
Hubli 1.00 0.99 0.99 1.00 0.99 0.97 0.98
Sirsi 1.00 1.00 1.00 0.96 0.97 0.99 0.98
Gulbarga 1.00 1.00 1.00 1.00 0.99 1.00 1.00
Yadgir 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Raichur 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bidar 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Koppal 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bellary 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Hospet --- --- --- --- --- --- 1.00
40

41. Efficiency of all divisions using DEA (VRS) model
0.95
0.96
0.96
0.97
0.97
0.98
0.98
0.99
0.99
1.00
1.00
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Mysore urban
Mysore rural
Mandya
ChamarajNagar
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
41

42. -10.00
-5.00
0.00
5.00
10.00
15.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Slack
in
Percentage
DMUs
Slack and Surplus for all divisions by DEA (VRS) for 2010 -11
Revenue
fleet utilisation
staff per schedule
breakdown rate
accident rate
KMPL
Eff.kms
EPKM/CPKM
Total number of
vehicles
Total employees
Fuel consumed(in
lakh litres)
Number of
schedules
42

43. Principal component analysis
PCA is carried on the data of 2010-11 of KSRTC divisions, to know the grouping of
indicators and to calculate the Principal components and their loadings.
1 Principal Component (PC) is identified as representatives of 04 input indicators
explaining 96.61% of the variable change.
4 PC’s are identified as representatives of 8 output indicators explaining 85.84% of
the variable change.
Input
Factor
Name
Eigen
value
Percent variation
explained
Dominant variable
1 Factor 1 3.86 96.61 Number of schedules
Output
Factor
Name
Eigen
value
Percent
variation
explained
Dominant variable
1 Factor 1 3.32 41.55 Effective Kilometer
2 Factor 2 1.54 19.22 Accident rate
3 Factor 3 1.08 13.45 Breakdown rate
4 Factor 4 0.93 11.62 staff per schedule
43

44. Efficiency values of the Divisions of KSRTC by PCA-DEA
Note: -- Divisions not established.
Divisions 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore Central 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bangalore Rural 0.79 1.00 0.98 1.00 1.00 0.61 0.96
Tumkur 0.55 1.00 1.00 0.98 0.71 0.51 1.00
Kolar 1.00 0.74 1.00 0.86 0.98 0.45 1.00
Chikkaballapur -- -- 1.00 1.00 0.94 0.59 0.94
Mysore Urban 1.00 0.85 1.00 1.00 1.00 0.68 1.00
Mysore Rural 1.00 1.00 1.00 1.00 0.98 1.00 1.00
Mandya -- -- 1.00 1.00 1.00 0.41 1.00
Chamaraj Nagar -- -- -- -- -- 1.00 1.00
Hassan 0.69 0.93 1.00 1.00 1.00 0.35 1.00
Chikmagalur 0.61 0.91 1.00 1.00 1.00 0.44 0.90
Mangalore 0.68 1.00 1.00 1.00 0.94 1.00 1.00
Davangere 1.00 1.00 0.86 0.85 0.78 0.48 0.74
44

45. Efficiency of KSRTC using PCA - DEA model
45
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Bangalore Central
Bangalore Rural
Tumkur
Kolar
Chikballapur
Mysore Urban
Mysore Rural
Mandya
Chamraj Nagar
Hassan
Chikamaglore
Mangalore
Davanagere
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11

46. Note: --- divisions not established
DIVISIONS 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore central 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Bangalore rural 0.72 0.89 0.87 0.87 0.86 0.93 0.77
Tumkur 1.00 1.00 0.93 1.00 0.97 0.97 0.77
Kolar 0.60 1.00 1.00 1.00 0.94 0.94 0.84
Chikkaballapur --- --- 0.89 0.99 0.94 0.98 0.84
Mysore urban 0.49 1.00 1.00 0.91 0.96 1.00 1.00
Mysore rural 0.83 0.76 0.75 0.97 1.00 1.00 0.98
Mandya --- --- 0.75 1.00 0.84 0.96 1.00
ChamarajNagar --- --- --- --- --- 1.00 0.82
Hassan 0.89 1.00 1.00 1.00 1.00 1.00 1.00
Chikamaglore 0.90 1.00 1.00 1.00 1.00 1.00 1.00
Mangalore 1.00 1.00 0.93 1.00 1.00 1.00 1.00
Davanagere 0.80 1.00 0.97 1.00 0.94 1.00 0.79
Bagalkote 0.93 0.83 0.84 0.93 0.77 1.00 0.96
Belgaum 0.72 1.00 0.82 1.00 1.00 1.00 0.94
Bijapur 1.00 0.88 1.00 1.00 1.00 0.93 0.67
Chikkodi 0.83 0.75 0.89 0.92 0.91 1.00 0.59
Haveri 1.00 0.64 0.59 1.00 1.00 1.00 0.51
Gadag 1.00 0.43 0.94 0.99 1.00 1.00 1.00
Hubli 0.60 0.89 1.00 1.00 1.00 0.97 0.60
Sirsi 1.00 0.61 0.91 1.00 1.00 0.96 0.46
Gulbarga 0.70 0.94 0.98 0.96 0.94 0.99 0.68
Yadgir 1.00 0.61 0.87 1.00 0.80 1.00 0.60
Raichur 0.84 1.00 0.84 0.99 0.91 0.93 0.72
Bidar 1.00 0.59 1.00 1.00 1.00 1.00 1.00
Koppal 1.00 0.90 0.84 1.00 0.99 1.00 0.49
Bellary 0.67 0.61 0.89 1.00 1.00 1.00 0.51
Hospet --- --- --- --- --- --- 0.45
46

47. Efficiency of all divisions using PCA - DEA model
0.40
0.50
0.60
0.70
0.80
0.90
1.00
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Mysore urban
Mysore rural
Mandya
ChamarajNagar
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkote
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
47

48. Efficiency values of the Divisions of KSRTC by Bootstrap-DEA
Note: --- Divisions not established.
Divisions 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore Central 0.999 0.997 1.000 1.000 0.999 0.999 1.000
Bangalore Rural 0.999 0.999 0.989 0.990 0.971 0.972 0.995
Tumkur 1.000 1.000 1.000 1.000 0.981 0.995 1.000
Kolar 0.999 1.000 1.000 0.996 1.000 1.000 0.999
Chikkaballapur --- --- 1.000 0.983 0.934 0.993 0.985
Mysore Urban 0.986 0.996 0.997 0.987 0.965 0.962 0.996
Mysore Rural 0.980 0.982 0.987 0.982 0.943 0.987 0.985
Mandya --- --- 1.000 1.000 0.969 1.000 1.000
Chamaraj Nagar --- --- --- --- --- 1.000 1.000
Hassan 0.999 1.000 1.000 1.000 0.999 1.000 1.000
Chikmagalur 0.999 1.000 0.999 1.000 1.000 0.995 1.000
Mangalore 1.000 0.995 0.995 1.000 1.000 0.990 0.999
Davangere 0.996 0.999 0.990 0.981 0.980 0.978 0.989
48

49. 0.930
0.940
0.950
0.960
0.970
0.980
0.990
1.000
Bangalore Central
Bangalore Rural
Tumkur
Kolar
Chikballapur
Mysore Urban
Mysore Rural
Mandya
Chamraj Nagar
Hassan
Chikamaglore
Mangalore
Davanagere
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
Efficiency values of the Divisions of KSRTC using Bootstrap-DEA
49

50. DIVISIONS 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Bangalore central 0.743 0.79 0.855 0.896 0.904 0.955 0.991
Bangalore rural 0.815 0.808 0.779 0.768 0.755 0.74 0.786
Tumkur 0.852 0.859 0.812 0.797 0.76 0.772 0.783
Kolar 0.759 0.748 0.754 0.763 0.768 0.761 0.768
Chikkaballapur --- --- 0.836 0.825 0.778 0.777 0.753
Mysore urban 0.768 0.759 0.734 0.718 0.732 0.758 0.834
Mysore rural 0.78 0.76 0.784 0.787 0.766 0.757 0.771
Mandya --- --- 0.884 0.937 0.788 0.886 0.836
ChamarajNagar --- --- --- --- --- 0.891 0.876
Hassan 0.815 0.811 0.794 0.788 0.761 0.996 0.751
Chikamaglore 0.83 0.829 0.796 0.813 0.753 0.749 0.75
Mangalore 0.759 0.713 0.694 0.651 0.713 0.741 0.815
Davanagere 0.823 0.797 0.775 0.748 0.732 0.709 0.745
Bagalkot 0.797 0.78 0.766 0.741 0.735 0.729 0.756
Belgaum 0.782 0.776 0.746 0.736 0.725 0.733 0.737
Bijapur 0.801 0.78 0.775 0.751 0.745 0.716 0.766
Chikkodi 0.79 0.787 0.773 0.751 0.74 0.748 0.775
Haveri 0.829 0.83 0.794 0.769 0.769 0.765 0.8
Gadag 0.806 0.798 0.791 0.795 0.787 0.76 0.769
Hubli 0.781 0.77 0.751 0.733 0.725 0.729 0.75
Sirsi 0.78 0.748 0.75 0.715 0.722 0.728 0.759
Gulbarga 0.888 0.882 0.839 0.79 0.76 0.747 0.891
Yadgir 0.882 0.878 0.862 0.901 0.887 0.857 0.777
Raichur 0.898 0.893 0.894 0.883 0.787 0.768 0.794
Bidar 0.936 0.931 0.903 0.886 0.814 0.795 0.88
Koppal 0.936 0.935 0.941 0.922 0.857 0.833 0.863
Bellary 0.917 0.859 0.821 0.797 0.776 0.76 0.765
Hospet --- --- --- --- --- --- 0.825
50
Note : --- divisions not established

51. Efficiency values of the all Divisions using Bootstrap-DEA
51
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Bangalore central
Bangalore rural
Tumkur
Kolar
Chikkaballapur
Mysore urban
Mysore rural
Mandya
ChamarajNagar
Hassan
Chikamaglore
Mangalore
Davanagere
Bagalkot
Belgaum
Bijapur
Chikkodi
Haveri
Gadag
Hubli
Sirsi
Gulbarga
Yadgir
Raichur
Bidar
Koppal
Bellary
Hospet
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11

52. 52
The Analytic Hierarchy Process (AHP)
 Founded by Saaty in 1980.
 It is a popular and widely used method for
multi-criteria decision making.
 Allows the use of qualitative, as well as
quantitative criteria in evaluation.

53. 53

54. Analytic Hierarchy Process (AHP)
• Information is decomposed into a hierarchy of alternatives and criterion
• Information is then synthesized to determine relative ranking of alternatives
• Both qualitative and quantitative information can be compared using
informed judgments to derive weights and priorities
54
Mathematical Model
Sj = wi rij
j
where:
Sj = score for decision alternative j
Wi = weight of the criterion i
rij = rating for criterion i and decision alternative j

55. 55
 AHP algorithm is basically composed of two
steps:
1. Determine the relative weights of the decision criteria
2. Determine the relative rankings (priorities) of
alternatives
 Ranking of Criteria and Alternatives
 Pairwise comparisons are made with the grades
ranging from 1-9.
 A basic, but very reasonable assumption for
comparing alternatives:
If attribute A is absolutely more important than attribute B
and is rated at 9, then B must be absolutely less
important than A and is graded as 1/9.

56. 56
Ranking Scale for Criteria and Alternatives

57. 57
Ranking of criteria
Style Reliability Fuel Economy
Style
Reliability
Fuel Economy
1 1/2 3
2 1 4
1/3 1/4 1

58. 58
Ranking of priorities
 Consider [Ax = maxx] where
 A is the comparison matrix of size n×n, for n criteria, also called the priority matrix.
 x is the Eigenvector of size n×1, also called the priority vector.
 max is the Eigenvalue .
 To find the ranking of priorities, namely the Eigen Vector X:
1) Normalize the column entries by dividing each entry by the sum of the column.
2) Take the overall row averages.
0.30 0.29 0.38
0.60 0.57 0.50
0.10 0.14 0.13
Column sums 3.33 1.75 8.00 1.00 1.00 1.00
A=
1 0.5 3
2 1 4
0.33 0.25 1.0
Normalized
Column Sums
Row
averages 0.30
0.60
0.10
Priority vector
X=

59. 59
Criteria weights
 Style .30
 Reliability .60
 Fuel Economy .10
Style
0.30
Reliability
0.60
Fuel Economy
0.10
Selecting a New bus
1.00

60. 60
Checking for Consistency
 The next stage is to calculate a Consistency Ratio
(CR) to measure how consistent the judgments have
been relative to large samples of purely random
judgments.
 AHP evaluations are based on the aasumption that
the decision maker is rational, i.e., if A is preferred to
B and B is preferred to C, then A is preferred to C.
 If the CR is greater than 0.1 the judgments are
untrustworthy because they are too close for comfort
to randomness and the exercise is valueless or must
be repeated.

61. 61
Calculation of Consistency Ratio
 The next stage is to calculate max so as to lead to
the Consistency Index and the Consistency Ratio.
 Consider [Ax = max x] where x is the Eigenvector.
0.30
0.60
0.10
1 0.5 3
2 1 4
0.333 0.25 1.0
0.90
1.60
0.35
= = max
λmax=average{0.90/0.30, 1.60/0.6, 0.35/0.10}=3.06
0.30
0.60
0.10
A x Ax x
Consistency index , CI is found by
CI=(λmax-n)/(n-1)=(3.06-3)/(3-1)= 0.03

62. 62
Consistency Ratio
 The final step is to calculate the Consistency Ratio, CR by using
the table below, derived from Saaty’s book. The upper row is the
order of the random matrix, and the lower row is the
corresponding index of consistency for random judgments.
Each of the numbers in this table is the average of CI’s derived from a
sample of randomly selected reciprocal matrices of AHP method.
An inconsistency of 10% or less implies that the adjustment is small as
compared to the actual values of the eigenvector entries.
A CR as high as, say, 90% would mean that the pairwise judgments are just
about random and are completely untrustworthy! In this case, comparisons
should be repeated.
In the above example: CR=CI/0.58=0.03/0.58=0.05
0.05<0.1, so the evaluations are consistent!

63. A.H.P
63

64. Depots of Bangalore
central division
(2010-11)
DEA-CRS DEA-VRS PCA-DEA
Bootstrapped
-DEA
AHP
in %
Depot-1 1.000 1.000 0.849 0.991 14.3
Depot-2 1.000 1.000 0.942 0.992 13.4
Depot-3 1.000 1.000 1.000 0.986 14.4
Depot-4 1.000 1.000 1.000 0.990 19.3
Depot-5 1.000 1.000 0.673 0.991 12.0
Depot-6 1.000 1.000 1.000 0.990 10.8
Nelamangala
1.000 1.000 1.000 1.000 15.7

65. 1. The efficiency values of the DMUs (depots) using DEA-CRS model are having the
range of 0.482 to1.00 and DEA-VRS values are having the range of 0.879 to 1.00
2. From division wise performance analysis it is concluded that DEA-CRS values are
having the range of 0.75 to1.00 and DEA-VRS values are having the range of
0.965 to 1.00.
3. PCA-DEA model has shown the discrimination between the efficient and
inefficient DMU’s which was not observed in DEA.
4. Bootstrapped-DEA has shown good results among all DEA models.
5. According to AHP Depot-4 of Bangalore central division is top performing the
reason is it is a Volvo (Air conditioned) bus depot, also Bangalore central division
is top performing division as per AHP results AHP has given the similar results as
that of DEA.
6. AHP results built the confidence on DEA results.
Summary
65

66. User and operator perception study

67. FOR SRTUs OF KARNATAKA
Commuters Crew Official staff
2400 2400 400
Number of samples for questionnaire survey
67
• The Sample size for random sampling is calculated as belows:
SS=Z2 P (1-P)/e2
New Sample size = SS/(1+((SS-1)/population)
Where,
Z = confidence interval 95% = 1.96
P = Percentage of picking a choice = 0.5
E= error interval 2% of true value =0.02
• The questionnaire were prepared in consultation with experts from both Transport
Organization and Institute, in the view of socio-economic characteristic, travel
characteristics, perception of existing public transport system and Intelligent
Transport System.

68. 68
Perception Survey: Questionnaire-User (SRTUs)

69. 69
Perception Survey: Questionnaire-Crew & Officers

70. User and Operator perception study
User perception data
• Travel Cost – transit fares, plus related costs like parking
• Door-to-door travel time
• Frequency of service – how often the service runs
• Hours of service – how early or late service runs, and/or weekend hours
• Convenience of service – goes where you need to go/parking availability
• On time performance of service – does the service run on time?
• Behaviour of crew
Operator perception data
• Passenger behaviour
• Leave and incentives
• Road condition
• Break down rate
• Retarding room facility
From this data the analysis has been done.
70

71. 71
57.7
42.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
percentage
Gender
Male Female
20.2
52.3
9.2 8.3
5.2 4.8
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Percentage
Age Group
12-21 22-31 32-41 42-51 52-61 >61
17.5
24.3
42.0
10.8
5.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Percentage
Education Qualification
10th or below
10+2
Graduate
Post Graduate
and above
others
5.7
29.9
13.6
0.3
9.5
39.6
1.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Percentage
Occupation
Government
Private
Limited/MNC
Self Employed
Retired
Housewife
Student
23.3
34.1
20.7
10.2
1.6
10.1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Percentage
Monthly Income
<4000
4000-10000
10000-20000
20000-40000
>40000
No Response
4.8
35.9
12.3
2.4
44.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
Percentage
Vehicle Ownership
Four wheeler
Two wheeler
Cycle
Other
None
Perception Study: Commuter – Social &Economic

72. 72
Home
37%
Work
15%
Business
14%
Education
28%
Recreation
3%
Others
3%
Purpose of Travel
Walk
14%
Cycle
1%
Two Wheeler
27%
Auto
6%
Car
6%
Bus
46%
Mode of Travel
9.9
28.9
25.3
15.8
4.3
2.6
6.3 6.9
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Percentage
Commuter: Average waiting time at the bus stop (in minutes)
0-5
6-10
11-15
16-20
21-25
26-30
>30
No Response
Perception Study: Commuter- Travel Characteristic

73. 73
Perception Study: Commuter – PT System
3.3
51.3
35.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Percentage
Preference of PT
Never Sometimes Always 56.6
15.5
6.9
16.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Percentage
Type of ticket using
Purchase of ticket in the
bus
Daily pass
Monthly pass
Any Concessional pass
32.3
36.6
24.9
6.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Percentage
factors influence choice of transport mode
Reduced Expenditure on
travel
Convenience & Comfort
Safety
Good Connectivity
8.2
38.4
29.6
23.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Percentage
safety at bus stop
Never
Sometimes
Always
only when other
Commuters around

74. 74
Perception Study: Commuters for SRTUs
28.2
13.1
32.6
39.1
8.3 6.2
16.8
32.2
45.2
69.8
53.9
32.8
41.5
48.8
63.1 61.8
26.6
17.1
13.5
28.1
50.2
45.0
20.1
6.0
0.0
20.0
40.0
60.0
80.0
100.0
Very Good
Good
Poor

75. 75
Perception Study: Crew of SRTUs
11.2
6.5
3.2
9.5
18.2
25.2
8.9
34.2
26.2
64.1
58.1
26.9
64.3
54.2
45.9
53.2
41.8
53.2
24.7
35.4
69.9
26.2 27.6 28.9
37.9
24.0
20.6
0.0
20.0
40.0
60.0
80.0
100.0
Very Good
Good
Poor

76. 76
Perception Study: Official Staff of SRTUs
8.1
14.9
0.4
12.8
7.9
16.6 18.4
62.2
69.0 68.0
82.7
67.2
74.7
34.2
49.2
33.6
22.9
17.1 16.9
20.0
17.4
49.2
32.4
4.2
0.0
20.0
40.0
60.0
80.0
100.0
Very Good
Good
Poor

77. Summary
 The major findings from the SRTUs qualitative study are: from the commuter survey
it is very clear that they prefer the SRTUs mainly because they are cheap and safe,
commuter also feel that attention towards reliability is required and around 50% of
commuters are not happy with behaviour of crew.
 According to qualitative and quantitative studies KSRTC is performing better
compared to NWKRTC and NEKRTC.
 From crew survey it is observed that, breakdown rate of buses is high, vehicle
maintenance is poor, rest room facility is also poor, the official staff behaviour has to
be improved and retarding facility needs attention
 official staffs are of opinion that, public complaints are more, support from higher
officials has to be improved, maintenance of vehicle require more skilled labour and
fuel efficiency has to be improved by providing training to drivers
77

78. Impact study of Mysore ITS

79. No. of Depots 4
Schedules 438
Number of Bus stops 484
Vehicles 459
Effective Kms/day 93,301
No. of Passenger/day 2,64,676
Number of staff 2327
Operational Characteristics of KSRTC in Mysore City
79
For Mysore city bus transportation, implementation of ITS project had been stared in
September 2011 and completed in November 2012

80. Display board
Prior to ITS in
CBS Mysore
Display board
after ITS in
CBS Mysore 80

81. Ratio analysis and Benchmarking
•Mysore ITS
 Kuvempu Nagar depot - 51.70 million Rs (2009-10) - before
 Kuvempu Nagar depot - 21.83 million Rs (2013-14) -after
81
Depots DEA-CRS DEA-VRS PCA-DEA
Bootstrapped
-DEA
AHP
in %
Banni mantapa 1.000 1.000 1.000 0.990 40.38
Kuvempunagar 1.000 1.000 1.000 0.993 43.01
Sathgalli 1.000 1.000 1.000 0.994 16.61
Non-parametric and Multivariate analysis 2013-14

82. FOR MYSORE ITS
Commuters Crew Official staff
2400 885 185
Number of samples for questionnaire survey
82
• User and Operator Study

83. 83
Perception Survey: Questionnaire-User (ITS)

84. 84
Perception Survey: Questionnaire-Crew (ITS)

85. 85
Perception Survey: Questionnaire- Staff (ITS)

86. 86
9.5
5.9
9.5 9.5 7.9 11.2
14.8
10.2 12.8
70.7 72.7
78.6
74.3 74.7
70.4
74.0
80.9
74.7
19.7
16.1
11.5 14.8 15.5 16.4 14.5 13.8 15.8
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Clean and
Comfort
Reliable &
punctual
Safety Frequent
service
Park & ride
facility at bus
station
Less fare Display of
information at
stops
Stoppage of
buses
Discount in
passes
Commuter Opinion(%): Bus Service
Very Good
Good
Poor
5.6
41.3 41.8
9.1
2.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Percentage
Overall opinion of Intelligent Transport system
VeryGood
Good
Satisfactory
Poor
VeryPoor
Perception Study: Commuter –ITS

87. 87
17.4
47.9
21.4
13.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Age Group
22-31
32-41
42-51
52-61
80.0
16.8
2.1 1.0
0.0
20.0
40.0
60.0
80.0
100.0
Educational Qualification
10+2 or below
Graduate
Post Graduate
and above
others
51.2
48.8
47.0
48.0
49.0
50.0
51.0
52.0
%age
proper training about ITS
Yes
No
39.2
60.8
0.0
20.0
40.0
60.0
80.0
improvement over relation with your
officials due to ITS
Yes
No
44.0
56.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
ITS attracted more Passengers
Yes
No
45.9
54.1
40.0
42.0
44.0
46.0
48.0
50.0
52.0
54.0
56.0
ITS equipments properly working
Yes
No
42.9
57.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
ITS information accuracy
Yes
No
6.9
27.3
16.8
34.2
14.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Overall opinion on ITS
Very Good
Good
Satisfactory
Poor
Very Poor
Perception Study: Crew
31.0
69.0
0.0
20.0
40.0
60.0
80.0
revenue improvement due to ITS
Yes
No

88. 88
14.1
20.0
17.3 18.4
27.6
2.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Work Experience
0-2
2-5
5-10
10-20
20-30
>30
76.2
2.70.00.0
11.49.7
0.0
20.0
40.0
60.0
80.0
100.0
publicizing about the ITS
Through KSRTC
Students
Citizen forums
NGOs
Print media
Visual media (TV)
91.2
70.2
91.2
8.8
29.8
8.8
0.0
20.0
40.0
60.0
80.0
100.0
Optimising
routes
Crew
management
Passenger
Information
System
ITS useful for operational purpose
Yes
No
84.2 83.3 80.7
15.8 16.7 19.3
0.0
20.0
40.0
60.0
80.0
100.0
Expected
Time of
Arrival
Expected
Time of
Departure
Bus stop
name
ITS information accuracy
Yes
No
73.0
27.0
0.0
20.0
40.0
60.0
80.0
Crew behavior improvement due to ITS
Yes
No
90.3
9.7
0.0
20.0
40.0
60.0
80.0
100.0
route deviation avoided
Yes
No
34.6
37.3
11.413.0
3.8
0.0
10.0
20.0
30.0
40.0
%age
Percentage of modal shift from private
transport to public transport after ITS
1-5%
5-10 %
11-20%
21-50%
more than 51%
22.2
47.0
24.9
5.4
0.5
0.0
10.0
20.0
30.0
40.0
50.0
%age
Overall opinion of ITS
Very Good
Good
Satisfactory
Poor
Very Poor
53.5
28.6
13.0
3.8 1.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
%age
Percentage of revenue increased after ITS
1-10%
10-25%
26-50%
51-75%
76-100%
Perception Study: Official Staff

89. 89
Perception Study: Commuters for Mysore ITS
9.5
5.9
9.5 9.5 7.9
11.2
14.8
10.2
12.8
70.7 72.7
78.6
74.3 74.7
70.4
74.0
80.9
74.7
19.7
16.1
11.5
14.8 15.5 16.4 14.5 13.8 15.8
0.0
20.0
40.0
60.0
80.0
100.0
Very Good
Good
Poor

90. 90
44.3
42.2
36.8
34.6
23.8
33.5
31.9
39.5
33.0
31.4
41.1
33.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
Percentage
% of Staff
Perception Study: Official Staff for Mysore ITS

91. 1. DEA: The results are similar and From AHP results we observe that the efficiency
of Banni mantap depot is improving after the implementation of ITS project.
2. AHP results built the confidence on DEA results.
3. In Mysore city Commuter awareness of ITS is just 41%, and 37% of commuters
utilize this system. This data indicates there is a scope for creating awareness.
4. Overall Opinion of ITS is Satisfactory for 42% commuters, Good for 41% of
commuters and Poor for 9% commuters.
5. According to 69% of the crew ITS has no effect on their operations and traffic
revenue.
6. According to 54% of the crew ITS equipments are not operating properly.
7. According to 57% of the crew the information from ITS equipments are not
proper.
8. 91% of Staff Opined that, ITS is useful for Optimizing routes, 70% for Crew
management and 91% for Passenger Information System.
9. According to staff accuracy of ITS information is 84% for ETA, 83% for ETD and
80% for Bus stop name.
Note :ETA -Expected Time of Arrival, ETD -Expected Time of Departure
Summary
91

92. 92
10. According to staff, Crew behavior improvement due to ITS is about 73%, KMPL
improvement due to ITS is 70% , Route deviation avoided due to ITS is 90%, ITS
standard reports and charts are helpful for better decision making is 91% and
Possibility to track accurate online real time information (Vehicle tracking) through ITS
is 99%.
11. From Staff Survey it is observed that ITS is attracting 85% Passengers, 71% of staff
say that improvement on revenue collection is achieved and 37% of staff say that after
ITS implementation 5-10% of increase in ridership.
12. Overall opinion of Intelligent Transport system by Staff is 47% Good, 25%
satisfactory, 22% Very Good and 5% Poor.

93. Performance evaluation of BMTC

94. Ratio analysis and Benchmarking for BMTC divisions
Divisions of
BMTC
Total
savings for
2009-10
in Million
Rs
Total
savings for
2010-11
in Million
Rs
Total
savings for
2011-12
in Million
Rs
Total
savings for
2012-13
in Million
Rs
Total
savings for
2013-14
in Million
Rs
East
115.29 59.07 134.41 56.068 0.00
West
346.32 367.27 431.76 393.78 501.78
North
529.70 455.49 692.17 858.78 1034.01
South
432.33 374.15 606.99 606.22 938.65
Volvo
519.22 711.90 765.35 884.96 1204.97
94

95. 95
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
East
West
North
South
Volvo
2009-10
2010-11
2011-12
2012-13
2013-14

96. Divisions of
BMTC DEA-CRS DEA-VRS PCA-DEA
Bootstrapped
-DEA
AHP in %
EAST 1.000 1.000 1.000 0.992 35.16
WEST 0.997 1.000 1.000 0.991 17.97
NORTH 0.955 1.000 1.000 0.990 14.84
SOUTH 1.000 1.000 1.000 0.989 20.97
VOLVO 1.000 1.000 1.000 1.000 11.06
DEA and AHP results for the year 2013-14 for BMTC
divisions
96

97. Ratio analysis and Benchmarking for Volvo division
97
Depots of
Volvo
division
Total
savings for
2009-10
in Million
Rs
Total
savings for
2010-11
in Million
Rs
Total
savings for
2011-12
in Million
Rs
Total
savings for
2012-13
in Million
Rs
Total
savings for
2013-14
in Million
Rs
Depot-07 136.06 92.13 168.93 78.57 86.72
Depot-13 169.30 129.55 118.70 152.98 153.95
Depot-18 --- --- --- --- 13.34
Depot-25 100.20 92.03 138.01 54.96 34.10
Depot-28 44.47 47.84 88.83 2.23 101.64
Note: --- indicates that this depot was not established

98. 98
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
Depot-07
Depot-13
Depot-18
Depot-25
Depot-28
2009-10
2010-11
2011-12
2012-13
2013-14

99. DEA and AHP results for the year 2013-14 for Volvo
division
99
Depots of
Volvo Division
DEA-CRS DEA-VRS PCA-DEA
Bootstrapped
-DEA
AHP in %
Depot-07 1.000 1.000 1.000 0.991 23.89
Depot-13 0.982 1.000 1.000 0.993 23.01
Depot-18 1.000 1.000 1.000 0.992 20.79
Depot-25 1.000 1.000 0.928 0.990 19.32
Depot-28 0.951 1.000 1.000 0.991 13.00

100. DEAD KILOMETER
• The bus has to travel from depot to route starting point in order to operate route
as depot and route starting point are not at same place. The distance travelled
by bus from depot to route starting point for operation of route and again from
route ending point to depot after completion of routes where no passengers
travels is known as “dead kilometer”.
• Non-revenue kilometers or dead kilometers depends upon distance between
depot and route starting point/ending point.
• The dead kilometers not only results in revenue loss, but also results in increase
in operating cost due to extra kilometers covered by buses.
• BMTC routes are considered for dead kilometer minimization problem.
100

101. 101
252E - KBS to Laggere
4.5
1.5

102. Literature Review on Dead Kilometer minimization problem
1 To optimize dead mileage they considered, two constraints, i.e., capacity of
garages and the number of buses required at the starting points of routes. Also
considered starting and end points of routes as same
Vijay Sharma and
Satya Prakash(1986)
2 They considered construction of new depots to reduce dead kilometers
comparing operation cost due to dead kilometers
Kumar et al.
(1989)
3 They considered types of buses based on road condition for allocating buses
to depots. Also starting and endpoints of routes as same. Dead kilometers
minimization problem solved using mixed integer linear programming
method.
C. B. Djiba et al.
(2012)
102
C.B Djiba literature Modifications details
Buses can be parked and maintained at any
depot
Bus is parked and maintained in same
depot.
Routes starting and end points are same Route starting point and end points are
different.
Routes operated in one shift Routes are operated in different shifts(day
out shift, general shift, night out shift and
night service).

103. 103
The following assumptions were made while formulating dead kilometers
problem:
 Each depot has a capacity.
 Buses are fixed to the depots.
 Each schedule is allotted to single depot.
 Route starting and ending points are different.
 Many schedules can be operated in a single route.
Assumptions
Dead Kilometer minimization problem

104. 104
 d : depots ( d = 1,……,m)
 r : routes ( r = 1,……., n)
 t : periods (t = 1,…….,T)
 adr : dead kilometers associated with pull-out trip of the route r from depot d
 fdr : dead kilometers associated with pull-in trip of the route r from depot d
 cd : capacity of depot d
 M : total number of buses for d depots
 Ldt: number of buses leaving from depot d at period t
 Gdt: number of buses going to depot d at period t
 xdr : binary variable which becomes 1 if the pull-out trip of the route r associated
with depot d, 0 otherwise
 edt: number of buses in depots d at the beginning of period t
Nomenclature

105. 105

 


n
1
r
)
1
( dr
dr
dr
m
d
x
f
a
1
1



m
d
dr
x n
r ,.....,
1




 

)
1
(
)
1
(
t
d
L
r
dr
t
d
dt x
e
e 


)
1
(t
d
G
r
dr
x
m
d ,......,
1

 T
t ,.......,
2


d
dt c
e 
m
d ,......,
1

 T
t ,.......,
1


minimize
s.t
(1)
(2)
(3)

106. 106



m
d
dt M
e
1
}
1
,
0
{

dr
x
m
d ,......,
1

 n
r ,.....,
1


0

dt
e
m
d ,......,
1

 T
t ,.......,
1


(4)
(5)
(6)

107. Dead Kilometer minimization problem
Depot No
Allocation of schedules Dead kilometers
Reduction in dead
kilometers
Before After Before After
7 200 205 365.1 123 242.1
13 202 207 1115.9 927.4 188.5
18 41 100 226 309.3 -83.3
25 183 148 1048 657.7 390.3
28 168 134 818.9 364.5 454.4
Total 794 794 3573.9 2381.9 1192 107

108. 108
0
100
200
300
400
500
600
700
800
900
7 13 18 25 28 Total
Number
of
Schedules
Depot number
Before
After
-500
0
500
1000
1500
2000
2500
3000
3500
4000
7 13 18 25 28 Total
Dead
kilometer
in
Kms
Depot number
Before
After
Reduction in
dead
kilometers
Allocation of schedules
Dead kilometers

109. • According to ratio analysis and benchmarking, maximum savings that can be
saved were observed for the year 2013-14 and it was 1204 million Rs for
Volvo division when all the divisions of BMTC are analyzed, however when
only volvo depots are analyzed the expected savings dropped down from 1204
million Rs to 387 million Rs.
• Again Bootstrapped-DEA shows good results
• AHP results show that East division has good performance compare to other
divisions.
• In BMTC dead kilometers of depots varies from 0.85% to 3.66% (average
2.44%)
• Overall the reduction in dead kilometers after optimization for the depots of
Volvo division is 1192 kilometers
• As cost required to operate one kilometers is Rs. 62/- for A/C buses, the total
saving in operating cost is Rs. 73904 per day due reduction of 1192 Kms in
dead kilometers after optimization.
• Analysis of percentage of dead kilometers of depots helps in allocating new
routes and choosing proper location for new depots minimizing dead
kilometers
Summary
109

110. THANK YOU ALL