This document discusses transport demand analysis for estimating ridership on a Mass Rapid Transit System. It describes conducting primary surveys like traffic counts, household travel surveys, and public transportation passenger surveys to collect data on travel patterns. Transport demand models are developed using the data to forecast future travel demand. The study area is divided into zones and trip production, attraction, and distribution models are used to estimate the number of trips originating from, ending in, and traveling between each zone. Growth factor models are applied to estimate future trip generation based on changes to population, income and vehicle ownership in each zone over time. The analysis is used to evaluate alternative MRTS network alignments and estimate passenger loading on each.

1. Transport demand analysis
Prepared By:- Bhishma Bhatti
Assistant Professor, Babaria Institute, Vadodara

2. Contents
Introduction
Travel demand survey
Trip production
Trip generation
Trip distribution

3. Introduction
• The following steps are involved in traffic demand analysis toward estimation
of ridership on Mass Rapid Transit System:-
• Preparation of Database: Involves collection of secondary data (studies
done earlier, census data, Master Plan, land use parameters etc) and primary
surveys (traffic and travel surveys).
• Development of Transport Demand Models: The process consists
of development of formulae (or models), enabling forecast of travel demand.

4. • Estimation of Land use Parameters: Land use parameters (viz., population,
employment) are to be estimated for the horizon years in order to assess the
future travel demand.
• Formulation & Evaluation of Alternative MRTS Networks: Various
alternative alignments for MRTS corridor are to be identified and passenger
loading on each alternative is estimated. The alternative having better ridership.

5. Traffic zone system
• The Study Area was divided into a total of 197 zones (186 internal and 11
external).
• The 186 internal zones consist of Ahmedabad Municipal Corporation (AMC)
area, Area of Ahmedabad Development Authority (AUDA) and
Gandhinagar.
• The zone size has been kept sufficiently small so as to have better sensitivity
to the Transport Demand model.
• The traffic zone system map for the Study Area is as below.

7. Traffic demand survey

8. Primary Surveys and Analysis
The following primary traffic and travel surveys have been conducted as a
part of the study:
i) Road Network Inventory and Speed & Delay Surveys
ii) Traffic Volume Counts
iii) Public Transport Passenger’s surveys
iv) Household Travel Surveys

9. 1. Road Network Inventory and Speed & Delay
Surveys
• Road network inventory has been carried out along all arterials and major
roads totaling to 1042 km.
• Speed and delay survey has been conducted using moving car method during
peak and off-peak periods.
• For private vehicles, during peak period about 71% road network is having
journey speed of less than or equal to 30 Kmph and for public vehicles during
peak period about 95% of the road network is having journey speed of less
than or equal to 30 Kmph.

10. No.
Journey
speed
Private Vehicle. Public Transport.
Peak Period off-peak period Peak period Off peak period
1 <= 10 1.92 % 0.89 % 3.94 % 1.54 %
2 11 - 20 10.66 % 9.23 % 39.46 % 10.94 %
3 21 - 30 58.67 % 29.14 % 51.66 % 47.87 %
4 > 30 28.76 % 60.74 % 4.94 % 39.65 %
Total 100.0% 100.0% 100.0% 100.0%

11. 2. Traffic Volume Counts
• Classified traffic volume count survey has been conducted at the selected mid-
block locations, Outer Cordon Location and Screen Line Locations.
• The survey helps in assessing the existing traffic characteristics as well as to
validate the transport demand model.
• The survey locations were selected in such a way that would cover entire
Study Area and assist in understanding the traffic pattern in the Study Area.

12. • a). Mid block count
• b). Outer cordon survey
• c). Traffic volume count at screen line
location

13. A). Midblock count
• Mid-block traffic counts were conducted at 33 locations in the Study Area. The
locations were chosen in such a way that combination of some of the mid-blocks
would act as screen line also.
• It was observed that traffic at different locations varies from 10511 PCUs (Thaltej
Village Road) to 64045 PCUs (K.G. Road, Near Delhi Darwaja) on a normal
working day
• At different mid-block locations peak hour traffic varies from 8.37% S.G. Highway
(Near Karnawati Club) to 10.85% (Ashraml Road. Near Fateh Pur) of the total daily
traffic .

14. Loc No. Name of location Vehicle PCUs Peak hr.
Traffic
PCUs
%peak
Hour
Share
factor
1 Ashram Road (Near Fateh Pur) 45281 35896 3893 10.85
2 Ashram Road (Near V.S. Hospital) 88343 62760 6012 9.58
3 Ashram Road (Near Sanyash Ashram 57528 42391 3680 8.68
4 Ashram Road (Near Usman Pura) 41048 31990 3268 10.22
5 Naya Wadaj Road 40972 28357 2661 9.38
6 R.T.O Office (Near Subhash Circle) 77999 55322 5561 10.05
7 Relief Road (Near Rewadi Bazar) 61380 40167 4006 9.97
8 Relief Road (Near Rupam Cinema) 39923 26713 2710 10.14
9 Relieaf Road (Near Relief Cinema) 67097 41750 4334 10.38
10 Gandhi Road (Near State Bank of 18729 12479 1215 9.73
11 Gandhi Road (Near Teen Darwaja) 36290 22928 1999 8.72
12 K.G. Road (Near Daryapur Darwaja) 78917 56218 5454 9.70
13 K.G. Road (Near Delhi Darwaja) 96115 64045 5458 8.52
14 Navrang Pura Road (Near Railway 41619 28580 3061 10.71

15. Loc No. Name of location Vehicle PCUs Peak hr.
Traffic
PCUs
%peak
Hour
Share
factor
15 Navrang Pura Terminal 20205 15332 1490 9.72
16 Gujarat University Road 54268 35451 3769 10.63
17 Manav Mandir Marg (Near Suchitra Apt.) 52830 36388 3623 9.96
18 Akashwani Kendra (Near Gandhi 100634 63586 5768 9.07
19 Rajiv Kaka Road (Near Usman 17693 13788 1227 8.90
20 Mangal Das Road (Near Pinnakle 63160 40476 3924 9.69
21 Mangal Das Road (Near Parimal 36644 25561 2295 8.98
22 Vikram Sara Bhai road 111039 72117 7716 10.70
23 Vikram Sarabhai Marg 35574 23893 2215 9.27
24 Vikram Sarabhai Marg (Near IIM) 41269 27148 2925 10.77
25 Netaji Road (Near Paldi Bus stand) 47104 33269 3264 9.81
26 C.G. Road 30742 20281 2144 10.57
27 120 Circular Road 25026 17702 1741 9.83
28 132 Ring Road (Near Jivraj Park 32046 25542 2464 9.65

16. b) Outer Cordon Surveys
• The outer cordon survey has been conducted at 11 locations along all the major roads
radiating from the Study Area.
• It was observed that the traffic at different locations varies between 3431 PCUs to
25041 PCUs.
• The peak hour traffic varies from 7.98% to 9.74% of the total daily traffic at various
locations.
• The daily passenger trips varies from 8184 (Borsad Highway) to 72948 (Rajkot
Highway).

17. 1 Viramgaon Highway 11837 15127 1235 8.16 35745
2 Rajkot Highway 17164 24748 2116 8.55 72948
3 Dholka Highway 4670 6204 604 9.74 18553
4 Baroda Highway 11042 20484 1731 8.45 40094
5 Borsad Highway 2609 3431 307 8.95 8184
6 Nadiad Highway 4058 5045 443 8.77 17136
7 Vadodra Exspress way 2986 3759 363 9.64 12989
8 Kapadwanj Highway 6058 7594 704 9.27 23671
9 Modasa Highway 6293 7698 620 8.05 19966
10 Himmatnagar highway 7779 12014 1063 8.85 28162
11 Mehsana Highway 15907 25041 1997 7.98 56982
Loc no. Name of location Vehicle PCUs Peak hour
Traffic
PCUs
% peak
hour
share
factor
Daily pass.
trips

18. c). Traffic volume count at screen line location.
• Traffic volume counts at 7 screen line points have been carried out
as part of the presented Study.
• The intensity of the traffic at screen line locations is presented in
Table.
1 Vasana Bridge 34478 47674 5585 11.71
2 Sardar Bridge 101381 72901 6673 9.15
3 Ellis Bridge 88975 59740 7391 12.37
4 Nehru Bridge 104937 68531 6226 9.08
5 Gandhi Bridge 154290 93938 9109 9.70
6 Subhash Bridge 80662 54269 4979 9.17
7 Indira Bridge 23938 20472 1990 9.72
No. Location Total vehicle Total
PCUs
Peak
hour
traffic
Peak
hour
factor

19. 3.) Public passanger survey
• In order to assess the public transport passenger’s characteristics in the
Study Area, two different types of surveys related to bus and other
public modes such as shared auto, tempo and rail were carried out at
selected locations.
• Bus Stop survey covered bus passenger origin-destination interviews with
boarding passenger count at 309 bus stops and 8 Terminals along the
proposed Metro Corridor and its influence area.

20. • Shared auto passengers’ boarding count was also carried out at 309 locations.
• . Rail passenger boarding count and OD surveys were carried out at 15 stations.
All surveys were conducted for 16 hour duration (from 6 AM to 10 PM).
• Subdevided into two parts:-
Bus and shared auto passenger survey
Rail Passenger survey
a).
b).

21. a) Bus and Shared Auto Passengers Survey
• The boarding and alighting counts of passengers was carried out at 15 minutes interval for
a period of 16 hrs..
• Income Tax office bus stop has the maximum volume of boarding passengers (about 2700),
Laldarwaja Terminal has the maximum volume of boarding passengers (about 35000) and
for shared autos, the maximum boarding of passenger takes place near Kalupur Terminal.
1 Bus 134425
2 Auto Rickshaw 62763
Total 197188
Sl no. Mode Total passanger
boarding

22. b). Rail Passenger’s Survey
• Rail passengers’ boarding and alighting counts and origin–destination
surveys were conducted at 15 railway stations.
• The maximum passenger traffic (boarding & alighting) is observed at
Kalupur station (71715 numbers) and the minimum passenger traffic
(boarding & alighting) is observed at GandhiNagar Station (275
numbers).

23. 4). Household Travel Survey
• The objective of the survey conducted at the residences of the Study
Area population was to collect the socio - economic characteristics
of the households and trip information of the individual members.
• The Household travel cum opinion survey for a sample of about 5247
households has been carried out.

24. • The following outputs are derived from the analysis of the household travel
survey.
• Distribution of the households according to household size and
vehicle ownership.
• Distribution of the individuals by their income, occupation,
education and expenditure on transport.
• Distribution of trips by mode and purpose.
• Distribution of trips by trip length.
1). Household by size
2). Distribution by monthly income
3). Distribution of trips by purpose

25. a) Households by Size
• Distribution of households according to the family size is
presented in Table.
• 5247 household had been surveyed.
SI. Household Number of Percentage
No. Size Households
1 Up to 2 290 5.53
2 3-4 2075 39.55
3 5-6 2053 39.13
4 7-8 568 10.83
5 >8 261 4.97
Total 5247 100.0

26. b). Distribution of Households by Monthly Income
• The distribution of sampled households according to their monthly
income ranges is presented in Table
1 <=Rs 5000 2232 42.54
2 Rs 5001 – Rs 10000 1790 34.11
3 Rs 10001 - Rs 15000 625 11.91
4 Rs 15001 – 20000 303 5.77
5 >Rs 20000 276 5.26
6 No Response 21 0.40
Total 5247 100.0
Sl no. Income group Number of individuals in
Samplad household
Percentage

27. Purposewise Distribution of Trips
• Table shows the distribution of trips.
Sl No Purpose No of Trips Percentage
1 Work 1223971 18.41
2 Business 616557 9.27
3 Education 1249221 18.79
4 Others 303372 4.56
6 Return Work 1177454 17.71
7
Return
Business 592519
8.91
8
Return
Education
1214181 18.26
9 Return Others 272560 4.10
Total 6667160 100.0

28. Trip Production
• Trip production is defined as all the trips of home based or as the origin of
the non home based trips.
• Trips can be classified by trip purpose, trip time of the day, and by person type.
• Trip generation models are found to be accurate if separate models are used based
on trip purpose.

29. Productions and Attractions
A worker leaves Zone 1 in the morning to
go to work in Zone 2
This results in 2 trip ends:
• One Production for Zone 1
• One Attraction for Zone 2
1
2
Residential
Non-residential
Residential
Non-residential
When that same worker leaves Zone 2 in
the evening to go to home to Zone 1
This results in another 2 trip ends:
• One Production for Zone 1
• One Attraction for Zone 2
Total Number of Trip Ends
Zone 1: 2 Trip Ends (2 Productions)
Zone 2: 2 Trip Ends (2 Attractions)

30. Modeling
• Growth factor mode is a mode which tries to predict the number of trips produced or attracted by a
house hold or zone as a linear function of explanatory variables.
• The models have the following basic equation:
Where, 𝑻𝒊 = number of future trips in the zone
𝒇𝒊 = 𝑔𝑟𝑜𝑤𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟
𝒕𝒊 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑡𝑟𝑖𝑝𝑠 𝑖𝑛 𝑡ℎ𝑎𝑡 𝑧𝑜𝑛𝑒
𝑻𝒊 = 𝒇𝒊 𝒕𝒊

31. • The simplest form of 𝒇𝒊 is represented as follows:
Where, P = Population of that zone
I = Income
V = Vehicles
c = Current year
d = Design year

32. Example :
Number of household - 275 with car and 275 without car
Avg. trip generation rate for each group is respectively 5.0 and 2.5 trips per day
Find the growth factor and future trips from that zone
Assuming that in the future, all household will have a car the population and income remains
constant.
Solution:
Current trip rate 𝒕𝒊 = [275 × 2.5] + [275 × 5.0] = 2062.5 trips / day.
Growth factor 𝒇𝒊 = = 550/257 = 2.0 (i.e. P & I are constant)

33. • Therefore, number of trips will produce in future:
= 2.0 x 2062.5
= 4125 Trips/Day
• Therefore in future we will have 4125 trips will Produce per day in that
particular area according to that we will design various facilities.
𝑻𝒊 = 𝒇𝒊 𝒕𝒊

34. Trip Generation
Trip Generation model is used to estimate the number of person-
trips that will begin or end in a given traffic analysis zone
1
2
3
4
5
6
8
7

35. Trip Generation
Developing and Using the Model
Calibrated
Model
Relating trip making
to socio-economic
and land use data
Estimated
Target year
socio-economic,
land use data
Predicted
Target year
No. of Trips
Survey Base Year
Socio-economic, land use
And
Trip making

36. Trip Generation
Developing and Using the Model
The trip generation model typically can take the form of
No. of trips = Function (pop, income, auto ownership rates)
The model is developed and calibrated using BASE year dataz

37. Trip Generation
Developing the Model
Trip Generation models are often developed from travel
surveys. These surveys are used to determine the trip
making pattern for a sampling of house holds in the area.
This trip making pattern is then related to land use
(nominally) and socioeconomic factors that are considered
to affect travel patterns
Common socioeconomic factors considered include
population, income, and auto ownership rates

38. Trip Generation
Trip Purpose
Often separate predictions are mode for different type of
trips since travel behavior depends on trip purpose
In other words different models must be developed for each
trip type
The category of trip types commonly used include
• Work trips
• School trips
• Shopping trips
• Recreational trips

39. Trip Generation
Demographics and Trip Making Factors affected by Land Use
The land use pattern may affect
Car ownership rates
Household size and composition
Number of daily trips
Mode of trips
Length of trips

40. Trip Generation
What is Predicted?
Trip generation models predict so called TRIP ENDS for each zone
The trip ends maybe classified as either
• ORIGINS and DESTINATIONS (O-D)
or
• PRODUCTIONS and ATTRACTIONS
The two sets of terms sound similar but there is a technical difference

41. Origins and Destinations
A worker leaves Zone 1 in the morning to
go to work in Zone 8
This results in 2 trip ends:
• One Origin for Zone 1
• One Destination for Zone 8
1
8
Residential
Non-residential
Residential
Non-residential
When that same worker leaves Zone 8 in
the evening to go to home to Zone 1
This results in another 2 trip ends:
• One Destination for Zone 1
• One Origin for Zone 8
Total Number of Trip Ends
Zone 1: 2 Trip Ends (1 O, 1 D)
Zone 8: 2 Trip Ends (1 O, 1 D)

42. Case study for Ahmedabad Metro
• Home based work trip
• The trip generation sub-model for home based one-way work
trips produced/attracted from/to a zone by all modes (mass, fast
and slow) is developed and presented in below Table .
• The independent variable for trip production is the zonal
population, whereas for the purpose of attraction, the
independent variable is the employment in each zone.

43. Trip Generation Sub-Models For Home Based One-Way
Work Trips – 2003
Dependent
Variable
Independent
Variable
Constant
Coefficient
Regression
Co-efficient
(Trip Rate)
(R2)
Co-efficient of
Determination
(Y) (X) (a) (b)
Trip Production
All Modes Population -61.9426 0.323506 0.97
Trip attraction
All modes Employment 1639.07 0.851493 0.57

44. • Home-Based Education Trips
• Summary of regression analysis for home-based one-way
education trips produced/attracted from/to a zone by different
modes is given in Table
• The independent variables used are zonal population and
zonal school enrolment respectively.

45. Trip Generation Sub-Models For Home Based One-Way
Education Trips – 2003
Dependent
Variable
Independent
Variable
Constant
Coefficient
Regression
Co-efficient
(Trip Rate)
(R2)
Co-efficient of
Determination
(Y) (X) (a) (b)
Trip Production
All modes Population 192.5665 0.046735 0.44
Trip attraction
All modes Employment 5066.982 2.960293 0.32

46. TRIP DISTRIBUTION

47. DEFINE TRIP
DISTRIBUTION
Estimates where the attraction in
zone i come from and where do
the productions go.
 The decision on where the trips
go is represented by comparing
the relative attractiveness and
accessibility of all zones in the
area.
We link production or origin
zones to attraction or destination
zones

48. Trip Distribution
A trip matrix is produced
The cells within the trip matrix are the
“trip interchanges” between zones
Zone 1
TAZ 1 2 3 4 5 6 7 8
1
2
3
4
5
6
7
8
Trip Matrix

49. Basic Assumptions of Trip Distribution
• Number of trips decrease with COST between
zones
• Number of trips increase with zone
“attractiveness”

50. Methods of Trip Distribution
1. GROWTH FACTOR MODEL
i Uniform Factor Model
ii Average Factor Model
iii Detroit Model
iv Frater Model
v Furness Model
2. SYNTHETIC MODEL
I Gravity Model
II Intervening opportunities Model
III Competing opportunities Model
IV Tanner Model

51. GROWTH FACTOR MODELS
• Growth Factor Models assume that we already have a basic trip matrix
• Usually obtained from a previous
study or recent survey data
TAZ 1 2 3 4
1 5 50 100 200
2 50 5 100 300
3 50 100 5 100
4 100 200 250 20

52. Growth Factor Models
• The goal is then to estimate the matrix at some point in the future
• For example, what would the trip matrix look like in 2 years time?
TAZ 1 2 3 4
1 5 50 100 200
2 50 5 100 300
3 50 100 5 100
4 100 200 250 20
Trip Matrix, t
(2008)
Trip Matrix, T
(2018)
TAZ 1 2 3 4
1 ? ? ? ?
2 ? ? ? ?
3 ? ? ? ?
4 ? ? ? ?

53. UNIFORM GROWTH
FACTOR MODEL

54. Uniform Growth Factor
 In this method a single growth factor for the entire urban area is computed.
All existing interzonal trips are then multiplied by this factor, in order to get future
interzonal trip .
The Uniform Growth Factor is typically used for over a 1 or 2 year horizon.
However, assuming that trips grow at a standard uniform rate is a fundamentally
flawed concept

55. Uniform Growth Factor
Tij = τ tij for each pair i and j
Tij = Future Trip Matrix
tij = Base-year Trip Matrix
τ = General Growth Rate
i = I = Production Zone
j = J = Attraction Zone

56. Uniform Growth Factor
TAZ 1 2 3 4
1 5 50 100 200
2 50 5 100 300
3 50 100 5 100
4 100 200 250 20
TAZ 1 2 3 4
1 6 60 120 240
2 60 6 120 360
3 60 120 6 120
4 120 240 300 24
Trip Matrix, t
(2008)
Trip Matrix, T
(2018)
If we assume τ = 1.2 (growth rate), then…
Tij = τ tij
= (1.2)(5)
= 6

57. CASE STUDY OF AHMEDABAD
METRO RAIL

59. Solution:

64. THE GRAVITY MODEL

65. The Gravity Model
 The most widely used trip distribution
model
 The model states that the number of trips
between two zones is directly proportional
to the number of trip attractions generated
by the zone of destination and inversely
proportional to a function of time of travel
between the two zones.

66. • The gravity model is expressed as

67. Calibrating a Gravity Model
• Calibrating of a gravity model is accomplished by developing
friction factors and developing socioeconomic adjustment factors
• Friction factors reflect the effect travel time of impedance has
on trip making
• A trial-and-error adjustment process is generally adopted
• One other way is to use the factors from a past study in a
similar urban area

69. • An example of smoothed values of F factors in Figure 11-9.
• In general, values of F decreases as travel time increases, and may take the
form F varies as t-1, t-2, or e-t.
Figure 11-9 Smoothed Adjusted Factors, Calibration 2

70. • A more general term used for representing travel time (or a
measure of separation between zones) is impedance, and the
relationship between a set of impedance (W) and friction factors (F)
can be written as:
Example:
A gravity model was calibrated with the following results:
Impedance (travel time, mins), W 4 6 8 11 15
Friction factors, F .035 .029 .025 .021 .019
Using the f as the dependent variable, calculate parameter
A and c of the equation.

71. Solution:
The equation can be written as
ln F = ln A – c ln W
ln W 1.39 1.79 2.08 2.40 2.71
ln F -3.35 -3.54 -3.69 -3.86 -3.96
These figures yield the following values of A = .07 and c = .48.
Hence, F = 0.07/W0.48

72. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.995749
R Square 0.991516
Adjusted R
Square 0.988688
Standard
Error 0.02602
Observations 5
ANOVA
df SS MS F
Significance
F
Regression 1 0.237369 0.237369 350.5919 0.000333
Residual 3 0.002031 0.000677
Total 4 0.2394
Coefficients
Standard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -2.73218 0.05194 -52.6023 1.51E-05 -2.89748 -2.56689 -2.89748 -2.56689
lnw -0.461 0.024621 -18.7241 0.000333 -0.53935 -0.38265 -0.53935 -0.38265
Since, ln A = -2.73218,
A = e^(-2.73218)
A = .065
and
c = - (-.461)
c = 0.461

73. Refrences
• DPR of Ahmedabad Metro

74. THANK YOU