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National Conference on Recent Trends in Engineering & Technology
Transportation Planning Models: A Review
Kevin B. Modi Dr. L. B. Zala Dr. F. S. Umrigar Dr. T. A. Desai
M.Tech (C) TSE student, Associate Professor, Principal, Professor and Head of
Civil Engg. Department, Civil Engg. Department, B. V. M. Engg. College, Mathematics Department,
B. V. M. Engg. College, B. V. M. Engg. College, Vallabh Vidyamagar, India B. V. M. Engg. College,
Vallabh Vidyamagar, India Vallabh Vidyamagar, India bvm_princi@yahoo.com Vallabh Vidyamagar, India
kevin_modi@yahoo.co.in lbzala@yahoo.co.in tadesaibvm@gmail.com
Abstract- The main objective of this paper is to present an the form of flows on each link of the horizon-year networks as
overview of the travel demand modelling for transportation recorded by Pangotra, P. and Sharma, S. (2006), “Modelling
planning. Mainly there are four stages model that is trip Travel Demand in a Metropolitan City”. In the present study,
generation, trip distribution, modal split and trip assignment. Modelling is an important part of any large scale decision
The choice of routes in the development of transportation making process in any system. Travel demand modelling aims
planning depends upon certain parameters like journey time, to establish the spatial distribution of travel explicitly by
distance, cost, comfort, and safety. The scope of study includes means of an appropriate system of zones. Modelling of
the literature review and logical arrangement of various models demand thus implies a procedure for predicting what travel
used in Urban Transportation Planning. decisions people would like to make given the generalized
Keywords- transportation planning; trip generation;trip travel cost of each alternatives. This paper presents review of
distribution;modal split; traffic assignment; transportation various Transportation Planning Models.
planning parameters (journey time, distance, cost, comfort, and
safety); logical arrangement. II. TRAVEL DEMAND MODELLING
I. INTRODUCTION The process of travel demand forecasting essentially consists
According to Chattaraj, Ujjal (2003), Transportation is the of four stage model (see figure 1). The process has been
backbone to the development of urban areas. It enables documented by Kadyali L. R. (2007), Mathew T. V. and K. V.
functioning of urban areas efficiently by providing access and K. Rao (2007), Kadyali L. R. and Lal N. B. (2008-09) and
mobility. Passenger transport has an overriding influence on others. In the subsequent paragraphs the four stage modelling
the functioning of the city. Transportation planning and has been elaborated.
development of infrastructure for the system is one of the most Data Base
crucial factors particularly for urban areas, where in high level Network data, zones Base-year data Future planning data
and rapid urbanization is taking place. The demand for
transportation in urban area is linked to the residential location
choices that people make in relation to places of work, Land use forecasting
shopping, entertainment, schools and other important
activities. As cities grow, they support more people and more Trip generation
dispersed settlement patterns. Increasing demand for
transportation is an inevitable outcome of urban growth. As
Patel N. A. (2008), a universal trend that has been observed is Modal split
that as household incomes grow, people prefer personal
transportation to public transport. The obvious and compelling
reason for this is that personal transport maximizes individual Public transport Private transport
mobility, freedom of choice and versatility that public trip distribution trip distribution
transport systems cannot match. However, the experience of
cities in many developed and developing countries shows that
an efficient and economic public transport system can reduce
dependence on personal transportation. Transportation Trip assignment
planning process involves prediction of most probable pattern
of land development for the horizon-year, usually taken as Traffic flows by link
twenty years, and the transport demands created by that land-
use are estimated. A set of alternative transport plans is then Figure 1. General form of the four stage transportation modeling
generated for that horizon-year. The operating characteristics A. Trip Generation
of each alternative in the horizon-year are then estimated in
13-14 May 2011 B.V.M. Engineering College, V.V.Nagar,Gujarat,India
National Conference on Recent Trends in Engineering & Technology
Trip generation is the first stage of the classical first B. Trip Distribution
generation aggregate demand models. Trip generation is the The decision to travel for a given purpose is called trip
analysis and model building phase starts with the first step generation. The decision to choose destination from origin is
commonly. It is a general term used in the transportation directional distribution of trips forms the second stage of
planning process to cover the number of trip ends in given travel demand modeling. Trip distribution is determined by the
area. Trip generation is classified in production and attraction. number of trips end originated in zone-i to number of trips end
Production (origin) means number of trips end originated attracted to zone-j, which can be understood by the matrix
in zone-i. Attraction (destination) means number of trips end between zones. The matrix is called origin - destination (O-D)
attracted to zone-j. There are basically two tools for trip matrix. Table I represents typical O-D matrix.
generation analysis, multiple linear regressions and category
analysis (cross classification), and these methods are TABLE I. NOTATIONOFATRIP DISTRIBUTIONMATRIX
explained in the following sections. Zones 1 2 3 … j … n O
i
1 T T T … T … T O
11 12 13 1j 1n 1
2 T T T … T … T O
21 22 23 2j 2n 2
3 T T T … T … T O
31 32 33 3j 3n 3
: … … … … … … … :
T T T … T … T O
i1 i2 i3 ij in i
: … … … … … … … :
n T T T … T … T O
ni n2 n3 nj nn n
D D D D … D … D T
j 1 2 3 j n
Where, D = ∑T O = ∑T T = ∑ T
j i ij j j ij ij ij
a. Mathew, T. V., Krishna Rao, K. V. (2007), “Trip Distribution”.
1) Trip Distribution Models: The various trip generation
Figure 2. Types of trips models are listed below classified as a Growth factor models,
1) Regression Methods: The trip generation models are Synthetic models, and opportunity models.
generally developed using regression analysis approach and a a) Growth factor models
zonal trips prediction equation is developed. Typically the i) Uniform factor model
functional form will be a multiple linear regression model is: ii) Average factor model
iii) Fratar model
iv) Detroit model
y = a + (a x ) + (a x ) + (a x ) + ……… + (a x ) + e (1) v) Doubly constrained growth factor model (Furness
0 1 1 2 2 3 3 n n model)
A simple one variable model is represented as: b) Synthetic models / Interaction models
i) Gravity model
c) Opportunity models
y = a + (a x ) + e (2) i) Intervening opportunity model
0 1 1
ii) Competing opportunity model
Where, y = dependent variable a) Growth Factor Models: The growth factors are based
x = independent variable (i = 1, 2, 3……n)
i on the assumption that the present travel pattern can be
a0 = constant term projected to the design year in the future by using certain
a = coefficient of independent variables (i = 1, 2,
i expansion factor. The growth factor methods are used in the
3……n) urban planning for approximation.
e = error term i) Uniform Growth Factor Model: The uniform growth
n = number of independent variables factor method is only a crude method, because there is
2) Category Analysis: The category analysis is also called differential growth in different zones. If the only information
cross classification analysis, which developed by Wottom and available is about a general growth rate for the whole of the
Pick. This technique is widely used for to determine the study area, then we can only assume that it will apply to each
number of trips generated. The approach is based on a control cell in the matrix, which is called uniform growth rate.
of total trips at the home end. The amount of home-end travel
generated is a function of number of households, the T = F * t (3)
characteristics of households, the income level, and car ij ij
ownership. The density of households could also be Where, T = future number of trips from zone-i to zone-j (the
considered. At the non-home end, a distribution index is ij
developed based on land use characteristics, such as the expanded total number of trips)
t = present number of trips from zone-I to zone-j (the
number of employees by employment category, land use type, ij
and school enrollment. previous total number of trips)
F = the uniform growth factor
13-14 May 2011 B.V.M. Engineering College, V.V.Nagar,Gujarat,India
National Conference on Recent Trends in Engineering & Technology
Growth Factor (F) All the future trip in the study area The distribution of future trips from a given origin is
All the present trip in the study area proportional to the present trip distribution.
n n This future distribution is modified by the growth
Tij (4) factor of the zone to which these trips are attached.
F i j iv) Detroit Model: The Detroit model is used for trip
n n
tij distribution in Detroit area of USA future trips between zone-I
i j and zone-j.
Advantages
They are simple to understand. F i * F j
T ij tij * (11)
F
They are useful for short-term planning.
Limitations Where, F = growth factor of entire area
The same growth factor is assumed for all zones as well v) Doubly Constrained Growth Factor Model: When
as attractions. information is available on the growth in the number of trips
ii) Average Growth Factor Model: The average growth originating and terminating in each zone, we know that there
factor model is calculated for the both ends of the trip (O-D will be different growth rates for trips in and out of each zone
zones). and consequently having two sets of growth factors for each
zone. This implies that there are two constraints for that model
Fi F j (5) and such a model is called doubly constrained growth factor
Tij tij *
2 model. This model is also called Furness model. In this model,
the production from zones is balanced and then the attraction
Where, T = future number of trips from zone-i to zone-j (the
ij to the zones is balanced. One of the methods of solving such a
expanded total number of trips) model is given by Furness who introduced balancing factors r
t = present based year number of trips from zone-i to i
ij and s as follows:
zone-j (the previous total number of trips) j
F = producted growth factor for zone-i
i T = t * r * s (12)
ij ij i j
F = P / p (6)
i i i Where, r = row balancing factor
i
s = column balancing factor
Where, P = future producted number of trips for zone-i j
i Limitations
p = present producted number of trips for zone-j
i
F = attracted growth factor for zone-j No consideration of spatial separation only growth is
j
given.
F = A / a (7) Travel behavior is not incorporated.
j j j
Where, A = future attracted number of trips for zone-i b) Synthetic Models / Interaction Models: The gravity
j model is included in this category.
a = present attracted number of trips for zone-j
j i) Gravity Model: This model originally generated from
iii) Fratar Model: The Fratar model is introduced by T. an analogy with Newton's gravitational law, which states that
J. Fratar (1954) and Fratar model of successive approximation the attractive force between any two bodies is directly related
is widely used for to distribute trips in a study area. This to their masses and inversely related to the distance between
model has been used extensively in several metropolitan study them. Similarly, in the gravity model, the number of trips
areas, particularly for estimating external trips coming from between two zones is directly related to activities in the two
outside the study areas to zones located within the study area. zones, and inversely related to the separation between the
Li L j (8) zones as a function of the travel time.
Tij tij * Fi * F j *
2
T = K * O * D * F(d ) (13)
Where, L = location factor for zone-i ij ij i j ij
i
tij Where, T = Future number of trips from zone-i to zone-j (the
L j (9) ij
i t * F expanded total number of trips)
ij j
j K = constant value (initial value = 1)
L = location factor for zone-j ij
j
t K = r * s (14)
ij (10) ij i j
L i
j t * F
ij i Where, r = row balancing factor
i i
Assumptions r O i O i
i t R (15)
ij i
j
13-14 May 2011 B.V.M. Engineering College, V.V.Nagar,Gujarat,India
National Conference on Recent Trends in Engineering & Technology
Where, O = total number of trips end originated in zone-i A = total number of destination from origin zone-i
i x
s = column balancing factor within the time bond containing the zone of
j
D D destination.
s j j (16)
j t C C. Modal Split
ij j
i The third stage in travel demand modeling is modal split.
Where, D = total number of trips end destinated to zone-j
j Modal split is determined by number of trips of people process
t = Present based year number of trips from zone-i to
ij by the different mode of travel. In other words, modal split sub
zone-j (the previous total number of trips) model of travel demand modelling is used to distribute the
s = t = r * R (17) total travel demand in two or more mode categories. These
j ij i j categories are public transport riders and personal / private
Where, F(d ) = the generalized function of the travel cost, vehicle riders. The demand can further be split into different
ij modes. According to Tom V. Mathew (2007), N. A. Patel
which is called deterrence function because it (2008), the socio-economic demand variables used to explain
represents the disincentive to travel as distance (time) mode choice behavior are income, vehicle ownership,
or cost increases. household size, residence location etc. The supply variables
F(d ) = d -b (18) are in vehicle time, waiting time, travel time, travel cost,
ij ij transfer time etc.
Where, b value depends on the trip purpose. 1) Modal Split Methods: The probit method and logit
method are included in this category.
TABLE II. VALUEOF‘B’ASPERTRIPPURPOSE a) Probit Method: The determination of the co-efficient
Trip Purpose ‘b’ Value of the supply and demand is done by calibration procedures,
Work 0.5 - 2.0 which are lengthy and time consuming.
Shopping 1.5 - 2.0 The probit equation can be written as:
Recreational 2.0 - 2.5
Other Purpose 2.0 - 2.5
y = a + (a x ) + (a x ) + (a x ) + ……… + (a x ) (22)
a. Kadyali, L. R. and Lal, N. B. (2007), “Traffic Engineering and Transport 0 1 1 2 2 3 3 n n
Planning”, Khanna Publishers, Delhi-6.
Where, y = probit value for the probability of transit mode
c) Opportunity Models: The intervening opportunity choice
model and competing opportunity model are included in this x = supply and demand vector
category. n
an = associated parameters
i) Intervening Opportunity Model: According to b) Logit Methods:
Stouffer (1943), number of trips from one origination in zone-i
to a destination to zone-j is directly proportional to the number P 1
of opportunities at the destination zone and inversely 1 1 eG(x) (23)
proportional to number of intervening opportunities. Where, P = probability of an individual choosing mode-1
1
t K a j (19) 1-P1 = probability of an individual choosing mode-2
ij v
j
Where, a = total number of opportunities in zone - j G(x) = α (c -c ) + α (t -t ) + ….. (24)
j 1 1 2 2 1 2
v = the number of intervening destination
j
opportunities between zone-i and zone-j Where, c and c = travel cost by mode 1 and mode 2
K = constant of proportionality 1 2
t and t = travel time by mode 1 and mode 2
1 2
According to Schneider (1963), α , α …. α = model parameters
1 2 n
T = O [P(acceptance in volume including zone-j) – The binary logit method and multinomial method are included
ij i r in this category.
p(acceptance in volume immediately prior to zone-j)]
r
LV LV i) Binary Logit Method: Binary logit model is the
T O [e j 1 e j ] (20) simplest form of mode choice, where the travel choice
ij i between two modes is made. The traveler will associate some
value for the utility of each mode. If the utility of one mode is
ii) Competing Opportunity Model: higher than the other, then that mode is chosen. But in
A transportation, we have disutility also. The disutility here is
j A the travel cost.
t O x (21)
ij i A A ii) Multinomial Method: Multinomial logit model is a
j x
i function of the system characteristics and user characteristics.
Where, A = number of destination opportunities in zone-j.
j
13-14 May 2011 B.V.M. Engineering College, V.V.Nagar,Gujarat,India
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