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Trip attraction rates of shopping centers in dhanmondi area of dhaka city final
1. CHAPTER ONE
INTRODUCTION
1.1 Background
These days, the transportation planning issues faced by most Asian cities include rapid
urbanization and motorization which is leading to sharp increase in travel demand whereas, the
supply has largely remained unmatched with demand. So, trip generation and trip attraction is
important to the traffic engineer and planner in considering the impact of new development such as
office complex, shopping centre and residential development. New development leads a
various impact to the people‟s daily activities. For example the impacts of surrounding
roadway network tend to make people moving far from one place to another place. Road length
is increasing and road network patterns change according to the accessibility needs of people
and desire to reach their destinations. Hence, new development will increase the travel demand
with there also increase the vehicles.
Trip attraction is obviously most pertinent relative to traffic at specific land use activity. It also
plays a role in many phases of transportation planning and traffic engineering related activities. It is
the part of trip generation in the travel-forecasting process. It involves the estimation of the total
number of trips entering a parcel of land as a function of the socioeconomic, location, and land-
use characteristics of the parcel. In the reliable sector, urban transportation covers the
movement of both people and goods within an urban area. At the individual level, urban
transportation can be characterized by a trip. However, at the metropolitan area level, millions of
these individual trips define urban transportation.
A trip as a journey made by an individual between two different points. Each trip is performed
using one or multiple transportation modes for a defined purpose at a given time. Although a
trip may involve more than one purpose, it is usually identified by its principal purpose (Hobbs,
1979). Trip generation analysis, as Meyer (1974) puts it, seeks to estimate the volume of trips
that will be made by individuals to work, shopping, school, and so forth, but not the flows
between points within the whole system. The functioning of metropolitan cities is highly
dependent on the movement of people, goods and information (Muller, 1995) and trip attraction
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2. studies are a vital part of transportation planning, due to the recursive nature of urban
transportation modeling procedure (Bruton, 1986; Badoe and Steuart, 1997).
Personal trips are commonly classified based on their main purpose (Barber, 1995); work trips,
shopping trips, social trips, recreational trips, school trips, home trips and business trips. Among
all trip purposes, work trips are the most numerous, followed by shopping trips (Vickerman
and Barmby, 1984), which count approximately for 40% of all trips generated in North
American metropolitan areas (Barber, 1995). A very few studies have been done in about
shopping trip attraction in Dhaka city. This study focuses on shopping trip attraction in
Dhaka city area specially in Dhanmondi area.
1.2 Problem Statement
The Trip Attractions Rate of a shopping center (Shopping Center) is influenced by a number of
factors, including time of the day, day of the week, seasonality, weather, configuration and
composition of the Shopping Center. Peaking is caused by business and social characteristics. The
most typical time for shopping during the weekday is after work, in particular, 4 to 7 PM on
Fridays attracts the most number of customers on a weekday. In addition Saturdays and
Sundays are very busy periods for Shopping Centers having a supermarket and discount stores.
In general, there is a large variation in the number of people arriving at the Shopping Center
Shopping Center even during the same time period over different fifteen-minute intervals. This
variation is more clearly shown in Table 3.7 to table of Chapter 3 where in the Trip Attraction
(TA) of a Shopping Center for two different days during the same time period are shown. As
a result, the sample size becomes very important particularly when significant fluctuations
exist in the number of trips to the Shopping Center. The need for a large sample space and the
highly inconsistent nature of the Trip Attraction makes the estimation of Trip Attractions Rate of
the Shopping Center a very complex process.
The ITE handbook has been the main reference material in the transportation planning
community when estimating Trip Attraction of an activity center. Although the ITE Trip
Generation Manual (1997) is a concise and easy to use reference, the models for Shopping
Center do not consider some of the features of Shopping Center Shopping Center, such as the
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3. number of stores, the number of the parking spaces, and the location of the Shopping Centers
that can have significant influence on the Trip Attraction Rate of the Shopping Centers. As a
result, the Trip Attraction Rate estimated cannot be made specific to a Shopping Center. On the
other hands, The ITE Trip Generation Manual (1997) offers the Trip Attraction Rate for
many different types of establishment or stores, when the establishment is freestanding. In other
words, there is no differentiation between the stores independently located or located in a
Shopping Center along with other stores. The phenomenon of trip chaining, in which a customer
visits more than one store during one trip to a Shopping Center, has to be taken into account for
the estimation of the TA in a Shopping Center.
It is difficult to consider all the factors influencing the Trip Attraction Rate of Shopping Center
especially factors like land use characteristics of the surrounding area. However other factors like
the physical features of the Shopping Center that are easy to measure and analyze should be
incorporated in the estimation of the Trip Attraction Rate. In addition to this the general
procedures for estimating Trip Attraction Rate of the Shopping Center do not consider the
effect of the type and features of the constituent stores of a Shopping Center. The TA of the
constituent stores in a Shopping Center affects the level of trip chaining in the Shopping Center. It
is very vital to involve the trip-chaining phenomenon in the estimation of Trip Attraction Rate of
the Shopping Center. The above-mentioned points form the basis for undertaking this study.
1.3 Aim and Objectives
The aim of this study is to determine the trip attractions of shopping centers in the Dhanmondi
area of Dhaka city. Through the trip attraction analysis, we can determine the attraction of the
shopping trip among the shopping center in Dhaka city. Then, the travel demand can be estimated
from the analysis. Thus, to achieve the aim, there are several objectives of the study listed:
a. To determine Trip Attraction Rate of shopping centers in Dhanmondi
area of Dhaka city.
b. To show the trip attraction pattern and variation during peak hour o the
shopping centers.
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4. 1.4 Study outcome
This research is intended to provide empirical trip attraction data for use in transportation
planning and traffic engineering studies for urban areas throughout Dhaka city. This study also
provides the foundation for subsequent research to be conducted, local agencies, and/or
private organizations to further build a comprehensive urban trip attraction database of shopping
centers.
The most applicable outcome of this study is the production of quantitative information on
travel characteristics of urban land uses like shopping centers that can be used in traffic impact
studies. This research is intended to establish a standardized data collection and analysis
methodology, which will result in consistent information gathering in the future
1.5 Report Organization
The subsequent chapters of this report are organized as follows
Chapter 2 - Defines trip attraction, discusses current trip attraction usage, and presents sources of
trip attraction data and relevant trip attraction research.
Chapter 3 - contains concept of trip rate analysis method and study data. Data collection and
survey are explained in detail. Data of trip attraction are gathered to be analyzed. Discusses
the different data collection methods considered for this study and their challenges. This
chapter provides an overview of the sites surveyed in the “initial pilot” study (used to test the
chosen survey methodology), and presents an evaluation of the study sites and their
surrounding context
Chapter 4 the empirical results are presented and analyzed. Trip attraction for the
Dhanmondi area is presented in a form of tables and figures. The Trip Attraction Rate (Trip
Attraction Rate) using Trip Rate Analysis Method is represented and applied.
Chapter 5 - Discusses the findings of the surveyed sites in brief.
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5. CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The transportation planning process relies on travel demand forecasting. Trip generation has
been identified as the first and most important step of the conventional sequential
forecasting procedure (Soslau et al., 1978). Trip generation is calculated by trip production and
trip attraction. Even with the exploration of new generation travel demand models such as
activity-based models, the traditional four-step procedure remains the most widely used
model by transportation planning agencies because of institutional and financial requirements
(McNally, 2000). So, it is important to this study to understand the fundamental of trip
generation and trip attraction in order to determine the shopping trips attraction in Dhaka city. In
addition to that overview of shopping system in Bangladesh generally can picture the situation in
the Dhaka city. Furthermore, concepts of the trip attraction modeling and trip attraction
analysis have to be understood before modeling the shopping trip attraction for Dhaka city.
In the early section, this chapter discussed about the trip generation as a first phase in the travel
demand forecasting model. The general form of the model is depicted in Figure 2.1. There
are four basic phases in the traditional travel demand forecasting process which is trip
generation, trip distribution, modal choice and trip assignment. The last section discussed the
previous study regarding trip generation in Provo, Utah and Texas.
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7. Trip generation is the process by which measures of urban activity are translated into
numbers of trips. For example, the number of trips that are generated by a shopping center is
quite different from the number of trips generated by an industrial complex that takes up
about the same amount of space. In trip generation, the transport engineers and planners
attempt to quantify the relationship between urban activity and travel. The inventory data
discussed earlier is the analyst's input for trip generation analysis. Surveys of travelers in the
study area show the numbers and types of trips made by relating these trips to land use patterns,
the analyst is able to forecast the number of trips that will be made in the future, given
forecasts of population and other urban activity.
After trip generation, the analyst knows the numbers of trip productions and trip attractions each
zone. Trip distribution procedures determine where the trips produced in each zone will go and
how they will be divided among all other zones in the study area. The output is a set of tables
that show the travel flow between each pair of zones. The decision on where to go is represented
by comparing the relative attractiveness and accessibility of all zones in the area. A person is
more likely to travel to a nearby zone with a high level of activity than to a distant zone with a
low level of activity. There are several types of trip distribution analyses which are the
Fratar method, the intervening, opportunity model, and the gravity model.
In modal choice of travel demand forecasting, the phase analyze people's decisions regarding
mode of travel (auto, bus, train, etc). In the travel demand forecasting process, mode usage
comes after trip distribution. However, mode usage analyses can be done at various points in
the forecasting process. Mode usage analyses are also commonly done within trip generation
analyses. The most common point is after trip distribution, since the information on where trips
are going allows the mode usage relationship to compare the alternative transportation
services competing for users. Trip assignment is the procedure by which the planner predicts the
paths the trips will take. For example, if a trip goes from a suburb to downtown, the model
predicts which specific roads or transit routes are used. The trip assignment process
begins by constructing a map representing the vehicle and transit network in. the study area. The
network maps show the possible paths that trips can take.
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8. The rationale of trip generation modeling is to determine the number of vehicle or person
trips to and from zones under consideration. Trip generation modeling consists of two types
of models which is trip-production and trip attraction. Trip production is defined as the home end
of home-based (HB) trips or as the origin of a non home-based (NHB) trip while trip attraction is
defined as the non home end of a HB trip or the destination of a NHB trip. A trip is often
defined as a single journey made by an individual between two points by a specified or combined
modes of travel and for a defined purpose. Thus, trip generation analysis is the key to
obtaining future trip ends by zones. The basic procedure is first, to relate survey-reported trip
making to household characteristics and land use types by zone through regression or factor
analysis using single variable or multi-variable approaches. The equation thus derived may then
be applied to forecast land use data.
A trip is a one-way person movement by a mechanized mode of transport, having two trip
ends, an origin (the start of the trip) and a destination (the end of the trip). Trips are usually
divided into home-based and non-home-based. Home-based trips are those having one end of the
trip (either origin or destination) at the home of the persons making the trip, while non-home-
based trips are those having neither end at the home of the person making the trip. Briefly, it
can be summarized as for a home-based trip; the zone of production is the home end of the trip;
while the zone of attraction is the non home end of the trip. Thus, a trip from home to work and
a trip from work to home will both have a production end which is home and an attraction end
which is work. For non home-based trips, the production end is the origin and the attraction end
is the destination.
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9. Figure 2.2: The Relationship between O/D and Production and Attraction
Once the study area has been broken into zones, the next task involves quantifying the
number of trips that each zone will produce or attract. The number of trips to and from an area
or zone is related to the land use activities of the zone and the socioeconomic characteristics
of the trip makers. There are at least three characteristics of land use and trip-makers that
are important. The density or intensity of the land use is important. Many studies begin by
determining the number of dwelling, employees, or tenants per acre. The intensity can be
related to an average number of trips per day, based on experience with the type of land use
at hand. Next, the social and economic character of the users can influence the number of trips
that are expected. Character attributes like average family income, education, and car
ownership influence the number of trips that will be produced by a zone. Finally, location plays
an important role in trip production and attraction. Street congestion, parking, and other
environmental attributes can increase or decrease the number of trips that an area produces or
attracts.
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10. 2.1.1 Trip Production and Trip Attraction
A trip is a movement of a person from one place (origin) to another (destination). Trip production
represents a trip starting or ending in a residential area, since a trip is considered “produced” at a
person‟s residence. Trip attraction (TA) represents a trip starting or ending in a non-residential area.
Figure 2.3 shows how a person traveling from residence to an activity center generates two trip
productions and two trip attractions.
Figure 2.3 Trip productions and trip attractions
A trip-end is the point at which a given trip starts or terminates; one trip has two trip ends. The
TA or the trip production “rate” is defined as the number of trip ends per unit time per unit of
independent variables (per employee, per square feet of floor area, etc.). Most typically, however, it
refers to the number of trips per day per activity center.
2.1.2. Types of trips
Some basic definitions are appropriate before we address the classification of trips in detail. We
will attempt to clarify the meaning of journey, home based trip, and non home based trip, trip
production, trip attraction and trip generation. Journey is an out way movement from a point of
origin to a point of destination, where as the word trip denotes an outward and return journey. If
either origin or destination of a trip is the home of the trip maker then such trips are called home
based trips and the rest of the trips are called non home based trips. Trip production is defined as
all the trips of home based or as the origin of the non home based trips
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11. Figure 2.4: Trip Types
Trips can be classified by trip purpose, trip time of the day, and by person type. Trip (attraction and
production) models are found to be accurate if separate models are used based on trip purpose. The
trips can be classified based on the purpose of the journey as trips for work, trips for education,
trips for shopping, trips for recreation and other trips. Among these the work and education
trips are often referred as mandatory trips and the rest as discretionary trips. All the above trips are
normally home based trips and constitute about 80 to 85 percent of trips. The rest of the trips
namely non home based trips, being a small proportion are not normally treated separately. The
second way of classification is based on the time of the day when the trips are made. The
broad classification is into peak trips and of peak trips. The third way of classification
is based on the type of the individual who makes the trips. This is important since the travel
behavior is highly influenced by the socio economic attribute of the traveler and are
normally categorized based on the income level, vehicle ownership and house hold size.
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12. 2.1.3 Factors Affecting Trip attraction
The personal trip attraction is influenced by factors such as roofed space available for industrial,
commercial and other services. At the zonal level zonal employment and accessibility are also
used. In trip attraction modeling in addition to personal trips, freight trip share also of interest.
Although the latter comprises about 20 percent of trips, their contribution to the congestion is
significant. Freight trips are influenced by number of employees, number of sales and area of
commercial firms.
2.1.4 Shopping Trip Attractions
2.1.4.1 Shopping mall employee and shop number
Shopping mall employee number is a factor for calculating trip attraction rate of shopping mall.
Total number of shops is also calculated for developing trip attraction rate for shopping malls. These
are done by surveys.
2.1.4.2 Gross floor area of shopping mall
Area of shopping mall is a factor readily available from maps. Shopping mall gross floor area is a
more reliable indicator of shopping trip attraction which is calculated by multiplying the one
floor area and total number of floor,
2.1.4.3 Number of parking spaces
Number of parking spaces available for car parking is another important factor for estimating Trip
Attraction Rate .
2.1.4.4 Traffic Analysis Zone
The location of shopping mall also effects the trip attraction of individual Shopping Center. A
traffic analysis zone is the unit of geography most commonly used in conventional
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13. transportation planning models. The size of a zone varies, but for typical metropolitan
planning software, a zone of under 3000 people is common. The spatial extent of zones
typically varies in models, ranging from very large areas in the exurb to as small as city blocks or
buildings in central business districts. There is no technical reason why zones cannot be as
small as single buildings, however additional zones add to the computational burden.
Figure 2.5: Example of TAZ Boundaries
Zones are constructed by census block information. Typically these blocks are used in
transportation models by providing socio-economic data. States differ in the socio-economic
data that they attribute to the zones. Most often the critical information is the number of
automobiles per household, household income, and employment within these zones. This
information helps to further the understanding of trips that are produced and attracted within
the zone. Again these zones can change or be altered as mentioned in the first paragraph.
This is done typically to eliminate unneeded area to limit the "computational burden."
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14. 2.2 Trip Attraction Analysis
There are several general alternative structures for specifying Trip Attraction, which are:
i. Cross-Classification
ii. Regression Analysis
iii. Trip Rate Analysis Method
2.2.1Cross-Classification
The Cross-Classification Analysis has become most widely accepted. This procedure provides
the planner or the highway engineer a basic model structure. This structure can be altered for
local situations by substituting or adding variables. There are separate recommended model
structures for each type of trip as trip productions, trip attraction and internal-external trip
generations.
2.2.2 Trip Attraction Model Structure
In order to analyze trip attractions, the number of trips attracted to certain activities is related
to a measure of the amount of that activity. For example, the number of trips attracted might
be related to the number of employees in a factory or the number of employees in a store. The
structure of the trip attraction model relates trip ends by purpose to the amount, character, and
in some cases location of the activities as shown below:
Table2.1: Trip Attraction Table
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15. In the attraction model structure, the amount of activity is reflected in the rate per unit measure,
the character of activity by the type of activity, and the location by the downtown versus other
retail employment classification.
A considerable amount of research and development has focused on the area of disaggregate
models for improved travel demand forecasting. The difference between the aggregate and
disaggregate techniques is mainly in the data efficiency. Aggregate models are usually based upon
home interview origin and destination data that has been aggregated into zones; then the
“average” zonal productions and attractions are derived. The disaggregate approach is based
on large samples of household types and travel behaviors and uses data directly. There are savings
in the amount of data required and some of the data can be transferred to other applications.
The disaggregate approach express non-linear relationships and is more easily
understood.
2.2.3. Regression analysis
Regression analysis is a technique used for the modeling and analysis of numerical data
consisting of values of a dependent variable (response variable) and of one or more independent
variables (explanatory variables). The dependent variable in the regression equation is
modeled as a function of the independent variables, corresponding parameters (“constant”), and
an error term. The error term is treated as random variable. It represents unexplained variation in
the dependent variable. The parameters are estimated so as to give a “best fit” of the data. Most
commonly the best fit is evaluated by using the least squares method, but other criteria have
also been used. The underlying assumptions of linear regression modeling are:
The sample must be representative of the population for the inference prediction.
• The dependent variable is subject to error. This error is assumed to be a random
variable, with a mean of zero. Systematic error may be present but its treatment is outside the
scope of regression analysis.
• The independent variable is error-free. If this is not so, modeling should be done using
errors-invariables model techniques.
• The predictors must be linearly independent, i.e. must not be possible to express any
predictor as a linear combination of the others.
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16. • The errors are uncorrelated, that is, the variance-covariance matrix of the errors is
diagonal and each non-zero element is the variance of the error.
• The variance of the error is constant . If not, weights should be used.
• The errors follow a normal distribution. If not, the generalized linear model should be
used.
In linear regression, the model specification is that the dependent variable, yi is a linear combination
of the parameters (but need not be linear in the independent variables). For example, in simple
linear regression for modeling N data points there is one independent variable: xi and two
parameters, β0 and β1: εi is an error term and the subscript i indexes a particular observation.
Given a random sample from the population, we estimate the population parameters and
obtain the sample linear regression model:
yi = β0 + β1Xi + ei Equation (2.1)
The term ei is the residual, ei = yi - yi. One method of estimation is ordinary least squares. This
method obtains parameter estimates that minimize the sum of squared residuals, SSE:
SSE = ∑ e2i Equation (2.2)
Minimization of this function results in a set of normal equations, a set of
simultaneous linear equations in the parameters, which are solved to yield the
parameters estimators, β0, and β1. See regression coefficients for statistical properties of these
estimators. In the case of simple regression, the formulas for the least squares estimates are:
Where x the mean (average) of the x is values and y is the mean of the y values. See linear
least squares (straight line fitting) for a derivation of these formulas and a numerical example.
Under the assumption that the population error has a constant variance, the estimate of that
16
17. variance is given by:
.
This is called the root mean square error (RMSE) of the regression. The standard errors of the
parameter estimates are given
Under the further assumption that the population error term is normally
distributed, the researcher can use these estimated standard errors to create
confidence intervals and conduct hypothesis tests about the population parameters .
2.2.4 General Linear Data Model
In the general multiple regression models, there are p independent variables: yi = β0 + β1X1i +…..+
βpXpi+ εi, The least square parameter estimates are obtained by p normal equations. The residual can
be written as:
In any case once a regression model has been constructed, it is important to confirm the
goodness of fit of the model and the statistical significance of the estimated parameters.
Commonly used checks of goodness of fit include the R-squared, analyses of the pattern of
residuals and hypothesis testing. Statistical significance is checked by an F-test of the
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18. overall fit, followed by t-tests of individual parameters.
Table 2.2: Description of the Statistic Test
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19. 2.2.5 Multiple Regression Analysis
Multiple regression analysis is based on trip generation as a function of one
or more independent variables. He approach is mathematical and all of the variables are considered
random, and with normal distribution. Multiple regression analysis is relatively simple
productions and attractions are coupled with data about the area that is though to impact the
production and attraction of trips. For instance, the total population is believed to impact the
number of trips produced. If we know the number of trips produced and the population for the
present and a few time periods in the past, it is possible to develop a relationship between
these parameters using statistical regression. Once we are satisfied with the relationship that
has been developed, we can extrapolate into the future by plugging the future population into our
relationship and solving for the number of productions. The process is called Multiple
Regression, because there are normally several variables that impact trip production
and attraction.
Yi = A0 + A1i X 1i + A2 X2i + A3X3i Equation (2.9)
Where,
Yi = trip attracted to the Shopping Center at peak hour
X 1i = Gross Floor Area of Shopping Center i
X2i = Number of parking space at Shopping Center i
X3i = Number of shop in the Shopping Center i
A0 = Constant
A1, A2, A3 is regression coefficients
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20. 2.2.6 Trip Rate Analysis Model
Trip rate analysis model are based on the determination of the average trip production or attraction
rates associated with important trip generator or attractor within the region. An example of this
method is given below.
Table: Rates of Trip Attraction in the Sample Shopping Centers using Trip Rate Analysis model
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21. 2.3 Previous Study Regarding Trip Attraction
Studies on trips and vehicular attraction to land uses have been conducted in most western
countries but few for Asian conditions especially in the Bangladesh. There has been limited study
done yet at present to study the trips being attracted to commercial buildings
2.3.1 Strategic Transport Plan for Dhaka
In 2004, a project was undertaken by the Government of Bangladesh with the help of World Bank
to prepare a long term Strategic Transport Plan (STP) for the Dhaka Metropolitan Area. As a part
of the STP project, an urban transport planning model (UTP Model) was developed and used to
forecast future travel demand resulting from different land use scenarios and transport strategies
and to predict the performance of the existing, committed and alternative development strategies
for Dhaka‟s urban transport network infrastructure, services and policies (STP, 2005). The UTP
model developed in the STP study (STP, 2005) has some critical weaknesses in assumptions,
principles, framework and methods. The following considerations, used in the model
The model uses a three-dimensional matrix balancing to produce balanced matrices that satisfy
trip productions and attractions and trip length frequency distributions. These distributions are
produced for trips by each Trip Purpose and by members of each Household Income Group. The
trip purposes are Home to Work , Home to Education, Home to Other ,Non-Home Based Trip
length frequency distributions for motorized person-trips for each Trip Purpose were
determined from data obtained from the Household Interview Survey. The trip generation
productions and attractions predicted for the Traffic Analysis Zone (TAZ) together with the
trip length frequency distributions determined from the complete Household Interview Survey
results were input to the Three Dimensional Matrix Balancing module which created trip
origin and destination matrices for each of the four trip purposes and three Household Income
Groups. The Trip Length Frequency Distributions from the Household Interview Survey O-
D matrices were created in the UTP model data bank containing the number of person-trips
identified by the Household Interview Survey from TAZ of origin to TAZ of destination.
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22. For each Trip Purpose and Household Income Group, listings of the number of person-
trips of each 1-km interval of minimum path travel distance were made.
The Trip Length Frequency Distributions were kept constant through time. This was partly
because there was no logical scientific basis for making alterations. At the same time,
developments in new areas (such as the satellite cities) will be populated by the same people as
live elsewhere and it is assumed that employment will be provided to accommodate the
population. In this way the residents will behave in a similar way to the existing population.
According to STP (2005), the trip attraction part needed the most improvements. The study did
not attempt to derive any trip attraction model for zones by using any conventional variables on
the ground of their unavailability. Instead, for trip attractions, the number of total trip productions
for the study area was allocated to TAZs based on TAZ population modified by judgment of the
significance of the TAZ in attracting trips. However, the criteria of „judgment of the significance‟
were not well defined; in fact they were set arbitrarily. Moreover, prediction of trip production
used trip rates and household number and prediction of trip attraction used population data
making a large difference between production and attraction. Since UTP model could not make its
trip attraction analysis based on land use category of Dhaka, the Land Use Scenario predicted by
STP is not well reflected in the UTP model.
2.3.2 Dhaka Strategic Transport Model (DSTM)
The critical issues for developing a travel demand model for Dhaka city are how to handle the
extensive growth of a mixed land use pattern, the heterogeneous socio-economic structure and
travel behaviors and the mixed transportation system. A comprehensive strategic modeling
framework, Dhaka Strategic Transport Model (DSTM), is developed and implemented to simulate
the travel demand behavior of Dhaka city. It comprises a system of models for simulating the
travel behavior by applying the four-step modeling process. The framework is developed based on
a number of principles such as having strategic perspective; being market oriented; addressing
heterogeneous demand characteristics; following disaggregate approach and capturing the multi-
modal nature of the transport system of Dhaka.. The complexity of the travel demand situation of
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23. Dhaka is addressed by following a simple, modular and flexible structure so that individual model
elements can be refined, enhanced and applied easily.
Trip attraction model of DSTM is based on a GIS map containing land use features classified as
commercial, mixed, public and housing area and the number of the stories of the buildings for
each land use feature. An assessment of gross floor area (GFA) of different land use features has
been made. From the survey and literature review trip rates have been established. Gross floor
area is multiplied with the established rates to calculate the total number of trips attracted.
2.3.3 Dhaka Urban Transport Network Development Study (DHUTS)
The study produced the projection of trip attraction on the basis of each purpose by constructing
a simple linear regression equation whose explanatory variables are each corresponding
population (no constant term). Also the prediction models for each low-, medium-, high-income
strata were constructed.
Trip Attraction and its Explanatory Variable
Purpose of Traffic Volime Population as Explanatory
Variable
To Work Worker at Office Base
To School Student at Enrollment Base
Non-Home Based Business Worker at Office Base
Private Worker at Office Base
To Home Night Population
Trip production in RAJUK was set as a control total of the amount of trips all over RAJUK.
Based on the trip production and future population distribution by each traffic analysis zone, trip
generation/attraction was forecasted. According to the results of forecasts in all trip purpose, all
income group, trip generation/attraction will significantly increase in eastern fringe area located
in the border area of DCC and DMA
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24. 2.3.4 Integrated Environmental Strategies (IES) Study in Hyderabad, India
IES (2004) study developed a 4-step transport demand model for Hyderabad, which is one of the
fastest growing centers of urban development in India. The massive growth of the city has
brought with it air quality and congestion problems. For various reasons, motorized two wheelers,
auto rickshaws and private passenger cars, have displaced trip making traditionally accomplished
by public transport and bicycle. Traffic congestion, the predominance of two-stroke vehicles in
the traffic mix and inability of public transport to attract significant ridership have all been
considered responsible for the severe air quality problems in Hyderabad. The objective of this
study was to perform analysis policies to address these important issues in Hyderabad‟s transport
sector. The study process consisted of development of models, forecast of future travel demand
and analysis of alternative strategies for handling the demand. In this particular study, an attempt
was made to develop operational models using normally available variables, which can be
forecasted with reasonable degree of accuracy. The standard and easily available planning
variables at zonal levels such as population, employment, number of workers residing, number of
students residing and student enrollment, etc. collected as a part of household survey and
secondary data collected were used in the analysis.
Regarding trip attraction part, various trip attraction equations or models were developed for
work, education and other purposes by relating the purpose wise trips attracted to zone with
independent variables such as zone wise employment, student enrollment, and distance from
CBD, accessibility rating and population. Work trips attracted to a zone were found to be
significantly related to the zone wise employment, while zone wise student enrollment was found
a significant variable in estimating education trips attracted to a zone. Employment and
accessibility rating were found to be most significant in estimating one-way other purpose trips
attracted to each zone. Accordingly, the most significant trip attraction models were used along
with projected values of the selected independent variables for estimating future year zone wise
trip productions and attractions. The model predicts 5.45 million daily home-based inter-zonal
trips for in 2003 when city population is 6.80 million.
24
25. 2.3.5 Trip Attraction Rates of Shopping Centers in Northern New Castle County, Delaware
This report presents the trip attraction rates of the shopping centers in Northern New Castle
County in Delaware. The study aims to provide an alternative to ITE Trip Generation Manual
(1997) for computing the trip attraction of shopping centers in Delaware. As part of this study, a
total of eighteen shopping centers were surveyed, for which the number of vehicles entering and
leaving the shopping center in every fifteen minutes interval and the number of people visiting
each store in the shopping center along with their movement patterns were measured. Based on
the surveyed data and the aerial photographs, two approaches, microscopic and macroscopic, are
developed to compute the trip attraction rate. The microscopic approach deals with the
relationship between the trip attraction rates of individual stores and the shopping center as a
whole. The macroscopic approach relates the trip attraction of the shopping center as a function of
the physical features of the shopping center, e.g. total parking spaces, total floor area, and the
number of stores in the shopping center. The study shows that microscopic approach gives a
better estimate of trip attraction compared with the macroscopic approach. The proposed models
incorporate the factors that have been neglected in ITE Trip Generation Manual. These models
should be useful for estimating the traffic volume to/from a new shopping center which, is being
planned and to assess the traffic impact of the shopping center on the geometric design of
roadways in the surrounding area. The report consists of the description of the analytical
approach, survey methods, the data collected from the survey and the analysis of the data using
the models proposed.
2.3.6 Trip Attraction Development Statistical Modeling Dohuk City Residential Area
According to Khalik & Taher, trip attraction phenomenon was studied for 20 residential out of 28
traffic zones located within Dohuk city urban area composed of more than 300,000 in residents.
Home-interview travel data was provided for the city were used in addition to special data
collected to perform the trip attraction analysis .Attraction trips were classified into seven types
and selected as dependent variables while other variables like number of dwelling units,
employment, etc. are selected as independent variables in the SPSS package to obtain the most
25
26. statistically well accepted predicted attraction trip models. Some models like HBW trips are
constant eliminated with good (R2 ) value. HBSH and HBOH trips are showing weak correlation
with their independent variables like amount of CBD area and number of retail sales located
within CBD area.
2.3.7 Deciding where to shop: disaggregate random utility destination choice modeling of
grocery shopping in canton Zurich
Axhausen and Bodenmann (2008) developed the choice process of individuals deciding where to
shop for groceries. Two main datasets were used: a trip dataset from the Micro-census for
Swiss travel behavior 2005 in which all locations were geo-coded; and a store attribute
dataset compiled by the author from various sources. Choice sets were generated based to the
travel time budget of individuals and a random sample with a fixed number of alternatives was
drawn for the discrete choice modeling process. Multinomial logit (MNL) models were run for
trips by car and by walking.
For the car model, the most relevant store attributes in the destination choice process were
found to be the store size and distance required to reach the store in home and non-home
based trips. Regarding socio-demographic characteristics of the individuals, income and
household size were found to drive the decision over age and gender.
Given the number of available observations, (810 identified shopping trips locations for all
modes, of which 331 by car and 250 by walking), the distribution of the trips by mode (over 70%
of the trips by car and walking), and the lack of data for other trips; two different models were
estimated, one for trips by car, and another one for trips done by walking. The results for the CAR
model were consistent with expectations, where the value of the parameter for distance was a
(significant) deterrent in the shopping decision, and store size played an important role in
attracting more customers.
Contrary to the CAR model, the value of the parameter for the WALK model results in the
distance from home to shop as an (insignificant) incentive to shop for groceries. It is against
26
27. common sense to believe people will prefer to travel further away from home to shop for
groceries, especially when they have to walk to the store. A possible explanation for this result
in the model is the store size, where people might walk past by many small stores with a
limited offer of goods to shop in larger stores where the offer might be larger. Another
explanation might be linked to the data: many very small stores, which have none, or in the
best case, just a few shopping destinations attached to them, were included in the choice set. A
total of 605 of the 1‟250 shops in the universe of alternatives (48.4%) are small (under 100 m )
bakeries, butchers or small green 2 grocers , in which a total of 55 persons in the sample (6.7%)
shopped for groceries. The extent of this effect is yet to be determined.
Regarding model complexity and interaction of terms, slightly better results were observed for
simpler models with linear interactions between the socio-demographic terms and the store
attributes, rather than for more complex models with non-linear interactions. This result
might be also be related to the low number of observations available to run the CAR model.
2.3.8 Disaggregate Attraction-End Choice Modeling, Formulation and Empirical Analysis
Bhat et al. formulated and estimated disaggregate attraction disaggregate attraction-end choice
model that will facilitate the replacement of the aggregate trip attraction models and aggregate
trip distribution model currently used by most metropolitan planning organization. The research
estimated attraction end model for two work purpose: home based work and home based
shopping and personal business. Six sets of explanatory variables were included for each work
purpose of attraction-end choice model: (a) impedance variables, (b) zonal size measure, (c)
zonal attractiveness measure, (d) zonal location indicator, (e) a zonal spatial structure measure,
and (f) interaction of socio demographic variable with impedance and zone associated variables.
27
28. 2.3.9 Trip Attraction of Mixed-Use Development in Metropolitan Manila
Fillon & Tescon developed multiple regression models that estimate the volume of person trips
and vehicles attracted to condominiums catering to mix uses in the study. Thirty condominiums
within Metro Manila were randomly selected and their attributes such as the available residential
floor area, parking slots, commercial floor area, occupancy rates and the like were gathered.
Morning to afternoon hourly counting of people and vehicles that went to the condominium was
conducted. The peak hour volume of people and vehicles was known to occur in the morning.
Number of commercial establishments, commercial floor area, residential floor area, and total
number of floors, unit occupancy rate, building employees employed, parking occupancy rate,
average person per unit, and years in operation, Number of entrance/exit was taken as site
characteristics. Multiple linear regression models were then developed separately on the casual
relationship between peak people and vehicular attraction as related to site characteristics. The
resulting equations showed that the residential floor area was strongly related to the peak
vehicular attraction as well as volume of people entering the condominium.
2.4 Summery
28
29. CHAPTER THREE
STUDY FRAMEWORK AND DATA COLLECTION
3.1 Overview:
The main purpose of this study is to analysis the current shopping mall trip rate for Dhanmondi
area of Dhaka city. For the purpose of this study, trip is defined as a journey made by people
from their accommodations and working place to the shopping mall in the Dhanmondi area or
vice versa which there are a total of six shopping malls were listed in the Dhanmondi area.
Population. In order to attain the aim of the study, numbers person and car incoming to the
shopping malls were collected at peak hour period.
Dhaka city is selected as a study area because it has been identified as the faster growing city in
Bangladesh. The study tends to get the recent trip attraction pattern of the Dhaka city with the
new development of their surrounding area. The process of completing the study involves 3 steps,
which could be categorized into a basic approaches namely data collection, analysis and primary
school trip generation model building. This chapter will discuss in sequence the procedure
applied in achieving the objectives of the study at next page in Figure 3.1
29
30. Phase 1: Initial Study
Preliminary understanding of the
Trip Attraction concept
Literature review
Problem statement
Aim , objective and overview of
study
Phase 2: Survey and Data collection
Survey Data collection
Site survey Total number of parking space
Shopping mall survey and gross floor area
Parking space survey Total number of storey, shops
and employees
Total number of incoming
people and car at peak period
Phase 3: Calculation and Analysis
of Shopping trip attraction rate
Phase 4: Conclusion
Fig 3.1: Study flow chart
30
31. The first phase of this study involves initial study about the fundamental of trip generation and
trip attraction model. Through this initial study, a basic concept or idea about the fundamental of
trip attraction rate calculation method can be understood. This first phase is important in order to
apply the concept in the detailed analysis in phase three.
Primary and secondary data were collected. For data such as Dhanmondi area population, land
use, employment, land value and other physical features of shopping centers in Dhanmondi area
were collected. The data of shopping mall was obtained for the year 2011. This survey
can be done by site visit or comparing the data with local plan, reports or refer to the local
authority. The number of incoming people and car attracted to the shopping mall were collected
during peak period at 15 minutes interval.
In the third phase, the data will be analyzed to calculate the trip attraction
rate. Through the calculation, relationship between every parameter will be determined and
significance of every parameter also can be defined. Furthermore the attraction of shopping trip
generation can be determined for Dhanmondi area.
As a final product of this study, shopping trip attraction rate in Dhaka city will be determined
and the mathematical relationships that synthesis trip attraction pattern on the basis of
observed trips will be produce. The trip rates of the shopping centers will indicate the
relationship between three parameters that used in the model which is accessibility, holding
capacity and cost index.
3.2 Study area
Once the nature of the problem at hand is identified, the study area can be
defined to encompass the area of expected. Study areas are geographic boundaries
created to define the extent of the study analysis. The boundary of this study area to
as the external cordon, include the develop area. The location of the boundary also
important to defined the location of the shopping centers in Dhanmondi area of Dhaka city. In
recent decades there are significant numbers of shopping mall had been constructed in
Dhanmondi area, which affects the travel pattern in the area. The shopping mall growth rate in
the area is higher. Six different shopping mall on Mirpur road was selected for calculating the
31
32. trip attraction rate .The shopping malls are Orchard point, ARA Center, Metro Shopping Mall,
Rapa plaza, Plaza AR and Adel Plaza. The study area is situated in TAZ 2 of Dhaka city.
Adel Plaza
Rapa Plaza
Plaza AR
ARA Center
Orchard Point
Fig 3.2: Map of the study area with studied shopping mall location Dhanmondi, Dhaka
(Source Google Map)
32
33. Shopping Center centered study
The survey was conducted for six Shopping Centers (Shopping Centers) in Dhanmondi Mirpur
road, Dhaka. This section describes how the Shopping Centers are categorized into different
groups for the purpose of analysis and the characteristics of the Shopping Centers belonging to
these groups. The Shopping Centers are classified into 4 groups based on the composition of the
stores in the Shopping Center.
Type 1: This is a large Shopping Center with a large supermarket, a large discount retail store, one
or two restaurants, a bank, and many small stores are located. The Shopping Center in this category
are New market, Gulshan shopping center, Mohakhali Municipal Market ,Bashundhara City
Type 2: This is a medium size Shopping Center where a medium sized supermarket, a medium
sized discount retail store and many smaller stores are located. The Shopping Centers in this
category are Rapa plaza, Metro Shopping Mall.
Type 3: This is a small Shopping Center where one supermarket and several small stores are
located. The Shopping Centers in the category are A.R.A Center, Sunrise Plaza.
Type 4: This is a collection of specialty stores, but does not include a supermarket or discount
retail store. The Shopping Centers in this category are Multiplan Center, BCS Computer city.
This study concludes the type 1 and type 2 Shopping center s in Dhanmondi area. The location of
the Shopping Centers surveyed is shown in Figure 3.2.
33
34. Fig 3.3: A front view of A.R.A Center Mirpur Road, Dhaka
Fig 3.4: A front view of Metro Shopping Mall Mirpur Road ,Dhaka
34
35. 3.4 Time of Survey
The data was collected on different days of the week and different times of the day for a
period of 1 month (Late Autumn of 2011 during October) all the data was collected for every 15
minutes time interval. This interval is chosen because Highway Capacity Manual uses
this interval as the base unit for capacity calculation, and also it is rather practical from
the standpoint of the person collecting the data. The typical duration of a survey was
three hours. The smaller Shopping Centers were observed between 4 p.m. and 7 p.m., usually
during the weekdays. The larger Shopping Centers were observed during the peak hour traffic on
Fridays (4 p.m.-7 p.m.)
3.5 Data required
Based on the models proposed in Chapter 2, the data required for the analysis is divided into two
general categories
The trip attraction rate (Trip Attraction Rate) of the whole Shopping Center in terms of the
number of vehicles and person entering the Shopping Center in 15-minute intervals.
The physical features of the Shopping Center, e.g., floor space of individual storey, The total
floor area of Shopping Center (cumulative floor area of all stores in the Shopping Center), number
of parking spaces, no of employee, total number of shops and total site area.
3.6 Initial Survey
Dhanmondi in Dhaka is located just beside the busy roads (Mirpur-Azimpur). Analysis of
existing data (from BRTC, BUET, 2010) indicates that all types of vehicle (motorized and non-
motorized) use this road. There are several shopping malls on this road. Among them six
shopping mall were selected for calculating trip attraction rate. The shopping malls are Orchard
point, ARA Center, Metro Shopping Mall, Rapa plaza, Plaza AR and Adel Plaza. From initial
survey it was found that. The peak period generally occurs at evening. So data collection time
was set from (4:00 pm -7 pm). The physical features of the shopping centers like no of entry
gate, no of storey, parking space availability, area of each floor space were also estimated initial
survey.
35
36. 3.7 Data collection
3.7.1. Physical features of shopping mall
Six Shopping Center (Shopping Center) were selected from initial survey. The Shopping
Center‟s are Orchard point, ARA Center, Metro Shopping Mall, Rapa plaza, Plaza AR and Adel
Plaza.
The Shopping Center site area was calculated from using website like Wikimapia. As the
Shopping Center‟s are geometrically rectangle shapes the length and width of each shopping
mall was measured using odometer. Then floor area is calculated multiplying the length and
width. The Gross Floor Area (Gross Floor Area) of each Shopping Center was calculated by
multiplying the floor area and number of storey.
Data regarding Number of storey, number of shops, total number of employee and number of
entry and exit gate were collected by visual observation and through the consultation of
Shopping Center authority.
The Shopping Centers contain stores and parking spaces. The number of parking spaces is based
upon accessibility characteristics, e.g., pedestrian orientation and transit availability. The
minimum standard for parking space for the Shopping Center is 5 spaces per 1,000 square feet
retail area (Calthorpe, 1993; Steiner, 1998). A rule of thumb parking requirement is two
hundred square feet per vehicle. The scale of the Shopping Center, the distance between
establishments (stores), the vast parking lots to cross, and lack of direct pedestrian
connections discourage the visitor to travel from store to store on foot (Campoli, Humstone
and McLean, 2002). Based on our observation, in all the Shopping Center‟s surveyed there are
plenty of parking spaces. The data is obtained from the Shopping Center authority. The
physical features of each shopping mall are given next page obtained by survey.
36
37. Table 3.1 Table showing the number of entry and exit gate of each shopping center
Name of the Shopping Center Total number of
Entry gates
Orchard Point 2
A.R.A Center 1
Plaza A.R. 2
Metro Shopping Center 2
Rapa Plaza 2
Adel Plaza 2
Table 3.2 Table showing the total number of shops of each shopping center
Name of the Shopping Center Total Number of
Shops
Orchard Point 80
A.R.A Center 38
Plaza A.R. 75
Metro Shopping Center 165
Rapa Plaza 85
Adel Plaza 10
37
38. Table 3.3 Table showing the total number of parking spaces of each shopping center
Name of the Shopping Center Total Number of
Parking Spaces
Orchard Point 60
A.R.A Center 20
Plaza A.R. 40
Metro Shopping Center 65
Rapa Plaza 90
Adel Plaza 20
Table 3.4 Table showing total number employees of each shopping center
Name of the Shopping Center Total number of
Employees
Orchard Point 300
A.R.A Center 80
Plaza A.R. 250
Metro Shopping Center 500
Rapa Plaza 300
Adel Plaza 130
38
39. Table 3.5 Table showing the floor area, total number of floor and gross floor area of each
shopping center
Name of the Floor Area Total number of Gross Floor Area
Shopping Center (Sq. feet) Floors (Sq. feet)
Orchard Point 12956 6 77736
A.R.A Center 5481 4 21924
Plaza A.R. 12831 6 76986
Metro Shopping 13321 6 79926
Center
Rapa Plaza 13300 6 79800
Adel Plaza 6825 8 54600
3.7.2 Trip Attraction at 15 minute interval
The Trip Attraction of the Shopping Centers is obtained from the number of people and vehicles
entering the Shopping Center in every 15 minutes interval. Number of incoming shoppers and
car trips were counted by survey for every 15 minute intervals during the peak period. The peak
period time range was (4:00 pm- 7:00 pm). Incoming shopping trip counts were collected by
visual observation. One surveyor was appointed in each gate of the shopping center. Data were
collected for typical week day and week end day (Friday, Saturday). For each shopping center
data were collected for two days to get the shopper trip variation during week day and weekends.
There is a large variation in the number of people coming to the Shopping Center depending on
the number of the time of the day, day of the week and the season. The fluctuations in the Trip
Attraction Rate of the stores and the Shopping Center on the whole show the complexity involved in
studying the trip attraction of Shopping Centers. Table shows the number of vehicles and people
entering the Shopping Center during a three-hour survey period for two different days. The
graphs showing variation of trips for weekday and weekends for six Shopping Centers included in
the survey in the following pages.
39
40. Table 3.6: Number of incoming persons and incoming vehicles to the Rapa Plaza in fifteen
minutes intervals.
Week day: Saturday Date: 29-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 127 8
4:15pm-4:30pm 144 9
4:30pm-4:45pm 160 13
4:45pm-5:00pm 164 16
5:00pm-5:15pm 182 11
5:15pm-5:30pm 209 9
5:30pm-5:45pm 167 11
5:45pm-6:00pm 173 13
6:00pm-6:15pm 172 12
6:15pm-6:30pm 154 9
6:30pm-6:45pm 195 17
6:45pm-7:00pm 185 14
Table 3.7: Number of incoming persons and incoming vehicles to the Rapa Plaza in fifteen
minutes intervals.
Week day: Thursday Date: 27-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 122 7
4:15pm-4:30pm 137 8
4:30pm-4:45pm 149 9
4:45pm-5:00pm 159 12
5:00pm-5:15pm 168 12
5:15pm-5:30pm 178 13
5:30pm-5:45pm 157 11
5:45pm-6:00pm 175 13
6:00pm-6:15pm 187 15
6:15pm-6:30pm 151 12
6:30pm-6:45pm 165 15
6:45pm-7:00pm 170 8
40
41. Person Trip Attraction in Day 1 & Day 2 Variation
at Rapa Plaza (at 15 min interval)
220
210
200
190
Person Trip
180
170
160
150
140 Person Trip(in) DAY 1
130
Person Trip(in) DAY 2
120
110
100
Graph 3.1 graph showing the variation of person trip attraction of day 1 and day 2 at Rapa plaza
(at 15 min interval)
Variation In Car Trip Atraction of Day 1 & Day 2
at Rapa Plaza (at 15 min interval)
18
16
14
12
car trip
10
8
6
Car Trip(in) DAY 1
4
2 Car Trip(in) DAY 2
0
Graph 3.2 graph showing the variation of car trip attraction of day 1 and day 2 at Rapa plaza (at
15 min interval)
41
42. Table 3.8: Number of incoming persons and incoming vehicles to the Orchard point in
fifteen minutes intervals.
Week day: Thursday Date: 20-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 103 8
4:15pm-4:30pm 101 10
4:30pm-4:45pm 107 14
4:45pm-5:00pm 115 10
5:00pm-5:15pm 130 13
5:15pm-5:30pm 106 15
5:30pm-5:45pm 88 19
5:45pm-6:00pm 97 15
6:00pm-6:15pm 114 13
6:15pm-6:30pm 133 13
6:30pm-6:45pm 140 16
6:45pm-7:00pm 104 11
Table 3.9: Number of incoming persons and incoming vehicles to Orchard point in fifteen
minutes intervals.
.
Week day: Friday Date: 21-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 106 9
4:15pm-4:30pm 127 11
4:30pm-4:45pm 157 14
4:45pm-5:00pm 123 8
5:00pm-5:15pm 146 11
5:15pm-5:30pm 140 9
5:30pm-5:45pm 120 14
5:45pm-6:00pm 109 13
6:00pm-6:15pm 149 11
6:15pm-6:30pm 153 14
6:30pm-6:45pm 162 13
6:45pm-7:00pm 136 8
42
43. Person Trip Attraction in Day 1 & Day 2 Variation
at Orchard point (at 15 min interval)
180
170
160
150
Person Trip
140
130
120
110
100 Person Trip(in) DAY 1
90
80 Person Trip(in) DAY 2
70
60
Graph 3.3 graph showing the variation of person trip attraction in day 1 and day 2 at Orchard
Point (at 15 min interval)
Variation In Car Trip attraction of Day 1 & Day 2
at Orchard point (at 15 min interval)
20
18
16
14
car trip
12
10
8
6 Car Trip(in) DAY 1
4
2 Car Trip(in) DAY 2
0
Graph 3.4 graph showing the variation of car trip attraction of day 1 and day 2 at Orchard
Point(at 15 min interval)
43
44. Table 3.10: Number of incoming persons and incoming vehicles to the Metro Shopping
Mall in fifteen minutes intervals.
Week day :Monday Date:03-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 133 5
4:15pm-4:30pm 160 7
4:30pm-4:45pm 203 8
4:45pm-5:00pm 148 5
5:00pm-5:15pm 197 6
5:15pm-5:30pm 166 5
5:30pm-5:45pm 170 8
5:45pm-6:00pm 140 8
6:00pm-6:15pm 190 5
6:15pm-6:30pm 185 10
6:30pm-6:45pm 170 7
6:45pm-7:00pm 165 5
Table 3.11: Number of incoming persons and incoming vehicles to the Metro Shopping
Mall in fifteen minutes intervals.
Week day :Friday Date:07-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 127 5
4:15pm-4:30pm 142 4
4:30pm-4:45pm 161 7
4:45pm-5:00pm 136 6
5:00pm-5:15pm 200 4
5:15pm-5:30pm 147 8
5:30pm-5:45pm 148 5
5:45pm-6:00pm 150 7
6:00pm-6:15pm 154 4
6:15pm-6:30pm 157 9
6:30pm-6:45pm 185 5
6:45pm-7:00pm 178 6
44
45. Person Trip Attraction in Day 1 & Day 2 Variation
at Metro Shopping center (at 15 min interval)
220
210
200
Person Trip
190
180
170
160
150
140
130 Person Trip(in) DAY 1
120
110 Person Trip(in) DAY 2
100
Graph 3.5 graph showing the variation of person trip attraction in day 1 and day 2 at Metro
Shopping Mall(at 15 min interval)
Variation In Car Trip Attraction of Day 1 & Day 2
at Metro Shopping Mall ( at 15 min interval)
12
10
8
car trip
6
4 Car Trip(in) DAY 1
2 Car Trip(in) DAY 2
0
Graph 3.6 graph showing the variation of car trip attraction of day 1 and day 2 at Metro
Shopping mall (at 15 min interval)
45
46. Table 3.12: Number of incoming persons and incoming vehicles to the A.R.A Center in
fifteen minutes intervals.
Week day :Sunday Date:23-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 22 5
4:15pm-4:30pm 15 3
4:30pm-4:45pm 24 5
4:45pm-5:00pm 22 4
5:00pm-5:15pm 37 9
5:15pm-5:30pm 23 4
5:30pm-5:45pm 32 10
5:45pm-6:00pm 34 6
6:00pm-6:15pm 27 7
6:15pm-6:30pm 17 4
6:30pm-6:45pm 23 6
6:45pm-7:00pm 16 3
Table 3.13: Number of incoming persons and incoming vehicles to the A.R.A Center in
fifteen minutes intervals.
Week day :Friday Date:28-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 25 6
4:15pm-4:30pm 20 3
4:30pm-4:45pm 30 7
4:45pm-5:00pm 32 5
5:00pm-5:15pm 40 10
5:15pm-5:30pm 35 7
5:30pm-5:45pm 45 10
5:45pm-6:00pm 36 5
6:00pm-6:15pm 30 4
6:15pm-6:30pm 22 3
6:30pm-6:45pm 25 6
6:45pm-7:00pm 20 5
46
47. Variation of Person Trip attraction in Day 1 &
Day 2 at A.R.A Center(15 min interval)
60
50
Person Trip
40
30
20 Person Trip(in) DAY 1
10 Person Trip(in) DAY 2
0
Graph 3.7 graph showing the variation person trip attraction in day 1 and day 2 at A.R.A Center
(at 15 min interval)
Variation In Car Trip Atraction of Day 1 & Day 2
at A.R.A Center(at 15 min interval)
12
10
8
car trip
6
4
Car Trip(in) DAY 1
2
Car Trip(in) DAY 2
0
Graph 3.8 graph showing the variation of car trip attraction of day 1 and day 2 at A.R.A
Center(at 15 min interval)
47
48. Table 3.14: Number of incoming persons and incoming vehicles to the Plaza A.R. in
fifteen minutes intervals.
Week day :Friday Date:14-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 117 5
4:15pm-4:30pm 120 8
4:30pm-4:45pm 135 10
4:45pm-5:00pm 143 11
5:00pm-5:15pm 140 13
5:15pm-5:30pm 145 12
5:30pm-5:45pm 134 8
5:45pm-6:00pm 115 9
6:00pm-6:15pm 164 11
6:15pm-6:30pm 163 10
6:30pm-6:45pm 145 8
6:45pm-7:00pm 138 7
Table 3.15: Number of incoming persons and incoming vehicles to the Plaza A.R. in
fifteen minutes intervals.
Week day :Thursday Date:13-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 105 7
4:15pm-4:30pm 112 10
4:30pm-4:45pm 117 11
4:45pm-5:00pm 111 15
5:00pm-5:15pm 127 13
5:15pm-5:30pm 86 12
5:30pm-5:45pm 108 9
5:45pm-6:00pm 90 8
6:00pm-6:15pm 81 12
6:15pm-6:30pm 138 7
6:30pm-6:45pm 140 7
6:45pm-7:00pm 125 6
48
49. Variation in Person Trip attraction of Day 1 &
Day 2 at Plaza A.R.(at 15 min interval)
180
170
160
150
Person Trip
140
130
120
110
100 Person Trip(in) DAY 1
90 Person Trip(in) DAY 2
80
70
60
Graph 3.9 graph showing the variation of person trip attraction in day 1 and day 2 at Plaza A.R.
(at 15 min interval)
Variation In Car Trip Atrraction of Day 1 & Day 2
at Plaza A.R ( at 15 min interval)
16
14
12
car trip
10
8
6
4 Car Trip(in) DAY 1
2 Car Trip(in) DAY 2
0
Graph 3.10 graphs showing the variation of car Trip Attraction Rate day 1 and day 2 at Plaza
A.R. (at 15 min interval)
49
50. Table 3.16: Number of incoming persons and incoming vehicles to the Adel Plaza in fifteen
minutes intervals.
Week day: Monday Date: 17-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 70 5
4:15pm-4:30pm 85 4
4:30pm-4:45pm 76 7
4:45pm-5:00pm 80 5
5:00pm-5:15pm 100 9
5:15pm-5:30pm 92 4
5:30pm-5:45pm 95 7
5:45pm-6:00pm 88 5
6:00pm-6:15pm 108 5
6:15pm-6:30pm 110 8
6:30pm-6:45pm 115 12
6:45pm-7:00pm 104 12
Table 3.17: Number of incoming persons and incoming vehicles to the Adel Plaza in fifteen
minutes intervals.
Week day: Saturday Date: 22-10-2011
Time interval Incoming people Incoming Vehicle(car)
4:00pm-4:15pm 63 6
4:15pm-4:30pm 70 4
4:30pm-4:45pm 77 6
4:45pm-5:00pm 83 5
5:00pm-5:15pm 97 7
5:15pm-5:30pm 89 9
5:30pm-5:45pm 81 7
5:45pm-6:00pm 74 4
6:00pm-6:15pm 92 5
6:15pm-6:30pm 88 9
6:30pm-6:45pm 115 8
6:45pm-7:00pm 110 8
50
51. Variation in Person Trip Attraction of Day 1 &
Day 2 at Adel Plaza ( at 15 min interval)
120
110
100
Person Trip
90
80
70
Person Trip(in) DAY 1
60
Person Trip(in) DAY 2
50
40
Graph 3.11 graphs showing the variation of person trip attraction in day 1 and day 2 at Adel
Plaza. (at 15 min interval)
14
Variation In Car Trip Attraction of Day 1 & Day 2
12
at Adel Plaza (at 15 min interval)
10
car trip
8
6
4
Car Trip(in) DAY 1
2 Car Trip(in) DAY 2
0
Graph 3.12 graphs showing the variation of car trip attraction of day 1 and day 2 at Adel Plaza.
(at 15 min interval)
51
52. CHAPTER FOUR
ANALYSIS AND FINDINGS
4.1 Overview
The chapter focuses on the calculation of trip attraction rates of the studied Shopping Centers.
The rates are estimated with the collected survey data. Trip rate analysis method will be applied
for calculating Trip Attraction Rate of the studied shopping center. At first PM peak hour
incoming trip will be calculated from the collected data both person trip and car trip for each
shopping center. After the estimation of pm peak hour trip rate, the trip rate will be used to
calculate Trip Attraction Rate with respect to the physical features of Shopping Centers. At last a
variation of Trip Attraction Rates among different shopping malls will be shown. The Trip
Attraction Rate will be calculated both for typical week day and week end for each Shopping
Center. From the Trip Attraction Rates of day 1 and day 2 an average trip rate will be calculated.
4.2 Peak hour trip rate calculation
Six different Shopping center of Dhanmondi areas were studied. Data were collected for each
Shopping Center from (4 pm – 7 pm.) which is the peak period for Shopping Centers. The
number of incoming people and number of car was counted for every 15 minutes interval. The
sum of every 4 consecutive interval incoming trip is then counted for calculating peak hour
incoming trip rate. Summation of every 4 interval data will be calculated for hourly trip rate. The
highest hourly data will be considered as peak hour trip rate for each Shopping Center. This
procedure will be done for both day1 and day 2 data. In the next consecutive pages peak hour
trip rate calculation will be shown in tabular format.
52
