M.Tech. Dissertation

1. P.G. section in Transportation Engineering & Planning
DEPARTMENT OF CIVIL ENGINEERING
SARDAR VALLABHBHAI NATIONAL INSTITUTE OF TECHNOLOGY SURAT, INDIA
DISSERTATION (CE-864)
“DISAGGREGATE TRAFFIC FORECASTING
AND DIVERSION ANALYSIS FOR INTER-REGIONAL TRAFFIC
CORRIDOR USING ECONOMETRIC APPROACH”
Presented by :
Rahul Deshwal
(P17TP003)
Guided by:
Prof. G. J. Joshi
Dr. S. S. Arkatkar
1

2. CONTENTS
I. Introduction
II. Need for Study
III. Literature Review
IV. Objectives and Scope of Work
V. Methodology
VI. Study Area
VII.Data Collection and Analysis
VIII.Employment Forecasting
IX. Traffic Prediction for NH-48
X. Diversion Analysis
XI. Results and Discussion
XII.References
2

3. Introduction
 Planning is the process of thinking about the activities required to achieve a
desired goal.
 Planning of the region is done in two aspects:
 Economic planning, and
 Physical planning.
 Transportation system plays an important role in economic, industrial, social and
cultural development for the region. Hence, Regional planning of transport
infrastructure for the region's connectivity and circuity with the other regions
becomes crucial.
 Regional transportation planning provides integration of transportation plan with
land use plan of the region.
 Road transport in India is the dominant mode of transport
 65% of freight movement, and
 85% of passenger traffic.
 The National Highways network of 115,435 km contributes to about 2.7% of the country’s
road network and carries about 40% of total traffic.
3

4. Need of Study
 The Mumbai–Ahmedabad Corridor in the western part of the country is one of the
important transport corridors of the country.
 Industries like, textile industry, gems and jewelleries, petrochemical & fertilizer and
other industrial complexes have been established along this corridor.
 According to EIA report submitted by ICT Pvt. Ltd.,
 The average journey speed on Mumbai-Vadodara section of NH-48 was found 50-60
km/hr whereas a NH is designed for the speed of 100 km/hr.
 NH-48, 6-lane highway, was carrying traffic in the range of 50,000 to 80,000 PCU per
day in 2013 and reached LOS E in 2015 itself.
 Report has growth factor and elasticity based aggregate traffic forecast which
undermines the individual growth patterns of different vehicle class.
4

5. Author Title Review and Findings
Stephen Fretz,
Christoph Gorgas
(2013)
Regional economic
effects of transport
infrastructure
expansion:
evidence from the
Swiss highway
network.
The main aim of the study was to quantitatively assess the
impact of improved regional accessibility. Variables such
as GDP, unemployment rate and industrial structure.
They concluded that improved transport infrastructure
network shows improvement in intangible benefits at
regional level, and also labour market play an important
role for infrastructure expansion to generate benefits in
terms of income.
Ying Jin,
Ian Williams
(2000)
A new regional
economic model for
European transport
corridor studies.
The author reported the development of new operational
model for assessing the socio-economic impact of
strategic transport initiatives.
They have described the likely use of regional economic
model for transport.
They linked transport demand forecast with regional
economy via regional economic model and assessed the
impact of transport in context of that regional economy.
Literature Review: Regional Economy5

6. Author Title Review and Findings
Jim Yoo Kim, Jung
Hoon Han.
(2015)
Straw effects of
new highway
construction on
local population and
employment
growth.
The study was carried out to examine the rising concern of
straw effects of new highway using difference-in-
difference model to measure effects of new highway on
local population and employment growth.
The results revealed that no evidence of the straw effects
were found and not only the new highways contribute to
increasing population and employment in lagging areas but
also improve the accessibility of existing highway.
Uwe Blien,
Tassinopoulos
(2001)
Forecasting
regional
employment
The authors described forecasting of employment was
based on entropy optimizing procedure, a newly developed
for the estimation of matrices from heterogeneous
information.
Simple linear regression using first differences between
years was developed for estimating the trend of
employment.
They found employment forecasting becomes necessary as
the change in employment causes change in vehicle
ownership.
Literature Review: Employment Forecasting6

7. Author Title Review and Findings
Kartikeya Jha,
Nishita Sinha, S.S.
Arkatkar
(2012)
Modelling Growth
Trend & Forecasting
Techniques for
Vehicular Population
in India.
Trend Line Analysis of last 25 years was carried out.
Econometric regression models were also developed using
variables such as population and per capita income.
Time Series Analysis was also done using the Box and Jenkins
Methodology.
They found ARIMA models of Box and Jenkins methodology
perform better than trend line analysis and econometric
regression analysis.
Shabana Thabassum
(2013)
Impact of state-wise
vehicle contribution
on traffic growth
rates for National
Highways
The impact of different state vehicular growth on the state
highway.
Socio-economic variable such a population, NSDP and per
capita income (PCI) were used to determine the elasticity of
transport demand and develop regression models.
Traffic growth rate for different categories of vehicle was
determined, and forecasted traffic was used for financial
analysis and pavement design.
Indian Road
Congress
(2015)
IRC: 108-2015
Guidelines for
Traffic Forecast on
Highways
It provides methodology for traffic forecasting process.
Literature Review: Traffic Forecasting7

8. 8
Stage – 3
Forecasting of Travel
Stage – 2
Analysis of Base Year Travel
Stage – 1
Preparatory Works
Study of the Base Year Transport Network Map
Reconnaissance Survey
Identification of the PIA and TAZs
Identification of the Homogenous Traffic Sections
Identification of Primary Surveys and
their Locations
Primary Traffic Studies
Base Year Traffic Analysis
Normal Traffic
Forecast
Identification of Secondary Data and
their Sources
Collection of Secondary Data
Estimation of Growth
Factor for Normal Traffic
Developmental Traffic
Forecast
Total Traffic Forecast
Horizon year Transport
Network
Generated Traffic Forecast
Source:- IRC:108-2015 Fig. A.1 Traffic Forecasting Process

9. Contd.
 Traffic Growth Forecast
9
AADT
Normal Traffic
Forcast
• Past Traffic Trend
• Past trend of Vehicle
Population
• Elasticity of Transport
Demand
• Time Series (SMA, DMA,
SES, ARMA, ARIMA, etc.)
Developmental
Traffic Forecast
• New township
• Industrial unit or SEZ
Generated
Traffic Forecast
• Diverted Traffic
• Induced Traffic

10. Objectives
The main objectives of this research work are as follows:
 To analyse historical inter-regional traffic flow pattern of NH 48 on various temporal
scale
 To analyse effect of Regional/National Economic growth on traffic flow of NH 48
 To develop appropriate Time Series model for Traffic Forecast and consequent
LOS pattern
 To develop models for Diversion Forecast for alternate highway.
10
Scope of Work
 Historical traffic data of toll booths in region
 Economic growth indicators GDP and GSDP
 Impact of the industrial growth of the study region (South Gujarat) on the National Highway 48.
 User response survey for diversion analysis
 Viability of the proposed Vadodara-Mumbai Expressway in terms of travel demand.

11. 11
Primary Data Secondary Data
Methodology

12. The National Highway passes from Major
Cities:-
• Delhi
• Gurugram
• Jaipur
• Ajmer
• Beawar
• Udaipur
• Ahmedabad
• Vadodara
• Surat
• Mumbai
12 NH-48 from Delhi to Mumbai

13. 13 STUDY REGION

14. DISTRICTS AND CITIES IN STUDY REGION14
Study Region consist of 5 Districts
of South Gujarat region:
• Vadodara,
• Bharuch,
• Surat,
• Navsari,
• Valsad.
Source: Draft EIA report submitted by ICT Pvt. Ltd., New Delhi

15. 15 CHANGE IN POPULATION OVER DECADE

16. 16 CHANGE IN POPULATION DENSITY OVER DECADE

17. • Vadodara – 3
• Bharuch – 8
• Surat – 3
• Valsad – 2
Districtwise
SEZ's
approvals
in Region
• 18 Private Industrial Park
• 42 GIDC Industrial Estate
Industrial
Park in
Region
• Dahej
• Hazira
• Magdalla
• Vansi
Ports in
Region
17
Private Industrial Park
Bharuch – 11
Surat – 3
Valsad – 2
Vapi – 2
GIDC Industrial Estate
Bharuch – 17
Surat – 14
Valsad – 7
Navsari – 4
SEZ AND INDUSTRIES IN REGION

18. NH 53
NH
360
Toll Booth
18 Existing Highway in Study Region

19.  Phase 1 consist of construction of 274 km road
stretch
 260.4 km is in state of Gujarat,
 5.5 km in Union Territory of Dadra and Nagar
Haveli and,
 8.1 km in district of Thane in state of
Maharashtra
 Existing NH 48 in the same corridor has stretch of
277 km in Gujarat.
• VME is passing through
• Vadodara (54.4km),
• Bharuch (62.5 km),
• Surat (57.3km),
• Navsari (37.6km) and
• Valsad (48.6km)
in the state of Gujarat (260.4km).
19 Vadodara Mumbai Expressway (VME)

20. 20
DATA

21. DATA COLLECTION AND ANALYSIS21
Primary Data
(Questionnaire Survey)
Revealed Preference data
Vehicle Characteristics
Travel Characteristics
Stated Preference data
Priority Ranking
Willingness to Pay
Secondary Data
Classified monthly
traffic data of toll
Road Network
GDP/GSDP data
Industry data
RTO data

22. SECONDARY DATA22
Government Agency Data Collected Period
NHAI – PIU Surat
Classified Monthly Traffic Volume
(Schedule M) of Karjan toll,
Boriach toll and Bhagwada toll
2009-2018
MSME – Development
Institute
Employment and Investment Data
1985-2011
2006-2015
Directorate of Census
Operations, Gujarat
Population, Household and
Employment
1991, 2001,
2011
Planning Commission GDP and GSDP 2001-2018
RTO, Gujarat Vehicle Registration 2000-2018

23. TOLL DATA23

24. Districts/Region Vadodara Bharuch Surat Navsari Valsad Region
Investment
Year 32 22 32 16 19 32
CAGR 24.93% 26.15% 29.34% 38.92% 40.62% 28.85%
Employment
Year 32 14 32 16 19 32
CAGR 17.16% 36.11% 21.01% 25.87% 27.49% 20.60%
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
0 10 20 30 40
CumulativeInvestment(LakhRs.)
Year
Growth in Investment in Districts of Region
Vadodara Bharuch Surat Navsari Valsad Region
0
500000
1000000
1500000
2000000
0 10 20 30 40
CumulativeEmployment
Year
Growth in Employment in Districts of Region
Vadodara Bharuch Surat Navsari Valsad Region
EMPLOYMENT AND INVESTMENT DATA24

25. EMPLOYMENT MULTIPLIER25
Gujarat Vadodara Bharuch Surat Navsari Valsad Region
Industry Emp 3.55 0.21 0.08 0.62 0.02 0.10 1.03
Agricultural Emp 10.17 0.88 0.33 0.44 0.32 0.33 2.30
Basic Emp 13.73 1.10 0.41 1.06 0.34 0.43 3.33
Non-Basic Emp 11.04 0.60 0.22 1.50 0.25 0.32 2.88
Total Emp 24.77 1.69 0.63 2.55 0.59 0.74 6.21
Total Population 60.44 4.17 1.55 6.08 1.33 1.71 14.83
WPR 0.41 0.41 0.40 0.42 0.45 0.44 0.42
Base Ratio 0.80 0.54 0.53 1.42 0.73 0.75 0.86
Emp Multiplier 1.80 1.54 1.53 2.42 1.73 1.75 1.86
Emp (Employment) and Population in Million's
WPR – Work Participation Rate and Region is Study Area

26. SURVEY LOCATIONS26
Preliminary Survey Locations Primary Survey Locations

27. 27
 A total of 806 samples were
collected in preliminary and
primary survey removing
samples with
inappropriate/incomplete data.
 70 Samples were collected in
preliminary survey on basis of
which 5 locations for primary
survey were finalised.
 736 Samples were collected from
5 locations in 2 days time during
primary survey.
PRIMARY
DATA

28. Traffic Entry and
Exit locations
Origin Destination
Gujarat 64 67
Maharashtra 26 20
0
10
20
30
40
50
60
70
80
Percentage%
Movement of Vehicle for Gujarat and Maharashtra
E
N
ENE
Sura
t
Valsad
E
E
E
S
0
5
10
15
20
25
30
Percentage
Location
Distribution of Primary data based on O-D pattern
Origin Destination
28

29. Travel Pattern and
Route Preference
0%
20%
40%
60%
80%
100%
Car LCV Bus Truck MAV
Percentage%
Vehicle Category
Vehicle Category Travel Pattern
Internal to Internal Internal to External
External to Internal External to External
Car LCV Bus Truck MAV Total
Yes 81 70 38 81 85 81
No 19 30 63 19 15 19
0
10
20
30
40
50
60
70
80
90
Percentage%
Vehicle Category
Preference for Vadodara Mumbai Expressway
Region
1
Region
2
Region
3
Region
4
External
External
External
External
I-E & E-I
E-E
I-I
29

30. Descriptive Statistics
45%
12%
20%
23%
Travel Pattern (No)
Internal to
Internal
Internal to
External
External to
Internal
External to
External
16%
21%
24%
39%
Travel Pattern (Yes)
Internal to
Internal
Internal to
External
External to
Internal
External to
External
30

31. Priority for Road Users
0
50
100
150
200
Travel
Time
Travel
Cost
Distance Road Side
Amenities
Prioirty for Car
1 2 3 4
0
10
20
30
40
50
60
70
Travel
Time
Travel
Cost
Distance Road Side
Amenities
Prioirty for LCV
1 2 3 4
0
20
40
60
80
100
120
140
Travel
Time
Travel
Cost
Distance Road Side
Amenities
Prioirty for Bus_Truck
1 2 3 4
0
50
100
150
200
250
Travel
Time
Travel
Cost
Distance Road Side
Amenities
Prioirty for MAV
1 2 3 4
31

32. PHOTOGRAPHS DURING SURVEY32

33. 33
FORECASTING

34. EMPLOYMENT FORECASTING34
District Model Equation R² RMSE MAE MAPE
Vadodara INEMPVA y = 0.396x + 25067 0.964 11481 9513 11%
Bharuch INEMPBH y = 0.372x - 359 0.979 4389 3530 8%
Surat INEMPST y = 0.512x + 74338 0.984 41060 31713 11%
Navsari INEMPNI y = 0.337x + 3915 0.933 2229 1693 12%
Valsad INEMPVD y = 0.317x + 7024 0.978 6433 4773 8%
Region INEMPRN y = 0.463x + 101376 0.986 54175 41013 10%
y = Cumulative Employment and x = Cumulative Investment

35. IN-EMP MODELS35
0
50000
100000
150000
200000
250000
300000
0 200000 400000 600000 800000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPVA
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
0 100000 200000 300000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPBH
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
0 1000000 2000000 3000000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPST
0
5000
10000
15000
20000
25000
30000
0 20000 40000 60000 80000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPNI
0
20000
40000
60000
80000
100000
120000
140000
0 100000 200000 300000 400000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPVD
0
500000
1000000
1500000
2000000
2500000
0 2000000 4000000 6000000
CumulativeEMPLOYMENT
Cumulative INVESTMENT (Lakh Rs.)
INEMPRN

36. TRAFFIC PREDICTION FOR NH-4836
 Traffic Prediction by Aggregate
Approach
 Traffic Prediction by Disaggregate
Approach
PCU Values as per Indo-HCM 2017
Vehicle Type Equivalent Factor
Car 1
Light Commercial Vehicle (LCV) 3.2
Bus/Truck 5
Multi Axle Vehicle (MAV) 5.5

37. AGGREGATE APPROACH37
 Employment and Investment
 Vehicle Registration
 GDP and GSDP

38. EMPLOYMENT, INVESTMENT & AADT RELATIONSHIP38
Variable Equations for Karjan toll R² RMSE MAE MAPE
REmp AADT = 0.013REmp + 83442 0.8583 2028 1879 1.91%
RIn AADT = 0.005RIn + 87022 0.8504 2083 1893 1.93%
REmp-RIn AADT = 0.295REmp - 0.11RIn 0.9923 8750 7700 7.64%
Variable Equation for Choryasi toll R² RMSE MAE MAPE
REmp AADT = 0.01REmp + 80271 0.7851 1903 1632 1.75%
RIn AADT = 0.004RIn + 83173 0.7293 2136 1870 2.00%
REmp-RIn AADT = 0.063REmp - 0.021Rin + 65982 0.9323 1068 871 0.97%
Variable Equation for Boriach toll R² RMSE MAE MAPE
REmp AADT = 0.022REmp + 51885 0.8177 4046 3195 3.96%
RIn AADT = 0.009RIn + 57482 0.8519 3647 3036 3.80%
REmp-RIn AADT = 0.189REmp - 0.064RIn 0.9891 8406 7539 9.33%
Variable Equation for Bhagwada toll R² RMSE MAE MAPE
REmp AADT = 0.019REmp + 52774 0.8852 2661 2035 2.61%
RIn AADT = 0.008RIn + 57795 0.9034 2441 2028 2.63%
REmp-RIn AADT = 0.193REmp - 0.067RIn 0.9917 7036 6225 7.99%

39. CONTD.39
80000
90000
100000
110000
120000
80000 90000 100000 110000 120000
PredictedAADT
Observed AADT
Predicted vs Observed Karjan AADT
REmp
Rin
REmp-RIn
80000
90000
100000
80000 90000 100000
PredictedAADT
Observed AADT
Predicted vs Observed Choryasi AADT
REmp
Rin
REmp-RIn
60000
70000
80000
90000
100000
110000
60000 80000 100000 120000
PredictedAADT
Observed AADT
Predicted vs Observed Boriach AADT
REmp
Rin
REmp-RIn
60000
70000
80000
90000
100000
60000 70000 80000 90000 100000
PredictedAADT
Observed AADT
Predicted vs Observed Bhagwada AADT
REmp
Rin
REmp-RIn

40. VEHICLE REGISTRATION & AADT RELATIONSHIP40
Variable (in Million) Equation (AADT in '000) for Karjan toll R² RMSE MAE MAPE
RegTV AADT = 22.04RegTV + 61 0.728 4512 3431 3.24%
RegV AADT = 2.076RegV + 69 0.753 4302 3296 3.13%
Variable (in Million) Equation (AADT in '000) for Choryasi toll R² RMSE MAE MAPE
RegTV AADT = 21.244RegTV + 55 0.849 3002 2398 2.49%
RegV AADT = 1.997RegV + 64 0.874 2737 2090 2.18%
Variable (in Million) Equation (AADT in '000) for Borich toll R² RMSE MAE MAPE
RegTV AADT = 28.005RegTV + 30 0.873 3915 3103 3.61%
RegV AADT = 2.529RegV + 42 0.876 3867 2807 3.20%
Variable (in Million) Equation (AADT in '000) for Bhagwada toll R² RMSE MAE MAPE
RegTV AADT = 26.601RegTV + 29 0.906 3143 2638 3.17%
RegV AADT = 2.41RegV + 41 0.915 2990 2574 3.10%

41. CONTD.41
90000
95000
100000
105000
110000
90000 95000 100000 105000 110000
PredictedAADT
Observed AADT
Observed vs Predicted Karjan AADT
RegTV
RegV
80000
90000
100000
110000
80000 90000 100000 110000
PredictedAADT
Observed AADT
Observed vs Predicted Choryasi AADT
RegTV
RegV
60000
70000
80000
90000
100000
110000
60000 80000 100000
PredictedAADT
Observed AADT
Observed vs Predicted Boriach AADT
RegTV
RegV
60000
70000
80000
90000
100000
110000
60000 80000 100000
PredictedAADT Observed AADT
Observed vs Predicted Bhagwada AADT
RegTV
RegV

42. GDP, GSDP & AADT RELATIONSHIP42
Variable Equation for Karjan toll R² RMSE MAE MAPE
GDPIND LN(AADT) = 0.22LN(GDPIND) + 9.04 0.72441 4695 3298 3.08%
GSDPGUJ LN(AADT) = 0.2LN(GSDPGUJ) + 9.75 0.74658 4496 3203 3.00%
GSDPMH LN(AADT) = 0.21LN(GSDPMH) + 9.55 0.71941 4728 3377 3.16%
GDPIND - GSDPGUJ LN(AADT) = 2.79LN(GDPIND) - 2.32LN(GSDPGUJ) 0.99996 7667 6318 6.05%
GSDPGUJ - GSDPMH LN(AADT) = -5.9LN(GSDPGUJ) + 6.68LN(GSDPMH) 0.99973 18279 16113 16.21%
GDPIND - GSDPMH LN(AADT) = 4LN(GDPIND) - 3.6LN(GSDPMH) 0.99998 6163 5147 4.85%
Variable Equation for Choryasi toll R² RMSE MAE MAPE
GDPIND LN(AADT) = 0.22LN(GDPIND) + 8.87 0.82618 3243 2634 2.72%
GSDPGUJ LN(AADT) = 0.21LN(GSDPGUJ) + 9.62 0.83755 3121 2562 2.66%
GSDPMH LN(AADT) = 0.21LN(GSDPMH) + 9.4 0.82725 3219 2671 2.76%
GDPIND - GSDPGUJ LN(AADT) = 2.77LN(GDPIND) - 2.29LN(GSDPGUJ) 0.99997 5729 5010 5.21%
GSDPGUJ - GSDPMH LN(AADT) = -5.94LN(GSDPGUJ) + 6.71LN(GSDPMH) 0.99977 15469 13375 14.44%
GDPIND - GSDPMH LN(AADT) = 3.92LN(GDPIND) - 3.52LN(GSDPMH) 0.99998 5549 4204 4.31%

43. CONTD.43
Variable Equation R² RMSE MAE MAPE
GDPIND LN(AADT) = 0.35LN(GDPIND) + 7.29 0.86467 4100 3288 3.90%
GSDPGUJ LN(AADT) = 0.31LN(GSDPGUJ) + 8.49 0.87359 4004 3086 3.64%
GSDPMH LN(AADT) = 0.33LN(GSDPMH) + 8.14 0.84806 4338 3507 4.17%
GDPIND - GSDPGUJ LN(AADT) = 2.38LN(GDPIND) - 1.81LN(GSDPGUJ) 0.99997 5395 4544 5.61%
GSDPGUJ - GSDPMH LN(AADT) = - 4.89LN(GSDPGUJ) + 5.72LN(GSDPMH) 0.99978 12768 11082 14.08%
GDPIND - GSDPMH LN(AADT) = 3.45LN(GDPIND) - 2.97LN(GSDPMH) 0.99999 2826 2324 2.70%
Variable Equation R² RMSE MAE MAPE
GDPIND LN(AADT) = 0.34LN(GDPIND) + 7.29 0.91217 3121 2561 3.10%
GSDPGUJ LN(AADT) = 0.31LN(GSDPGUJ) + 8.48 0.92252 2938 2426 2.93%
GSDPMH LN(AADT) = 0.33LN(GSDPMH) + 8.12 0.90280 3275 2673 3.24%
GDPIND - GSDPGUJ LN(AADT) = 2.38LN(GDPIND) -1.81LN(GSDPGUJ) 0.99997 4828 4150 5.18%
GSDPGUJ - GSDPMH LN(AADT) = - 4.95LN(GSDPGUJ) + 5.77LN(GSDPMH) 0.99980 11760 9973 13.15%
GDPIND - GSDPMH LN(AADT) = 3.42LN(GDPIND) - 2.93LN(GSDPMH) 0.99999 2999 2554 3.10%

44. CONTD.44
80000
90000
100000
110000
80000 90000 100000 110000
PredictedAADT
Observed AADT
Predicted vs Observed Karjan AADT
Ind
Guj
MH
Ind-Guj
Guj-MH
Ind-MH
70000
80000
90000
100000
110000
70000 80000 90000 100000 110000
PredictedAADT
Observed AADT
Predicted vs Observed Choryasi AADT
Ind
Guj
MH
Ind-Guj
Guj-MH
Ind-MH
60000
70000
80000
90000
100000
110000
60000 80000 100000
PredictedAADT
Observed AADT
Predicted vs Observed Boriach AADT
Ind
Guj
MH
Ind-Guj
Guj-MH
Ind-MH 60000
70000
80000
90000
100000
110000
60000 70000 80000 90000 100000 110000PredictedAADT
Observed AADT
Predicted vs Observed Bhagwada AADT
Ind
Guj
MH
Ind-Guj
Guj-MH
Ind-MH

45. TIME SERIES45
 Regression Model
 Moving Average with Classical
Decomposition
 ARIMA Model

46. REGRESSION MODEL
46
Vehicle KARJAN Trend R² MAE MAPE RMSE
Car
Calibration
y = 0.74x + 3691 0.55
1722 13 2236
Validation 2614 12 3522
LCV
Calibration
y = 0.85x + 596 0.76
203 6 251
Validation 331 8 411
Bus_Truck
Calibration
y = 0.7x + 1587 0.48
299 6 386
Validation 388 7 457
MAV
Calibration
y = 0.6x + 3603 0.38
386 4 501
Validation 821 8 896
Vehicle BORIACH Trend R² MAE MAPE RMSE
Car
Calibration
y = 0.85x + 1196 0.72
1094 15 1448
Validation 1444 12 1961
LCV
Calibration
y = 0.97x + 157 0.92
215 6 276
Validation 304 6 384
Bus_Truck
Calibration
y = 0.79x + 931 0.60
247 6 334
Validation 242 6 346
MAV
Calibration
y = 0.78x + 1598 0.56
331 5 418
Validation 482 6 594

47. CONTD.47
Vehicle BHAGWADA Trend R² MAE MAPE RMSE
Car
Calibration
y = 0.89x + 706 0.79
891 17 1250
Validation 1202 16 1581
LCV
Calibration
y = 0.95x + 235 0.90
276 7 361
Validation 230 4 1136
Bus_Truck
Calibration
y = 0.59x + 1845 0.34
268 6 339
Validation 243 5 1026
MAV
Calibration
y = 0.73x + 1915 0.51
378 6 469
Validation 245 3 77

48. MOVING AVERAGE WITH CLASSICAL
DECOMPOSITION48
SEASONAL FACTOR CALCULATION
2009 2010 2011 2012 2013 2014 2015 Average
Jan 1.03 1.06 1.06 1.08 0.97 1.06 1.04 1.04
Feb 0.99 1.01 1.03 1.01 0.97 1.27 1.05 1.04
Mar 0.91 0.86 0.92 0.94 0.88 1.05 0.93 0.93
Apr 0.99 0.96 1.08 1.01 0.88 1.08 1.00 1.00
May 1.38 1.30 1.22 1.34 1.26 1.26 1.29 1.29
Jun 1.11 1.06 1.09 1.09 1.18 1.00 1.09 1.09
Jul 0.85 0.86 0.83 0.79 0.89 0.76 0.83 0.83
Aug 0.75 0.83 0.88 0.85 0.94 0.82 0.85 0.84
Sep 0.80 0.72 0.72 0.75 0.73 0.78 0.75 0.75
Oct 1.22 0.81 1.01 0.76 0.79 1.00 0.93 0.93
Nov 1.04 1.37 1.15 1.29 1.34 0.97 1.19 1.19
Dec 1.15 1.14 1.05 1.09 1.05 1.04 1.08 1.08
Total 12.03 12
Required Total 12
Adjusting factor 0.998

49. CONTD.49
Vehicle Approach KARJAN Trend in data MAE RMSE MAPE
Car
Multiplicative
Calibration
y= 115.43x + 8733
885 1333 7
Validation 1321 2376 6
Additive
Calibration
y =114.03x + 8806
752 1014 6
Validation 1020 1570 5
LCV
Multiplicative
Calibration
y= 18.52x + 2888
228 316 7
Validation 412 490 9
Additive
Calibration
y =18.23x + 2867
154 200 4
Validation 258 352 6
BUS_TRUCK
Multiplicative
Calibration
y= -10.73x + 5634
361 452 7
Validation 1324 1379 22
Additive
Calibration
y= -10.55x + 5607
229 291 4
Validation 1340 1350 23
MAV
Multiplicative
Calibration
y=15.95x + 8303
555 727 6
Validation 731 906 7
Additive
Calibration
y= 15.56x + 8264
347 422 4
Validation 823 884 8

50. CONTD.50
5000
10000
15000
20000
25000
5000 10000 15000 20000 25000
Predicted
Observed
Cars
M_Predicted A_Predicted
2000
3000
4000
5000
2000 3000 4000 5000
Predicted
Observed
LCV
M_Predicted A_Predicted
3000
4000
5000
6000
7000
3000 4000 5000 6000 7000
Predicted
Observed
BUS_TRUCK
M_Predicted A_Predicted
6000
7000
8000
9000
10000
11000
12000
6000 7000 8000 9000 10000 11000 12000
Predicted
Observed
MAV
M_Predicted A_Predicted

51. CONTD.51
Vehicle Approach BORIACH Trend in data MAE RMSE MAPE
Car
Multiplicative
Calibration
y = 98.69x + 3263
792 1010 11
Validation 1810 2170 16
Additive
Calibration
y = 98.45x + 3253
822 1053 12
Validation 1879 2082 17
LCV
Multiplicative
Calibration
y = 38.66x + 1842
95 242 6
Validation 646 767 13
Additive
Calibration
y = 38.70x + 1835
214 245 6
Validation 648 754 13
BUS_TRUCK
Multiplicative
Calibration
y = -1.26x + 4317
440 472 10
Validation 352 466 8
Additive
Calibration
y = -1.17x + 4311
407 471 10
Validation 353 466 8
MAV
Multiplicative
Calibration
y = 20.08x + 6188
524 248 3
Validation 563 641 6
Additive
Calibration
y = 18.45x + 6236
199 254 3
Validation 654 721 7

52. CONTD.52
0
5000
10000
15000
0 5000 10000 15000
Predicted
Observed
Cars
M_Predicted A_Predicted
1000
2000
3000
4000
5000
6000
7000
1000 2000 3000 4000 5000 6000 7000
Predicted
Observed
LCV
M_Predicted A_Predicted
3000
4000
5000
6000
3000 4000 5000 6000
Predicted
Observed
BUS_TRUCK
M_Predicted A_Predicted
5000
6000
7000
8000
9000
10000
11000
5000 6000 7000 8000 9000 10000 11000
Predicted
Observed
MAV
M_Predicted A_Predicted

53. CONTD.53
Vehicle Approach BHAGWADA Trend in data MAE RMSE MAPE
Car
Multiplicative
Calibration
y = 89.96x + 1635
1010 1458 16
Validation 3165 3404 43
Additive
Calibration
y = 90.44x + 1576
1065 1487 19
Validation 3170 3356 45
LCV
Multiplicative
Calibration
y =45.65x + 1825
264 318 7
Validation 960 1091 18
Additive
Calibration
y =45.7x + 1822
264 315 7
Validation 969 1090 18
BUS_TRUCK
Multiplicative
Calibration
y= -4.99x + 4851
338 912 6
Validation 542 597 11
Additive
Calibration
y = -4.24x + 4819
341 911 6
Validation 519 578 11
MAV
Multiplicative
Calibration
y=19.97x + 5903
219 266 3
Validation 357 464 4
Additive
Calibration
y =19.95x + 5908
218 265 3
Validation 353 453 4

54. CONTD.54
0
5000
10000
15000
0 5000 10000 15000
Predicted
Observed
Cars
M_Predicted A_Predicted
1500
3000
4500
6000
1500 3000 4500 6000
Predicted
Observed
LCV
M_Predicted A_Predicted
3000
4000
5000
6000
7000
3000 4000 5000 6000 7000
Predicted
Observed
BUS_TRUCK
M_Predicted A_Predicted
5000
6000
7000
8000
9000
5000 6000 7000 8000 9000
Predicted
Observed
MAV
M_Predicted A_Predicted

55. ARIMA MODEL55

56. CONTD.56
 ARIMA(p,d,q)
 ‘p’ represent Autoregression (AR),
 ‘d’ represent non-seasonal differencing order, and
 ‘q’ represent Moving average (MA)
 Seasonal ARIMA(p,d,q)x(P,D,Q) (SARIMA)
 ‘P’ represent Seasonal Autoregression (SAR) order,
 ‘D’ represent Seasonal differencing order and
 ‘Q’ represent Seasonal Moving average (SMA)
ACF PACF
AR Geometric decay Significant till p lags
MA Significant till p lags Geometric decay

57. AR SIGNATURE57

58. MA SIGNATURE58

59. SAR AND SMA SIGNATURE59
SAR signature: Positive spikes in ACF at lag s, 2s,
3s and positive spike in PACF at lag s.
SMA signature: Negative spike in ACF at lag s, and
negative spikes in PACF at lags s, 2s, 3s.

60. PROCEDURE60
Analysis involves formation of ACF and PACF plots for four different cases
which are:
 Plots without any differencing.
 Plots with non-seasonal differencing.
 Plots with seasonal differencing.
 Plots with non-seasonal and seasonal differencing.

61. CONTD.61
For example:
 Case with Non-Seasonal and Seasonal Differencing of Karjan toll Car traffic
 Model – ARIMA(p,d,q)x(P,D,Q) = ARIMA(0,1,1)x(0,1,1)

62. SPSS 2362

63. KARJAN TOLL
63 Vehicle Model R2 RMSE MAPE MAE Normalized BIC
CAR
ARIMA (011 010)
Calibration .722 1598 8 1131 14.81
Validation 2386 8 1782
ARIMA (011 011)
Calibration .823 1283 7 949 14.44
Validation 2127 8 1728
LCV
ARIMA (011 011)
Calibration .717 223 5 176 10.94
Validation 97 2 97
ARIMA (011 000)
Calibration .767 248 6 199 11.08
Validation 504 10 454
BUS_TRUCK
ARIMA (100 010)
Calibration .507 329 5 238 11.65
Validation 799 13 761
ARIMA (011 011)
Calibration .627 289 4 208 11.46
Validation 718 12 699
MAV
ARIMA (011 000)
Calibration .360 514 5 419 12.54
Validation 1277 10 1131
ARIMA (011 011)
Calibration .328 502 4 384 12.56
Validation 1123 10 1049

64. CONTD.64

65. BORIACH TOLL
65
Vehicle Model R2 RMSE MAPE MAE Normalized BIC
CAR
ARIMA (011 011)
Calibration .867 1022 9 701 13.98
Validation 1600 13 1409
ARIMA (200 011)
Calibration .865 1039 9 706 14.07
Validation 1279 8 950
LCV
ARIMA (011 000)
Calibration .936 245 5 189 11.06
Validation 427 6 319
ARIMA (011 010)
Calibration .902 285 5 195 11.37
Validation 567 9 463
BUS_TRUCK
ARIMA (100 010)
Calibration .289 448 6 263 12.27
Validation 734 12 536
ARIMA (011 000)
Calibration .577 345 6 247 11.74
Validation 629 16 656
MAV
ARIMA (011 010)
Calibration .494 400 4 298 12.04
Validation 584 5 435
ARIMA (011 000)
Calibration .608 385 4 293 11.96
Validation 574 5 470

66. CONTD.66

67. BHAGWADA TOLL
67 Vehicle Model R2 RMSE MAPE MAE Normalized BIC
CAR
ARIMA (010 011)
Calibration .830 1126 11 693 14.11
Validation 1268 12 958
ARIMA (100 011)
Calibration .833 1124 11 701 14.17
Validation 1766 14 1142
LCV
ARIMA (011 000)
Calibration .917 334 6 246 11.67
Validation 516 8 408
ARIMA (200 011)
Calibration .891 346 6 238 11.87
Validation 968 17 866
BUS_TRUCK
ARIMA (011 011)
Calibration .682 246 4 193 11.13
Validation 530 9 431
ARIMA (200 011)
Calibration .694 244 4 187 11.18
Validation 469 8 370
MAV
ARIMA (011 000)
Calibration .619 410 5 328 12.08
Validation 523 6 432
ARIMA (011 010)
Calibration .472 462 5 353 12.33
Validation 1127 13 1009

68. CONTD.68

69. VOLUME TO CAPACITY ANALYSIS69
LOS THRESHOLD FOR 6-LANE DIVIDED INTERURBAN HIGHWAY SEGMENTS
LOS V/C PCU/day Threshold
A <0.2 <27000 34000 @ LOS-B: Suggested
threshold flow for conversion
from six lane to eight lane
divided road to ensure
enhanced safety in traffic
operations.
B 0.21-0.3 27001-41000
C 0.31-0.5 41001-68000
D 0.51-0.7 68001-95000
E 0.7-1 95001-136000
F >1 >136000
Indian Highway Capacity Manual (Indo-HCM)

70. KARJAN & CHORYASI TOLL70
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Volume to Capacity
Analysis
REmp RIn RegTV RegV
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Volume to Capacity
Analysis
REmp RIn RegTV RegV
0.0
0.5
1.0
1.5
2.0
Volume to Capacity Analysis
GDPIND GSDPGUJ
GSDPMH GDPIND - GSDPGUJ
GSDPGUJ - GSDPMH GDPIND - GSDPMH
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Volume to Capacity Analysis
GDPIND GSDPGUJ
GSDPMH GDPIND - GSDPGUJ
GSDPGUJ - GSDPMH GDPIND - GSDPMH

71. BORIACH & BHAGWADA TOLL71
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Volume to Capacity Analysis
REmp RIn RegTV RegV
0.0
0.5
1.0
1.5
2.0
Volume to Capacity Analysis
GDPIND GSDPGUJ
GSDPMH GDPIND - GSDPGUJ
GSDPGUJ - GSDPMH GDPIND - GSDPMH
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Volume to Capacity Analysis
REmp RIn RegTV RegV
0.0
0.5
1.0
1.5
2.0
Volume to Capacity Analysis
GDPIND GSDPGUJ
GSDPMH GDPIND - GSDPGUJ
GSDPGUJ - GSDPMH GDPIND - GSDPMH

72. TIME SERIES MODELS72
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
2018-19
2019-20
2020-21
2021-22
2022-23
2023-24
2024-25
2025-26
2026-27
2027-28
2028-29
2029-30
2030-31
2031-32
2032-33
2033-34
V/C
Year
Volume to Capacity Analysis
Karjan Boriach Bhagwada

73. 73
DIVERSION

74. Diversion Analysis based
on
Origin-Destination
Primary Survey Data
Survey Proportion Percentage
Actual Volume Proportion
Average Volume
Calculation of Utility value
Diversion Proportion (Logit Model)
Highway Traffic
MADT
for
NH48 Toll Plazas
MADT
for
VME sections
DIVERSION ANALYSIS74

75. Karjan Toll
Narmada Toll
Choryasi Toll
Boriach Toll
Bhagwada Toll
National
Highway
48
External
East
External
South
East
External
North
External
South
Vadodara
External
West
Bharuch Surat Navsari Valsad
1 2 3 4 5 6
EXISTING HIGHWAY (NH-48)75

76. MONTH
KARJAN
TOLL
NARMADA
TOLL
CHORYASI
TOLL
BORIACH
TOLL
BHAGWADA
TOLL
Sep-18 1,17,596 1,21,835 1,10,531 1,18,284 1,03,961
Oct-18 1,20,453 1,24,776 1,14,687 1,21,540 1,04,342
Nov-18 1,24,255 1,21,260 1,09,491 1,15,035 98,709
Dec-18 1,27,274 1,29,522 1,22,924 1,26,250 1,07,934
Jan-19 1,27,993 1,27,935 1,31,608
EXTERNAL
EAST
1,33,347
EXTERNAL
SOUTHEAST
1,09,985
Feb-19 1,31,942 1,33,302 1,47,885 1,40,237 1,20,986
EXTERNAL
NORTH
EXTERNAL
SOUTH
VADODARA
EXTERNAL
WEST
BHARUCH SURAT NAVSARI VALSAD
76

77. Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
80,000
90,000
1,00,000
1,10,000
1,20,000
1,30,000
1,40,000
1,50,000
Traffic Volume (AADT) variation over Study Stretch
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
TOLLS TRAFFIC VARIATION OVER
STUDY STRETCH
77

78. V/C FOR TRAFFIC AT TOLL PLAZAS OF
NH48 UPTO HORIZON YEAR 2035
78
0.0
0.5
1.0
1.5
2.0
2018 2023 2028 2033
V/C
Year
V/C Trend for NH-48
Karjan
Narmada
Choryasi
Boriach
Bhagwada
LOS B
LOS C
LOS F

79. National
Highway
48
External
East
External
SouthEast
External
North
External
South
Vadodara
External
West
Bharuch Surat Navsari Valsad
Vadodara
Mumbai
Expressway
External
East
External
SouthEast
External
North
External
South
Vadodara
External
West
Bharuch Surat Navsari Valsad
1 2 3 4 5 6
1 2 3 4 5 6 7
EXISTING ROUTE (NH-48) WITH
ALTERNATE FACILITY (VME)79

80. 80
Binary Logit model based on vehicle category.
P ΤR2 R1
k
=
eUR2
eUR1 + eUR2
Utility Functions derived from Logit Model.
Uk
R1 = α H_TTR1+ β H_costR1 + γ H_distR1 + C
Uk
R2 = α H_TTR2+ β H_costR2 + γ H_distR2 + C
ROUTE CHOICE MODEL
Where, Pk
(R2/R1) = Probability of shifting from route 1 to 2 for kth vehicle category.
Uk
R1 = Utility function of route 1 for kth vehicle category.
Uk
R2 = Utility function of route 2 for kth vehicle category.
H_costR1/ H_costR1 = Travel cost for route 1 and route 2.
H_TTR1 / H_TTR2 = Travel time for route 1 and route 2.
H_distR1 / H_distR2 = Distance for route 1 and route 2.
C = Constant for unexplained part.
α, β, γ = Parameters or coefficients of the variables in model.

81. DATA FORMATION81

82. 82

83. BINARY LOGIT MODEL83
Vehicle Utility Equation
-2 Log
Likelihood
Nagelkerke
R²
Percentage
Correct
Car Ucar = -0.016 H_TT+ 0.042 H_cost – 0.083 H_dist 316.153 .675 85.6
LCV ULCV = -0.012 H_TT + 0.042 H_cost – 0.182 H_dist 86.434 .803 90.3
Bus_Truck UBus_Truck = -0.013 H_TT + 0.011 H_cost – 0.057H_dist 367.997 .524 76.4
MAV UMAV= -0.019 H_TT + 0.009 H_cost – 0.072H_dist 682.999 .555 80.5

84. UTILITY EQUATION DATA84
Utility Modelling
• Using primary survey data for each vehicle category.
• Each data set was converted into 1-4 choice sets based on responses of
respondent for Willingness to pay section.
Logit Model inputs for Utility Calculation
Distance Based on Entry and Exit point of highway for each O-D pair
NH48 Distance between Entry & Exit point of NH48 alignment
VME Distance between Entry & Exit point of VME proposed alignment
Travel Time for each O-D pair
NH48 Travel time on existing route through primary survey data (50 percentile value)
VME Travel time through 10% increase in journey speed (Assumption)
Travel Cost for each O-D pair
NH48 Travel cost on existing route through toll collection
VME
Toll Cost through document with Regd. No. D.L. - 33004/99, The Gazette of
India: Extraordinary, Part II Section - 3, MORTH
Vehicle Car LCV Bus_Truck MAV
NH Speed (KM/hr) 73 64 59 55
VME Speed (KM/hr) 80 70 65 60
Toll Rate per KM 1.08 1.92 3.92 6.24
Toll Rate per Structure 5 7.5 15 22

85. DIVERSION PROPORTION85
O-D Pair Distance
Vehicle Category
Car LCV Bus_Truck MAV
External North External South 260 0.92 0.99 0.89 0.93
External North
External South
East
250 0.92 0.97 0.93 0.96
External North External East 161 0.76 0.85 0.73 0.77
External North Bharuch 91 0.61 0.58 0.63 0.65
External North Surat 124 0.6 0.71 0.5 0.49
External North Navsari 188 0.85 0.92 0.85 0.9
External North Valsad 250 0.11 0.01 0.21 0.13
Vadodara Vadodara 0 0 0 0 0
Vadodara Bharuch 91 0.61 0.58 0.63 0.65
Vadodara Surat 124 0.6 0.71 0.5 0.49
Vadodara Navsari 188 0.23 0.07 0.29 0.22
Vadodara Valsad 250 0.11 0.01 0.21 0.13
External West External South 169 0.87 0.98 0.82 0.87
External West
External South
East
159 0.88 0.96 0.88 0.93
External West External East 70 0.66 0.8 0.61 0.65

86. CONTD.86
O-D Pair Distance
Vehicle Category
Car LCV Bus_Truck MAV
External West Surat 33 0.48 0.63 0.37 0.34
External West Navsari 97 0.16 0.05 0.19 0.13
External West Valsad 159 0.07 0.01 0.14 0.07
Bharuch Bharuch 0 0 0 0 0
Bharuch Surat 33 0.48 0.63 0.37 0.34
Bharuch Navsari 97 0.16 0.05 0.19 0.13
Bharuch Valsad 159 0.07 0.01 0.14 0.07
Surat Surat 0 0 0 0 0
Surat Navsari 64 0.17 0.03 0.29 0.22
Surat Valsad 126 0.08 0 0.21 0.13
External East External South 99 0.77 0.92 0.75 0.78
External East
External South
East
89 0.79 0.86 0.82 0.87
External East Vadodara 161 0.76 0.85 0.73 0.77
External East Bharuch 70 0.66 0.8 0.61 0.65
External East Navsari 27 0.09 0.01 0.13 0.07

87. CONTD.87
O-D Pair Distance
Vehicle Category
Car LCV Bus_Truck MAV
External East Valsad 89 0.04 0 0.09 0.04
Navsari Navsari 0 0 0 0 0
Navsari Valsad 62 0.02 0 0.04 0.02
External South East External South 10 0.47 0.66 0.39 0.35
External South East Vadodara 250 0.92 0.97 0.93 0.96
External South East Bharuch 159 0.88 0.96 0.88 0.93
External South East Surat 126 0.21 0.03 0.33 0.27
External South East Navsari 62 0.67 0.76 0.68 0.72
External South East Valsad 0 0 0 0 0
Valsad Valsad 0 0 0 0 0
External South Vadodara 260 0.92 0.99 0.89 0.93
External South Bharuch 169 0.87 0.98 0.82 0.87
External South Surat 136 0.2 0.06 0.24 0.16
External South Navsari 72 0.66 0.85 0.58 0.58
External South Valsad 10 0.74 0.95 0.6 0.62

88. LOS THRESHOLDS88
LOS Thresholds for Six Lane Divided
Interurban Expressway Segments
LOS V/C PCU/day Threshold
A <0.25 <39800
58200 @ LOS-B:
Suggested
threshold flow for
conversion from
six lane to eight
lane divided road
to ensure
enhanced safety in
traffic operations.
B 0.26-0.5
39801-
76500
C 0.51-0.75
76501-
114800
D 0.76-0.93
114801-
142300
E 0.94-1
142301-
153000
F >1 >153000
LOS Thresholds for Eight Lane Divided
Urban Expressway Segments
LOS V/C PCU/day Threshold
A <0.25 <47600
69600 @ LOS-B:
Suggested
threshold flow for
conversion from
six lane to eight
lane divided road
to ensure
enhanced safety in
traffic operations.
B 0.26-0.5
47601-
91500
C
0.51-
0.75
91501-
137300
D
0.76-
0.93
137301-
170200
E 0.94-1
170201-
183000
F >1 >183000

89. VOLUME TO CAPACITY ANALYSIS89
0.0
0.5
1.0
1.5
2.0
2018 2023 2028 2033
V/C
Year
V/C Trend for NH-48
Karjan
Narmada
Choryasi
Boriach
Bhagwada
LOS B
LOS C
LOS F

90. CONTD.90
0.0
0.5
1.0
1.5
2.0
2018 2022 2026 2030 2034
V/C
Year
V/C Trend for 6-Lane VME
1
2
3
4
5
6
7
LOS B
LOS C
LOS F
0.0
0.5
1.0
1.5
2.0
2018 2022 2026 2030 2034
V/C
Year
V/C Trend for 8-Lane VME
1
2
3
4
5
6
7
LOS B
LOS C
LOS F

91. RESULTS AND DISCUSSIONS
 The employment multiplier for region is 1.86. i.e. E = 1.86 EB.. Employment
multiplier of our districts in study regions i.e. Vadodara, Bharuch, Surat,
Navsari and Valsad are 1.54, 1.53, 2.42, 1.73 and 1.75 respectively with
1.8 value for Gujarat State.
 The actual growth rate of investment and employment in region are 29%
and 21% respectively.
 The traffic of NH-48 is forecasted using aggregate and disaggregate
models.
 Aggregate models are developed with annual economic data
that are
 Employment and investment data of region,
Employments models are performing better
91

92. CONTD.
 Vehicle registration data of Gujarat, and
Vehlice registration data is able to explain traffic well
 GDPIND, GSDPGUJ and GSDPMH data.
GSDPGUJ model is performing well
 Disaggregate models are developed using
 Regression Analysis
Bus_Truck and MAV models are not able to explain data
 Moving Average with Classical Decomposition
Bus_Truck and Car models have high MAE and MAP values
 ARIMA Models
Most of the models have seasonal Moving average component after
seasonal decomposition.
92

93. CONTD.
 V/C Analysis
 RegV, RegTV and GSDPGUJ models are showing smooth growth trend
while others are shooting up after 2019.
 The highway sections have already reached capacity and the traffic is
except to grow twice of capacity by 2035.
 Diversion Analysis
 The diversion proportion through logit model show that mostly E-E
(through) traffic, I-E and E-I traffic will shift to proposed facility (VME).
 The V/C analysis show that NH-48 and VME will operate in LOS ‘C’,
considering VME as 6-lane divided while VME can also be in LOS ‘B’
for 8-lane divided.
93

94. FUTURE SCOPE OF WORK
 Change in land use pattern which is responsible for
development traffic for NH during the horizon year is likely to
be considered.
 Freight demand modeling is to be included to calculate the
growth rate of freight vehicles for exclusive freight corridors.
94

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98. ACKNOWLEDGEMENT98

99. THANK YOU