Trip attraction rates of shopping centers in dhanmondi area of dhaka city final

CHAPTER ONE

INTRODUCTION

11 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 peoplersquos 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

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

12 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 37 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

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

13 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

14 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 andor

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

15 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 ldquoinitial pilotrdquo 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

CHAPTER TWO

LITERATURE REVIEW

21 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 21 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

Figure 21 The Classic Four-stage Transport Model

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 analysts 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 peoples 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

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

Figure 22 The Relationship between OD 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

211 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 ldquoproducedrdquo at a

personrsquos residence Trip attraction (TA) represents a trip starting or ending in a non-residential area

Figure 23 shows how a person traveling from residence to an activity center generates two trip

productions and two trip attractions

Figure 23 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 ldquoraterdquo 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

212 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

Figure 24 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

213 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

214 Shopping Trip Attractions

2141 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

2142 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

2143 Number of parking spaces

Number of parking spaces available for car parking is another important factor for estimating Trip

Attraction Rate

2144 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

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 25 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

22 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

221Cross-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

222 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

Table21 Trip Attraction Table

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

ldquoaveragerdquo 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

223 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 (ldquoconstantrdquo) 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 ldquobest fitrdquo 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

bull 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

bull The independent variable is error-free If this is not so modeling should be done using

errors-invariables model techniques

bull The predictors must be linearly independent ie must not be possible to express any

predictor as a linear combination of the others

bull 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

bull The variance of the error is constant If not weights should be used

bull The errors follow a normal distribution If not the generalized linear model should be

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 (21)

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 = sum e2i Equation (22)

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

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

224 General Linear Data Model

In the general multiple regression models there are p independent variables yi = β0 + β1X1i +hellip+

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

overall fit followed by t-tests of individual parameters

Table 22 Description of the Statistic Test

225 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 (29)

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

226 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

23 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

231 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 Dhakarsquos 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

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 lsquojudgment of the significancersquo

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

232 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

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

233 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

generationattraction was forecasted According to the results of forecasts in all trip purpose all

income group trip generationattraction will significantly increase in eastern fringe area located

in the border area of DCC and DMA

234 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 Hyderabadrsquos 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 545 million daily home-based inter-zonal

trips for in 2003 when city population is 680 million

235 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 eg 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 tofrom 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

236 Trip Attraction Development Statistical Modeling Dohuk City Residential Area

According to Khalik amp Taher trip attraction phenomenon was studied for 20 residential out of 28

traffic zones located within Dohuk city urban area composed of more than 300000 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

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

237 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

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 1rsquo250 shops in the universe of alternatives (484) are small (under 100 m )

bakeries butchers or small green 2 grocers in which a total of 55 persons in the sample (67)

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

238 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

239 Trip Attraction of Mixed-Use Development in Metropolitan Manila

Fillon amp 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 entranceexit 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

24 Summery

CHAPTER THREE

STUDY FRAMEWORK AND DATA COLLECTION

31 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 31

Fig 31 Study flow chart

Phase 1 Initial Study

Preliminary understanding of the Trip Attraction concept

Literature review Problem statement Aim objective and overview of

Phase 2 Survey and Data collection

Survey

Site survey Shopping mall survey Parking space survey

Data collection

Total number of parking space and gross floor area

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

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

32 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

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

Fig 32 Map of the study area with studied shopping mall location Dhanmondi Dhaka

(Source Google Map)

Adel Plaza

Rapa Plaza

Plaza AR

ARA Center Orchard Point

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 ARA 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 32

Fig 33 A front view of ARA Center Mirpur Road Dhaka

Fig 34 A front view of Metro Shopping Mall Mirpur Road Dhaka

34 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 pm and 7 pm usually

during the weekdays The larger Shopping Centers were observed during the peak hour traffic on

Fridays (4 pm-7 pm)

35 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 eg 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

36 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 (400 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

37 Data collection

371 Physical features of shopping mall

Six Shopping Center (Shopping Center) were selected from initial survey The Shopping

Centerrsquos are Orchard point ARA Center Metro Shopping Mall Rapa plaza Plaza AR and Adel

The Shopping Center site area was calculated from using website like Wikimapia As the

Shopping Centerrsquos 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 eg pedestrian orientation and transit availability The

minimum standard for parking space for the Shopping Center is 5 spaces per 1000 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 Centerrsquos 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

Table 31 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

ARA Center 1

Plaza AR 2

Metro Shopping Center 2

Rapa Plaza 2

Adel Plaza 2

Table 32 Table showing the total number of shops of each shopping center

Name of the Shopping Center Total Number of Shops

Orchard Point 80

ARA Center 38

Plaza AR 75

Rapa Plaza 85

Adel Plaza 10

Table 33 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

ARA Center 20

Plaza AR 40

Rapa Plaza 90

Adel Plaza 20

Table 34 Table showing total number employees of each shopping center

Name of the Shopping Center Total number of Employees

Orchard Point 300

ARA Center 80

Plaza AR 250

Rapa Plaza 300

Adel Plaza 130

Table 35 Table showing the floor area total number of floor and gross floor area of each shopping center

Name of the Shopping Center

Floor Area(Sq feet)

Total number of Floors

Gross Floor Area(Sq feet)

Orchard Point 12956 6 77736

ARA Center 5481 4 21924

Plaza AR 12831 6 76986

Metro Shopping Center

13321 6 79926

Rapa Plaza 13300 6 79800

Adel Plaza 6825 8 54600

372 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 (400 pm- 700 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

Table 36 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)

400pm-415pm 127 8415pm-430pm 144 9430pm-445pm 160 13445pm-500pm 164 16

Table 37 Number of incoming persons and incoming vehicles to the Rapa Plaza in fifteen minutes intervals

Week day Thursday Date 27-10-2011

600pm-615pm 187 15615pm-630pm 151 12630pm-645pm 165 15

645pm-700pm 170 8

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm100110120130140150160170180190200210220

Person Trip Attraction in Day 1 amp Day 2 Variation at Rapa Plaza (at 15 min interval)

Person Trip(in) DAY 1Person Trip(in) DAY 2

Graph 31 graph showing the variation of person trip attraction of day 1 and day 2 at Rapa plaza (at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm02468

1012141618

Variation In Car Trip Atraction of Day 1 amp Day 2at Rapa Plaza (at 15 min interval)

Car Trip(in) DAY 1Car Trip(in) DAY 2

Graph 32 graph showing the variation of car trip attraction of day 1 and day 2 at Rapa plaza (at 15 min interval)

Table 38 Number of incoming persons and incoming vehicles to the Orchard point in fifteen minutes intervals

Table 39 Number of incoming persons and incoming vehicles to Orchard point in fifteen minutes intervals

Week day Friday Date 21-10-2011

600pm-615pm 149 11615pm-630pm 153 14630pm-645pm 162 13

645pm-700pm 136 8

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm60708090

100110120130140150160170180

Person Trip Attraction in Day 1 amp Day 2 Variation at Orchard point (at 15 min interval)

Graph 33 graph showing the variation of person trip attraction in day 1 and day 2 at Orchard Point (at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm02468

101214161820

Variation In Car Trip attraction of Day 1 amp Day 2at Orchard point (at 15 min interval)

Graph 34 graph showing the variation of car trip attraction of day 1 and day 2 at Orchard Point(at 15 min interval)

Table 310 Number of incoming persons and incoming vehicles to the Metro Shopping Mall in fifteen minutes intervals

Week day Monday Date03-10-2011

Week day Friday Date07-10-2011

600pm-615pm 154 4615pm-630pm 157 9630pm-645pm 185 5

645pm-700pm 178 6

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm100

Person Trip Attraction in Day 1 amp Day 2 Variation at Metro Shopping center (at 15 min interval)

Graph 35 graph showing the variation of person trip attraction in day 1 and day 2 at Metro Shopping Mall(at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm0

Variation In Car Trip Attraction of Day 1 amp Day 2at Metro Shopping Mall ( at 15 min interval)

Graph 36 graph showing the variation of car trip attraction of day 1 and day 2 at Metro Shopping mall (at 15 min interval)

Table 312 Number of incoming persons and incoming vehicles to the ARA Center in fifteen minutes intervals

Week day Sunday Date23-10-2011

600pm-615pm 30 4615pm-630pm 22 3630pm-645pm 25 6

645pm-700pm 20 5

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm0

Variation of Person Trip attraction in Day 1 amp Day 2 at ARA Center(15 min interval)

Graph 37 graph showing the variation person trip attraction in day 1 and day 2 at ARA Center (at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm0

Variation In Car Trip Atraction of Day 1 amp Day 2at ARA Center(at 15 min interval)

Graph 38 graph showing the variation of car trip attraction of day 1 and day 2 at ARA Center(at 15 min interval)

Table 314 Number of incoming persons and incoming vehicles to the Plaza AR in fifteen minutes intervals

Week day Thursday Date13-10-2011

600pm-615pm 81 12615pm-630pm 138 7630pm-645pm 140 7

645pm-700pm 125 6

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm60708090

100110120130140150160170180

Variation in Person Trip attraction of Day 1 amp Day 2 at Plaza AR(at 15 min interval)

Graph 39 graph showing the variation of person trip attraction in day 1 and day 2 at Plaza AR (at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm02468

10121416

Variation In Car Trip Atrraction of Day 1 amp Day 2at Plaza AR ( at 15 min interval)

Graph 310 graphs showing the variation of car Trip Attraction Rate day 1 and day 2 at Plaza AR (at 15 min interval)

Table 316 Number of incoming persons and incoming vehicles to the Adel Plaza in fifteen minutes intervals Week day Monday Date 17-10-2011

Table 317 Number of incoming persons and incoming vehicles to the Adel Plaza in fifteen minutes intervals

600pm-615pm 92 5615pm-630pm 88 9630pm-645pm 115 8

645pm-700pm 110 8

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm40

Variation in Person Trip Attraction of Day 1 amp Day 2 at Adel Plaza ( at 15 min interval)

Graph 311 graphs showing the variation of person trip attraction in day 1 and day 2 at Adel Plaza (at 15 min interval)

400pm-415pm

415pm-430pm

430pm-445pm

445pm-500pm

500pm-515pm

515pm-530pm

530pm-545pm

545pm-600pm

600pm-615pm

615pm-630pm

630pm-645pm

645pm-700pm0

14 Variation In Car Trip Attraction of Day 1 amp Day 2at Adel Plaza (at 15 min interval)

Graph 312 graphs showing the variation of car trip attraction of day 1 and day 2 at Adel Plaza (at 15 min interval)

CHAPTER FOUR

ANALYSIS AND FINDINGS

41 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

42 Peak hour trip rate calculation

Six different Shopping center of Dhanmondi areas were studied Data were collected for each

Shopping Center from (4 pm ndash 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

Table 41 Peak hour trip rate calculation both for person and car trip of Rapa Plaza Day 1 Week Day Saturday Date 29-10-2011

Time interval Person Trip(in)

400(pm)

-500(pm

415(pm)

-515(pm)

430(pm)

-530(pm

445(pm)

-545(pm)

500(pm

600(pm

515(pm)

-615(pm

530(pm)

-630(pm)

545(pm)

-645(pm

600(pm)

-700(pm)

400pm-415pm 127

415pm-430pm 144

430pm-445pm 160

445pm-500pm 164

500pm-515pm 182

515pm-530pm 209

530pm-545pm 167

545pm-600pm 173

600pm-615pm 172

706615pm-630pm 154 630pm-645pm 195 645pm-700pm 185

Peak Hour Trip Attraction from (500 PM - 600PM ) 731 TRIP HOUR

Time interval Car Trip(in)

400(pm)

-500(pm

415(pm)

-515(pm

430(pm)

-530(pm

445(pm)

-545(pm

500(pm

600(pm

515(pm)

-615(pm

530(pm)

-630(pm

545(pm)

-645(pm

600(pm)

-700(pm

)400pm-415pm 8

415pm-430pm 9

430pm-445pm 13

445pm-500pm 16

500pm-515pm 11

515pm-530pm 9

530pm-545pm 11

545pm-600pm 13

600pm-615pm 12

65615pm-630pm 9 630pm-645pm 17 645pm-700pm 14

Peak Hour Trip Attraction from (600 PM - 700PM ) 65 CarHr

Table 42 Peak hour trip rate calculation both for person and car trip of Orchard Point

Day 2 Week Day Thursday Date 27-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 122

415pm-430pm 137

430pm-445pm 149

445pm-500pm 159

500pm-515pm 168

515pm-530pm 178

530pm-545pm 157

545pm-600pm 175

600pm-615pm 187

673615pm-630pm 151 630pm-645pm 165 645pm-700pm 170

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 7

415pm-430pm 8

430pm-445pm 9

445pm-500pm 12

500pm-515pm 12

515pm-530pm 13

530pm-545pm 11

545pm-600pm 13

600pm-615pm 15

63615pm-630pm 12 630pm-645pm 15 645pm-700pm 8

Table 43 Peak hour trip rate calculation both for person and car trip of Orchard Point Day 1 Week day Thursday Date 20-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 103

415pm-430pm 101

430pm-445pm 107

445pm-500pm 115

500pm-515pm 130

515pm-530pm 106

530pm-545pm 88

545pm-600pm 97

600pm-615pm 114

491615pm-630pm 133 630pm-645pm 140 645pm-700pm 104

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 8

415pm-430pm 10

430pm-445pm 14

445pm-500pm 10

500pm-515pm 13

515pm-530pm 15

530pm-545pm 19

545pm-600pm 15

600pm-615pm 13

71615pm-630pm 13 630pm-645pm 16 645pm-700pm 14

Day 2 Week day Friday Date 21-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 106

415pm-430pm 127

430pm-445pm 157

445pm-500pm 123

500pm-515pm 146

515pm-530pm 140

530pm-545pm 120

545pm-600pm 109

600pm-615pm 149

600615pm-630pm 153 630pm-645pm 162 645pm-700pm 136

Peak Hour Trip Attraction from (600 PM - 7005PM ) 600TRIP HOUR

Time interval Car Trip(in) 400

500(pm)

415(pm)

-515(pm)

430(pm

530(pm

445(pm)

-545(pm)

500(pm)

-600(pm)

515(pm)

-615(pm)

530(pm)

-630(pm)

545(pm)

-645(pm)

600(pm)

-700(pm)

400pm-415pm 9

415pm-430pm 11

430pm-445pm 14

445pm-500pm 8

500pm-515pm 11

515pm-530pm 9

530pm-545pm 14

545pm-600pm 13

600pm-615pm 11

59615pm-630pm 14 630pm-645pm 13

645pm-700pm 8

Peak Hour Trip Attraction from (545 PM - 645PM ) 65 CarHrTable 45 Peak hour trip rate calculation both for person and car trip of Metro Shopping Mall

Day 1 Week day Monday Date 03-10-2011Time interval Person

Trip(in)400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 127

415pm-430pm 142

430pm-445pm 161

445pm-500pm 136

500pm-515pm 200

515pm-530pm 147

530pm-545pm 148

545pm-600pm 150

600pm-615pm 154

674615pm-630pm 157 630pm-645pm 185 645pm-700pm 178

Peak Hour Trip Attraction from (600 PM - 700 PM ) 674 TRIP HOUR

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 5

415pm-430pm 4

430pm-445pm 7

445pm-500pm 6

500pm-515pm 4

515pm-530pm 8

530pm-545pm 6

545pm-600pm 7

600pm-615pm 4

33615pm-630pm 9 630pm-645pm 5 645pm-700pm 8

Table 46 Peak hour trip rate calculation both for person and car trip of Metro Shopping MallDay 2 Week day Friday Date 07-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 133

415pm-430pm 160

430pm-445pm 203

445pm-500pm 148

500pm-515pm 197

515pm-530pm 166

530pm-545pm 170

545pm-600pm 140

600pm-615pm 190

710615pm-630pm 185 630pm-645pm 170 645pm-700pm 165

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 5

415pm-430pm 7

430pm-445pm 8

445pm-500pm 5

500pm-515pm 6

515pm-530pm 5

530pm-545pm 8

545pm-600pm 8

600pm-615pm 5 35

615pm-630pm 10

630pm-645pm 7 645pm-700pm 5

Table 47 Peak hour trip rate calculation both for person and car trip of ARA Center

Day 1 Week day Sunday Date 23-10-2011Time interval Person

Trip(in)400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 22

415pm-430pm 15

430pm-445pm 24

445pm-500pm 22

500pm-515pm 37

515pm-530pm 23

530pm-545pm 32

545pm-600pm 34

600pm-615pm 27

83615pm-630pm 17 630pm-645pm 23 645pm-700pm 16

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 5

415pm-430pm 3

430pm-445pm 5

445pm-500pm 4

500pm-515pm 9

515pm-530pm 4

530pm-545pm 10 31

545pm-600pm 6 33

600pm-615pm 7

26615pm-630pm 4 630pm-645pm 6 645pm-700pm 3

Peak Hour Trip Attraction from (545 PM - 645 PM ) 33 CarHr

Table 48 Peak hour trip rate calculation both for person and car trip of ARA Center Day 2 Week day Friday Date 28-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 25

415pm-430pm 20

430pm-445pm 30

445pm-500pm 32

500pm-515pm 40

515pm-530pm 35

530pm-545pm 45

545pm-600pm 36

600pm-615pm 30

97615pm-630pm 22 630pm-645pm 25 645pm-700pm 20

500(pm)

415(pm)

-515(pm)

430(pm

530(pm

445(pm)

-545(pm)

500(pm)

-600(pm)

515(pm)

-615(pm)

530(pm)

-630(pm)

545(pm)

-645(pm)

600(pm)

-700(pm)

400pm-415pm 6

415pm-430pm 3

430pm-445pm 7

445pm-500pm 5

500pm-515pm 10

515pm-530pm 7 32

530pm-545pm 10 29

545pm-600pm 5

600pm-615pm 4

23615pm-630pm 3 630pm-645pm 6 645pm-700pm 5

Table 49 Peak hour trip rate calculation both for person and car trip of Plaza AR

Day 1 Week day Thursday Date 13-10-2011Time interval Car

Trip(in)400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 105

415pm-430pm 112

430pm-445pm 117

445pm-500pm 111

500pm-515pm 127

515pm-530pm 86

530pm-545pm 108

545pm-600pm 90

600pm-615pm 81

574615pm-630pm 138 630pm-645pm 140 645pm-700pm 125

Peak Hour Trip Attraction from (600 PM ndash 700 PM ) 574 TripHr

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 7

415pm-430pm 10

430pm-445pm 11

445pm-500pm 1540

500pm-515pm 13 38

515pm-530pm 12

530pm-545pm 13

545pm-600pm 8

600pm-615pm 11

39615pm-630pm 7 630pm-645pm 7 645pm-700pm 6

Table 410 Peak hour trip rate calculation both for person and car trip of Plaza ARDay 2 Week Day Friday Date 14-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 117

415pm-430pm 120

430pm-445pm 135

445pm-500pm 143

500pm-515pm 140

515pm-530pm 145

530pm-545pm 134

545pm-600pm 115

600pm-615pm 164

610615pm-630pm 163 630pm-645pm 145 645pm-700pm 138

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 5

415pm-430pm 8

430pm-445pm 1034

445pm-500pm 11 36

500pm-515pm 13

515pm-530pm 12

530pm-545pm 8

545pm-600pm 9

600pm-615pm 8

42615pm-630pm 10 630pm-645pm 8 645pm-700pm 7

Peak Hour Trip Attraction from (530 PM - 630 PM ) 47 CarHrTable 411 Peak hour trip rate calculation both for person and car trip of Adel plaza

Day 2 Week day Saturday Date 22-10-2011 Time interval Person

Trip(in)400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 70

415pm-430pm 85

430pm-445pm 76

445pm-500pm 80

500pm-515pm 100

515pm-530pm 92

530pm-545pm 95

545pm-600pm 88

600pm-615pm 108

437615pm-630pm 110 630pm-645pm 115 645pm-700pm 104

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 5

415pm-430pm 4

430pm-445pm 7

445pm-500pm 518

500pm-515pm 9 20

515pm-530pm 4

530pm-545pm 7

545pm-600pm 5

600pm-615pm 5

42615pm-630pm 8 630pm-645pm 12 645pm-700pm 12

Table 412 Peak hour trip rate calculation both for person and car trip of Adel plazaDay 1 Week Day Monday Date 17-10-2011

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 63

415pm-430pm 70

430pm-445pm 77

445pm-500pm 83

500pm-515pm 97

515pm-530pm 89

530pm-545pm 81

545pm-600pm 74

600pm-615pm 92

405615pm-630pm 88 630pm-645pm 115 645pm-700pm 110

400(pm

500(pm

415(pm

515(pm

430(pm

530(pm

445(pm

545(pm

500(pm

600(pm

515(pm

615(pm

530(pm

630(pm

545(pm

645(pm

600(pm

700(pm

)400pm-415pm 6

415pm-430pm 4

430pm-445pm 618

445pm-500pm 5 21

500pm-515pm 7

515pm-530pm 9

530pm-545pm 7

545pm-600pm 4

600pm-615pm 5

34615pm-630pm 9 630pm-645pm 8 645pm-700pm 8

43 Trip Attraction Rate calculation and findings

After estimation of peak hour trip rate for each day and each Shopping Center Trip Attraction

Rates Trip Attraction Rate will be calculated The Trip Attraction Rate will be calculated relating

the peak hour trip rate with the physical features of the Shopping Centers

Peak hour person trip attraction rate

(Trips per 1000 sq ft per hour) = (Peak hour person trip Gross Floor Area (GFA))1000

(Trips per shop per hour) = (Peak hour person trip total number of shop)

(Trips per entry gate per hour) = (Peak hour person trip number of entry gate)

(Trips per 100 employees per hour) = (Peak hour person triptotal no of employee of Shopping

Center)100

Peak hour car trip attraction rate

(Trips per 10 parking spaces per hour) = (Peak hour car triptotal number of parking spaces)10

(Trips per 10000 sq ft per hour) = (Peak hour car trip Gross Floor Area)10000

Using the equations Trip Attraction Rates will be calculated for six Shopping Center s and also

for day 1 and day 2 From the Trip Attraction Rate of day 1 and day 2 Average Trip Attraction

Rate will be calculated for each Shopping Center A Grand Average of the Average Trip

Attraction will be calculated After that The Standard Deviation of Average Trip Attraction Rate

of the Shopping Centers from Grand Average will be calculated using the equation

Table 413 Calculation of Peak hour person trip attraction rate (trips 1000 ft2 hour)

Name of the Shopping center

(col 1)

(col 2)

PM peak hour person

trip (in)(col 3)

Gross Floor Area(ft2)(col 4)

PM peak hour person trip attraction rate

(trips 1000 ft2 hour)

col 5=( col 3col4)1000

Orchard Point 1 431 77736 55

2 600 77736 77

Avg 5155 77736 66

ARACenter 1 126 21924 57

2 146 21924 67

Avg 136 21924 62

Plaza AR 1 574 76986 75

2 610 76986 79

Avg 592 76986 77Metro Shopping

Center 1 674 79926 84

2 714 79926 89

Avg 694 79926 87

Rapa Plaza 1 697 79800 87

2 731 79800 92

Avg 714 79800 89

Adel Plaza 1 405 54600 74

2 637 54600 117

Avg 521 54600 95Grand Average of Peak hour person trip attraction rate =79 (trips 1000 ft2 hour) Range = (62 ndash 95) (trips 1000 ft2 hour) Standard Deviation (SD)= 12 (trips 1000 ft2 hour)

From the Table 413 and Graph 41 Adel Plaza (Arong) has been noticed with highest average

peak hour Trip Attraction Rate 95 person trip1000 ft2hr and Orchard point is found with the

least average peak hour Trip Attraction Rate 62 person trip1000 ft2hr Grand Average of Peak

hour person trip attraction rate has been calculated 79 trips 1000 ft2 hour Standard Deviation

(SD) of average peak hour Trip Attraction Rates of different Shopping Centers from Grand

Average is calculated 12 trips 1000 ft2 hour Adel plaza is found with highest person trip per

1000 ft2 area during peak hour From the chart it has been clearly noticeable that day 2 (week

ends) is found with higher Trip Attraction Rate (person trip1000 ft2hr) than day 1 (typical

working day) for six Shopping Centers

Orchard Point

ARACenter Plaza AR Metro Shopping

Center

Rapa Plaza Adel Plaza0

PM peak hour car trip attraction rate (person trip1000 sq feethr) variation for different SC

Day 1Day 2Avg

Graph 41 variation of peak hour Trip Attraction Rate (person trip1000 ft2hr) of different

Shopping Centers

Table 414 Calculation of Peak hour person trip attraction rate (trips 100 employee hour)

( col 1)

( col 2)

Peak hour person trip

( col 3)

Total no of employee

( col 4)

(trips 100 employee hour)

col 5=( col 3col 4)100

2 600 300 200

Avg 516 300 172

ARACenter 1 126 80 158

2 146 80 183

Avg 136 80 170

Plaza AR 1 574 250 230

2 610 250 244

Center 1 674 500 135

2 714 500 143

Avg 694 500 139

Rapa Plaza 1 697 300 232

2 731 300 244

Avg 714 300 238

Adel Plaza 1 405 130 312

2 637 130 490

Avg 521 130 401Grand Average of Peak hour person trip attraction rate = 226 (trips 100 employee hour) Range = (136 to 401) (trips 100 employee hour) Standard Deviation (SD)= 943 (trips 100 employee hour)

Table 414 and Graph 42 have indicated that the variation in peak hour Trip Attraction Rate

(person10 employeehour) of six Shopping Centers Again Adel Plazais found with highest

person trip 10 employees hour with a value of 401 Metro Shopping Mall has been attracted

least Trip Attraction Rate 136 person10 employeehour Plaza A R and Rapa Plaza attract

nearly 238 person trip 100 employee per hour Grand Average of Peak hour person Trip

Attraction Rate of different Shopping Centers is calculated 226 trips 100 employee hour The

Standard Deviation (SD) of average peak hour Trip Attraction Rates from grand average of

different Shopping Centers is found 943 trips 100 employee hour Day 2 has been made

greater Trip Attraction Rate (person10 employeehour) than day 1

Orchard Point

Center

Peak hour person trip attraction rate (person100 employeehr) variation for dif-

ferent shopping centers

Day 1Day 2Avg

Graph 42 variation of peak hour Trip Attraction Rate (person trip100 employeehr) of different

Shopping Centers

Table 415 Calculation of Peak hour person trip attraction rate (trips entry gate hour)

( col 1)

( col 2)

( col 3)

Total no of entry gate

( col 4)

(trips entry gate hour)col 5=( col 3col 4)

2 600 2 3000

Avg 516 2 2578

2 146 1 1460

Avg 136 1 1360

Plaza AR 1 574 2 2870

2 610 2 3050

Avg 592 2 2960

Metro Shopping Center 1 674 2 3370

2 714 2 3570

Avg 694 2 3470

Rapa Plaza 1 697 2 3485

2 731 2 3655

Avg 714 2 3570

Adel Plaza 1 405 2 2025

2 637 2 3185

Avg 521 2 2605Grand Average of Peak hour person trip attraction rate = 276 trips entry gate hour

Range = (136 to 357) trips entry gate hour

Standard Deviation(SD) = 802 trips entry gate hour

Table 415 and Graph 43 have indicated the peak hour Trip Attraction Rate (personentry

gatehr) variation of six Shopping Centers ARA Center has only one entry gate Rest of the

Shopping Centers has two entry gates But ARA Center is found with least peak hour Trip

Attraction Rate value 136 (personentry gatehr) Rapa plaza is found with highest Trip

Attraction Rate 357 personentry gatehr Grand Average of Peak hour person Trip Attraction

Rate of different Shopping Centers is found 226 trips entry gate hour The Standard Deviation

(SD) of average peak hour Trip Attraction Rates from Grand Average of different Shopping

Centers is found 802 trips entry gate hour

Orchard Point

Center

Peak hour trip attraction rate (personentry gatehr) variation for different shopping malls

Day 1Day 2Avg

Graph 43 variation of peak hour Trip Attraction Rate (person tripentry gate hr) of different

Shopping Centers

Table 416 Calculation of Peak hour person trip attraction rate (trips shop hour)

Name the Shopping center

( col 1)

( col 2)

( col 3)

Total no of shops

( col4)

(trips shop hour)

col 5=( col 3col4)

2 600 80 75

Avg 516 80 64

2 146 38 38

Avg 136 38 36

Plaza AR 1 574 75 77

2 610 75 81

Center 1 674 165 41

2 714 165 43

Avg 694 165 42

Rapa Plaza 1 697 85 82

2 731 85 86

Avg 714 85 84

Adel Plaza 1 405 10 405

2 637 10 637

Avg 521 10 521Grand Average of Peak hour person trip attraction rate = 14 trips shop hour

Range = (36 to 521) trips shop hour

Standard Deviation(SD) = 188 trips shop hour

Table 416 and Graph 44 have indicated the peak hour Trip Attraction Rate (trips shop hour)

variation of six Shopping Centers ARA Center has produced least peak hour Trip Attraction

Rate value 36 trips shop hour Adel plaza has made highest Trip Attraction Rate 521 trips

shop hour Grand Average of Peak hour person Trip Attraction Rate of different Shopping

Centers is found 14 trips shop hour The Standard Deviation (SD) of average peak hour Trip

Attraction Rates of different Shopping Centers from Grand Average is found 188 trips shop

Orchard Point

Center

Peak hour person trip attraction rate (personshophr) variation for different SCs

Day 1Day 2Avg

Graph 44 variation of peak hour Trip Attraction Rate (person tripshophr) of different SCs

Table 417 Calculation of peak hour car trip attraction rate (trips 10000 ft2 hour)

(Col1)

Day( col 2)

Peak hour car trip (in)( col 3)

Gross Floor Area(ft2)

( col4)

(trips 10000 ft2 hour)col 5=( col 3col 4)10000

2 65 77736 84

Avg 71 77736 91

ARACenter 1 33 21924 151

2 32 21924 146

Avg 33 21924 148

Plaza AR 1 47 76986 61

2 51 76986 66

Center 1 38 79926 48

2 34 79926 43

Avg 36 79926 45

Rapa Plaza 1 65 79800 81

2 67 79800 84

Avg 66 79800 83

Adel Plaza 1 42 54600 77

2 34 54600 62

Avg 38 54600 70Grand Average of Peak hour car trip attraction rate = 83 (trips 10000 ft2 hour)

Range = (45ndash 148) (trips 10000 ft2 hour)

Standard Deviation(SD) = 35 (trips 10000 ft2 hour)

Table 417and Graph 45 have indicated that the variation in peak hour Car Trip Attraction Rate

(trips 10000 ft2 hour) of six Shopping Centers Again Metro Shopping Mall has least (trips

10000 ft2 hour) with a value of 45 ARA Center has the highest Trip Attraction Rate 148 trips

10000 ft2 hour Grand Average of Average Peak hour car Trip Attraction Rate of different

Shopping Centers is calculated 83 car trips 10000 ft2 hour The Standard Deviation (SD) of

average peak hour Trip Attraction Rates of different Shopping Centers from grand average is

found 35 trips 10000 ft2 hour

Orchard Point

Center

Peak hour car trip attraction rate (car10000 ft2hr) variation for different shopping malls

Day 1Day 2Avg

Graph 45 variation of peak hour car Trip Attraction Rate(car trip100000 ft2hr) of different

Shopping Centers

Table 418 Calculation of Peak hour car trip attraction rate (trips 10 parking spaces hour)

( col 1)

( col 2)

Peak hour car trip (in)

( col 3)

Total no of parking spaces

( col 4)

(trips 10 parking spaces hour)

2 65 60 108

Avg 71 60 118

2 32 20 160

Avg 33 20 163

Plaza AR 1 47 40 118

2 51 40 128

Center 1 38 65 58

2 34 65 52

Avg 36 65 55

2 67 90 74

Avg 66 90 73

Adel Plaza 1 42 20 210

2 34 20 170

Avg 38 20 190Grand Average of Peak hour car trip attraction rate = 12 (trips 10 parking spaceshour)

Range = (55ndash 190) (trips 10 parking spaceshour)

Standard Deviation(SD) = 51 (trips 10 parking spaceshour)

Table 418 and Graph 46 have indicated that the variation in peak hour Car Trip Attraction

Rate (trips 10 parking spaceshour) of six Shopping Centers Again Metro Shopping Mall has

found least (trips 10 parking spaceshour) with a value of 45 ARA Center has found the

highest Trip Attraction Rate 148 trips 10 parking spaceshourGrand Average of Average Peak

hour car Trip Attraction Rate of different Shopping Centers has calculated 12car trips 10

parking spaceshour The Standard Deviation (SD) of average peak hour Trip Attraction Rates

of different Shopping Centers from grand average has found 51 car trips 10 parking

spaceshour

Orchard

Plaza A

Rapa P

Adel P

Peak hour car trip attraction rate (car10 parking spaceshr) variation for different shopping malls

Day 1Day 2Avg

Graph 46 variation of peak hour Trip Attraction Rate (car trip10 parking spaces hr) of

different Shopping Centers

CHAPTER FIVE

CONCLUSION

51 Summery of Findings

The shopping trips constitute the second largest share of trips after the work trips in Dhaka The

size of a shopping center is growing in Dhaka city Further the size of a Shopping Center has

become very large eg Rapa Plaza Metro Shopping Mall and Bashundhara city hence the travel

pattern in the region is greatly affected by the size and activities of the Shopping Center This

study has collected data on trip attraction rates of Shopping Centers in Dhanmondi area Dhaka

city and the trip attraction rate was calculated as a function of physical features of the Shopping

Centers This report has showed a Trip Attraction data obtained at 6 shopping centers in

Dhanmondi Dhaka

The trip rate analysis method relates the Trip Attraction of the Shopping Center at peak period to

the physical features of the Shopping Center The method confirms that the gross floor area of

Shopping Center the number of stores number of employee the number of parking spaces have

relevant impact on the Trip Attraction Rate of the Shopping Center and thus average Trip

Attraction Rates are calculated

Grand Average of peak hour person trip attraction rate 79 person trips 1000 ft2 hour Ranges

values (From 62 to 95) trips 1000 ft2 hour for different shopping centers and with a Standard

Deviation (SD) of 12 (trips 1000 ft2 hour)

Grand Average of peak hour person trip attraction rate is found 226 (trips 100 employee hour)

Ranges ( from136 to 401 ) trips 100 employee hour and with a Standard Deviation (SD) of

943 (trips 100 employee hour)

Grand Average of peak hour person trip attraction rate 14 trips shop hour Ranges values

(minimum 36 trips shop hour to maximum 521 trips shop hour) for different shopping

centers and with a Standard Deviation (SD) of 188 trips shop hour

Grand Average of peak hour person trip attraction rate 276 trips entry gate hour Ranges

values (minimum 136 trips entry gate hour to maximum 357 trips entry gate hour ) for

different shopping centers and with a Standard Deviation (SD) of 802 trips entry gate hour

Grand Average of peak hour car trip attraction rate 83 car trips 10000 ft2 hour Ranges

values (minimum 136 car trips 10000 ft2 hour to maximum car trips 10000 ft2 hour) for

different shopping centers and with a Standard Deviation (SD) of 35 car trips 10000 ft2

Grand Average of peak hour car trip attraction rate 276 car trips 10 parking spaces hour

Ranges values (minimum 136 car trips 10 parking spaces hour 357 car trips parking spaces

hour) for different shopping centers and with a Standard Deviation (SD) of 51 car trips 10

parking spaces hour

A good estimate of Trip Attraction Rate is fundamental in planning of transportation facilities It

would be useful in the regional transportation network or the channelization of traffic control

around a shopping center The data would be useful in assessing the traffic impacts surrounding a

new shopping center and also the region wide traffic volume impacts

52 Further Research

1048714 Continuous data collection with the goal of developing a larger database that includes at least

five data points for up to ten land use categories This will provide enough data to perform a

reasonable statistical analysis and to correlate the data

1048714 Use of the optional demographic data to cross-reference trip attraction to income auto

ownership and other socio-economic factors

1048714 Develop additional indicators correlating trip attraction rates to urban area characteristics such

as distance to the Central Business District walking environment number of on-site parking

spaces and distance to transit