Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this document? Why not share!

- Service Quality Dimension of Teletalk by Shakhawatul Islam... 1863 views
- Balance scorecard by Teletalk Banglade... 3422 views
- Ridership Forecast by Wali Memon 367 views
- My summer vacation with Eric Plasencia by University Of Pue... 463 views
- Advanced Performance Measurement Wo... by Taryn Soltysiak 1788 views
- Philippines Airlines balance scorecard by Jesse Eman 14257 views

2,438 views

2,294 views

2,294 views

Published on

No Downloads

Total views

2,438

On SlideShare

0

From Embeds

0

Number of Embeds

11

Shares

0

Downloads

82

Comments

0

Likes

2

No embeds

No notes for slide

- 1. CHAPTER ONEINTRODUCTION1.1 BackgroundThese days, the transportation planning issues faced by most Asian cities include rapidurbanization and motorization which is leading to sharp increase in travel demand whereas, thesupply has largely remained unmatched with demand. So, trip generation and trip attraction isimportant to the traffic engineer and planner in considering the impact of new development such asoffice complex, shopping centre and residential development. New development leads avarious impact to the people‟s daily activities. For example the impacts of surroundingroadway network tend to make people moving far from one place to another place. Road lengthis increasing and road network patterns change according to the accessibility needs of peopleand desire to reach their destinations. Hence, new development will increase the travel demandwith there also increase the vehicles.Trip attraction is obviously most pertinent relative to traffic at specific land use activity. It alsoplays a role in many phases of transportation planning and traffic engineering related activities. It isthe part of trip generation in the travel-forecasting process. It involves the estimation of the totalnumber 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 themovement of both people and goods within an urban area. At the individual level, urbantransportation can be characterized by a trip. However, at the metropolitan area level, millions ofthese individual trips define urban transportation.A trip as a journey made by an individual between two different points. Each trip is performedusing one or multiple transportation modes for a defined purpose at a given time. Although atrip 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 tripsthat will be made by individuals to work, shopping, school, and so forth, but not the flowsbetween points within the whole system. The functioning of metropolitan cities is highlydependent on the movement of people, goods and information (Muller, 1995) and trip attraction1
- 2. studies are a vital part of transportation planning, due to the recursive nature of urbantransportation 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. Amongall trip purposes, work trips are the most numerous, followed by shopping trips (Vickermanand Barmby, 1984), which count approximately for 40% of all trips generated in NorthAmerican metropolitan areas (Barber, 1995). A very few studies have been done in aboutshopping trip attraction in Dhaka city. This study focuses on shopping trip attraction inDhaka city area specially in Dhanmondi area.1.2 Problem StatementThe Trip Attractions Rate of a shopping center (Shopping Center) is influenced by a number offactors, including time of the day, day of the week, seasonality, weather, configuration andcomposition of the Shopping Center. Peaking is caused by business and social characteristics. Themost typical time for shopping during the weekday is after work, in particular, 4 to 7 PM onFridays attracts the most number of customers on a weekday. In addition Saturdays andSundays 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 CenterShopping Center even during the same time period over different fifteen-minute intervals. Thisvariation 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. Asa result, the sample size becomes very important particularly when significant fluctuationsexist in the number of trips to the Shopping Center. The need for a large sample space and thehighly inconsistent nature of the Trip Attraction makes the estimation of Trip Attractions Rate ofthe Shopping Center a very complex process.The ITE handbook has been the main reference material in the transportation planningcommunity when estimating Trip Attraction of an activity center. Although the ITE TripGeneration Manual (1997) is a concise and easy to use reference, the models for ShoppingCenter do not consider some of the features of Shopping Center Shopping Center, such as the2
- 3. number of stores, the number of the parking spaces, and the location of the Shopping Centersthat can have significant influence on the Trip Attraction Rate of the Shopping Centers. As aresult, the Trip Attraction Rate estimated cannot be made specific to a Shopping Center. On theother hands, The ITE Trip Generation Manual (1997) offers the Trip Attraction Rate formany different types of establishment or stores, when the establishment is freestanding. In otherwords, there is no differentiation between the stores independently located or located in aShopping Center along with other stores. The phenomenon of trip chaining, in which a customervisits more than one store during one trip to a Shopping Center, has to be taken into account forthe estimation of the TA in a Shopping Center.It is difficult to consider all the factors influencing the Trip Attraction Rate of Shopping Centerespecially factors like land use characteristics of the surrounding area. However other factors likethe physical features of the Shopping Center that are easy to measure and analyze should beincorporated in the estimation of the Trip Attraction Rate. In addition to this the generalprocedures for estimating Trip Attraction Rate of the Shopping Center do not consider theeffect of the type and features of the constituent stores of a Shopping Center. The TA of theconstituent stores in a Shopping Center affects the level of trip chaining in the Shopping Center. Itis very vital to involve the trip-chaining phenomenon in the estimation of Trip Attraction Rate ofthe Shopping Center. The above-mentioned points form the basis for undertaking this study.1.3 Aim and ObjectivesThe aim of this study is to determine the trip attractions of shopping centers in the Dhanmondiarea of Dhaka city. Through the trip attraction analysis, we can determine the attraction of theshopping trip among the shopping center in Dhaka city. Then, the travel demand can be estimatedfrom 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.3
- 4. 1.4 Study outcomeThis research is intended to provide empirical trip attraction data for use in transportationplanning and traffic engineering studies for urban areas throughout Dhaka city. This study alsoprovides the foundation for subsequent research to be conducted, local agencies, and/orprivate organizations to further build a comprehensive urban trip attraction database of shoppingcenters.The most applicable outcome of this study is the production of quantitative information ontravel characteristics of urban land uses like shopping centers that can be used in traffic impactstudies. This research is intended to establish a standardized data collection and analysismethodology, which will result in consistent information gathering in the future1.5 Report OrganizationThe subsequent chapters of this report are organized as followsChapter 2 - Defines trip attraction, discusses current trip attraction usage, and presents sources oftrip attraction data and relevant trip attraction research.Chapter 3 - contains concept of trip rate analysis method and study data. Data collection andsurvey are explained in detail. Data of trip attraction are gathered to be analyzed. Discussesthe different data collection methods considered for this study and their challenges. Thischapter provides an overview of the sites surveyed in the “initial pilot” study (used to test thechosen survey methodology), and presents an evaluation of the study sites and theirsurrounding contextChapter 4 the empirical results are presented and analyzed. Trip attraction for theDhanmondi area is presented in a form of tables and figures. The Trip Attraction Rate (TripAttraction Rate) using Trip Rate Analysis Method is represented and applied.Chapter 5 - Discusses the findings of the surveyed sites in brief.4
- 5. CHAPTER TWOLITERATURE REVIEW2.1 IntroductionThe transportation planning process relies on travel demand forecasting. Trip generation hasbeen identified as the first and most important step of the conventional sequentialforecasting procedure (Soslau et al., 1978). Trip generation is calculated by trip production andtrip attraction. Even with the exploration of new generation travel demand models such asactivity-based models, the traditional four-step procedure remains the most widely usedmodel by transportation planning agencies because of institutional and financial requirements(McNally, 2000). So, it is important to this study to understand the fundamental of tripgeneration and trip attraction in order to determine the shopping trips attraction in Dhaka city. Inaddition to that overview of shopping system in Bangladesh generally can picture the situation inthe Dhaka city. Furthermore, concepts of the trip attraction modeling and trip attractionanalysis 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 traveldemand forecasting model. The general form of the model is depicted in Figure 2.1. Thereare four basic phases in the traditional travel demand forecasting process which is tripgeneration, trip distribution, modal choice and trip assignment. The last section discussed theprevious study regarding trip generation in Provo, Utah and Texas.5
- 6. Figure 2.1: The Classic Four-stage Transport Model6
- 7. Trip generation is the process by which measures of urban activity are translated intonumbers of trips. For example, the number of trips that are generated by a shopping center isquite different from the number of trips generated by an industrial complex that takes upabout the same amount of space. In trip generation, the transport engineers and plannersattempt to quantify the relationship between urban activity and travel. The inventory datadiscussed earlier is the analysts input for trip generation analysis. Surveys of travelers in thestudy 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, givenforecasts of population and other urban activity.After trip generation, the analyst knows the numbers of trip productions and trip attractions eachzone. Trip distribution procedures determine where the trips produced in each zone will go andhow they will be divided among all other zones in the study area. The output is a set of tablesthat show the travel flow between each pair of zones. The decision on where to go is representedby comparing the relative attractiveness and accessibility of all zones in the area. A person ismore likely to travel to a nearby zone with a high level of activity than to a distant zone with alow level of activity. There are several types of trip distribution analyses which are theFratar method, the intervening, opportunity model, and the gravity model.In modal choice of travel demand forecasting, the phase analyze peoples decisions regardingmode of travel (auto, bus, train, etc). In the travel demand forecasting process, mode usagecomes after trip distribution. However, mode usage analyses can be done at various points inthe forecasting process. Mode usage analyses are also commonly done within trip generationanalyses. The most common point is after trip distribution, since the information on where tripsare going allows the mode usage relationship to compare the alternative transportationservices competing for users. Trip assignment is the procedure by which the planner predicts thepaths the trips will take. For example, if a trip goes from a suburb to downtown, the modelpredicts which specific roads or transit routes are used. The trip assignment processbegins by constructing a map representing the vehicle and transit network in. the study area. Thenetwork maps show the possible paths that trips can take.7
- 8. The rationale of trip generation modeling is to determine the number of vehicle or persontrips to and from zones under consideration. Trip generation modeling consists of two typesof models which is trip-production and trip attraction. Trip production is defined as the home endof home-based (HB) trips or as the origin of a non home-based (NHB) trip while trip attraction isdefined as the non home end of a HB trip or the destination of a NHB trip. A trip is oftendefined as a single journey made by an individual between two points by a specified or combinedmodes of travel and for a defined purpose. Thus, trip generation analysis is the key toobtaining future trip ends by zones. The basic procedure is first, to relate survey-reported tripmaking to household characteristics and land use types by zone through regression or factoranalysis using single variable or multi-variable approaches. The equation thus derived may thenbe applied to forecast land use data.A trip is a one-way person movement by a mechanized mode of transport, having two tripends, an origin (the start of the trip) and a destination (the end of the trip). Trips are usuallydivided into home-based and non-home-based. Home-based trips are those having one end of thetrip (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, itcan 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 anda trip from work to home will both have a production end which is home and an attraction endwhich is work. For non home-based trips, the production end is the origin and the attraction endis the destination.8
- 9. Figure 2.2: The Relationship between O/D and Production and AttractionOnce the study area has been broken into zones, the next task involves quantifying thenumber of trips that each zone will produce or attract. The number of trips to and from an areaor zone is related to the land use activities of the zone and the socioeconomic characteristicsof the trip makers. There are at least three characteristics of land use and trip-makers thatare important. The density or intensity of the land use is important. Many studies begin bydetermining the number of dwelling, employees, or tenants per acre. The intensity can berelated to an average number of trips per day, based on experience with the type of land useat hand. Next, the social and economic character of the users can influence the number of tripsthat are expected. Character attributes like average family income, education, and carownership influence the number of trips that will be produced by a zone. Finally, location playsan important role in trip production and attraction. Street congestion, parking, and otherenvironmental attributes can increase or decrease the number of trips that an area produces orattracts.9
- 10. 2.1.1 Trip Production and Trip AttractionA trip is a movement of a person from one place (origin) to another (destination). Trip productionrepresents a trip starting or ending in a residential area, since a trip is considered “produced” at aperson‟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 tripproductions and two trip attractions. Figure 2.3 Trip productions and trip attractionsA trip-end is the point at which a given trip starts or terminates; one trip has two trip ends. TheTA or the trip production “rate” is defined as the number of trip ends per unit time per unit ofindependent variables (per employee, per square feet of floor area, etc.). Most typically, however, itrefers to the number of trips per day per activity center.2.1.2. Types of tripsSome basic definitions are appropriate before we address the classification of trips in detail. Wewill attempt to clarify the meaning of journey, home based trip, and non home based trip, tripproduction, trip attraction and trip generation. Journey is an out way movement from a point oforigin to a point of destination, where as the word trip denotes an outward and return journey. Ifeither origin or destination of a trip is the home of the trip maker then such trips are called homebased trips and the rest of the trips are called non home based trips. Trip production is defined asall the trips of home based or as the origin of the non home based trips10
- 11. Figure 2.4: Trip TypesTrips can be classified by trip purpose, trip time of the day, and by person type. Trip (attraction andproduction) models are found to be accurate if separate models are used based on trip purpose. Thetrips 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 educationtrips are often referred as mandatory trips and the rest as discretionary trips. All the above trips arenormally home based trips and constitute about 80 to 85 percent of trips. The rest of the tripsnamely non home based trips, being a small proportion are not normally treated separately. Thesecond way of classification is based on the time of the day when the trips are made. Thebroad classification is into peak trips and of peak trips. The third way of classificationis based on the type of the individual who makes the trips. This is important since the travelbehavior is highly influenced by the socio economic attribute of the traveler and arenormally categorized based on the income level, vehicle ownership and house hold size.11
- 12. 2.1.3 Factors Affecting Trip attractionThe 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 alsoused. 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 issignificant. Freight trips are influenced by number of employees, number of sales and area ofcommercial firms.2.1.4 Shopping Trip Attractions2.1.4.1 Shopping mall employee and shop numberShopping 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. Theseare done by surveys.2.1.4.2 Gross floor area of shopping mallArea of shopping mall is a factor readily available from maps. Shopping mall gross floor area is amore reliable indicator of shopping trip attraction which is calculated by multiplying the onefloor area and total number of floor,2.1.4.3 Number of parking spacesNumber of parking spaces available for car parking is another important factor for estimating TripAttraction Rate .2.1.4.4 Traffic Analysis ZoneThe location of shopping mall also effects the trip attraction of individual Shopping Center. Atraffic analysis zone is the unit of geography most commonly used in conventional12
- 13. transportation planning models. The size of a zone varies, but for typical metropolitanplanning software, a zone of under 3000 people is common. The spatial extent of zonestypically varies in models, ranging from very large areas in the exurb to as small as city blocks orbuildings in central business districts. There is no technical reason why zones cannot be assmall as single buildings, however additional zones add to the computational burden. Figure 2.5: Example of TAZ BoundariesZones are constructed by census block information. Typically these blocks are used intransportation models by providing socio-economic data. States differ in the socio-economicdata that they attribute to the zones. Most often the critical information is the number ofautomobiles per household, household income, and employment within these zones. Thisinformation helps to further the understanding of trips that are produced and attracted withinthe 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."13
- 14. 2.2 Trip Attraction AnalysisThere are several general alternative structures for specifying Trip Attraction, which are: i. Cross-Classification ii. Regression Analysis iii. Trip Rate Analysis Method2.2.1Cross-ClassificationThe Cross-Classification Analysis has become most widely accepted. This procedure providesthe planner or the highway engineer a basic model structure. This structure can be altered forlocal situations by substituting or adding variables. There are separate recommended modelstructures for each type of trip as trip productions, trip attraction and internal-external tripgenerations.2.2.2 Trip Attraction Model StructureIn order to analyze trip attractions, the number of trips attracted to certain activities is relatedto a measure of the amount of that activity. For example, the number of trips attracted mightbe related to the number of employees in a factory or the number of employees in a store. Thestructure of the trip attraction model relates trip ends by purpose to the amount, character, andin some cases location of the activities as shown below: Table2.1: Trip Attraction Table14
- 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 otherretail employment classification.A considerable amount of research and development has focused on the area of disaggregatemodels for improved travel demand forecasting. The difference between the aggregate anddisaggregate techniques is mainly in the data efficiency. Aggregate models are usually based uponhome interview origin and destination data that has been aggregated into zones; then the“average” zonal productions and attractions are derived. The disaggregate approach is basedon large samples of household types and travel behaviors and uses data directly. There are savingsin 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 easilyunderstood.2.2.3. Regression analysisRegression analysis is a technique used for the modeling and analysis of numerical dataconsisting of values of a dependent variable (response variable) and of one or more independentvariables (explanatory variables). The dependent variable in the regression equation ismodeled as a function of the independent variables, corresponding parameters (“constant”), andan error term. The error term is treated as random variable. It represents unexplained variation inthe dependent variable. The parameters are estimated so as to give a “best fit” of the data. Mostcommonly the best fit is evaluated by using the least squares method, but other criteria havealso 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 randomvariable, with a mean of zero. Systematic error may be present but its treatment is outside thescope of regression analysis.• The independent variable is error-free. If this is not so, modeling should be done usingerrors-invariables model techniques.• The predictors must be linearly independent, i.e. must not be possible to express anypredictor as a linear combination of the others.15
- 16. • The errors are uncorrelated, that is, the variance-covariance matrix of the errors isdiagonal 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 beused.In linear regression, the model specification is that the dependent variable, yi is a linear combinationof the parameters (but need not be linear in the independent variables). For example, in simplelinear regression for modeling N data points there is one independent variable: xi and twoparameters, 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 andobtain 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. Thismethod 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 ofsimultaneous linear equations in the parameters, which are solved to yield theparameters estimators, β0, and β1. See regression coefficients for statistical properties of theseestimators. 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 linearleast 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 that16
- 17. variance is given by:.This is called the root mean square error (RMSE) of the regression. The standard errors of theparameter estimates are given Under the further assumption that the population error term is normallydistributed, the researcher can use these estimated standard errors to createconfidence intervals and conduct hypothesis tests about the population parameters .2.2.4 General Linear Data ModelIn 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 canbe written as:In any case once a regression model has been constructed, it is important to confirm thegoodness 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 ofresiduals and hypothesis testing. Statistical significance is checked by an F-test of the17
- 18. overall fit, followed by t-tests of individual parameters. Table 2.2: Description of the Statistic Test18
- 19. 2.2.5 Multiple Regression AnalysisMultiple regression analysis is based on trip generation as a function of oneor more independent variables. He approach is mathematical and all of the variables are consideredrandom, and with normal distribution. Multiple regression analysis is relatively simpleproductions and attractions are coupled with data about the area that is though to impact theproduction and attraction of trips. For instance, the total population is believed to impact thenumber of trips produced. If we know the number of trips produced and the population for thepresent and a few time periods in the past, it is possible to develop a relationship betweenthese parameters using statistical regression. Once we are satisfied with the relationship thathas been developed, we can extrapolate into the future by plugging the future population into ourrelationship and solving for the number of productions. The process is called MultipleRegression, because there are normally several variables that impact trip productionand 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 coefficients19
- 20. 2.2.6 Trip Rate Analysis ModelTrip rate analysis model are based on the determination of the average trip production or attractionrates associated with important trip generator or attractor within the region. An example of thismethod is given below.Table: Rates of Trip Attraction in the Sample Shopping Centers using Trip Rate Analysis model20
- 21. 2.3 Previous Study Regarding Trip AttractionStudies on trips and vehicular attraction to land uses have been conducted in most westerncountries but few for Asian conditions especially in the Bangladesh. There has been limited studydone yet at present to study the trips being attracted to commercial buildings2.3.1 Strategic Transport Plan for DhakaIn 2004, a project was undertaken by the Government of Bangladesh with the help of World Bankto prepare a long term Strategic Transport Plan (STP) for the Dhaka Metropolitan Area. As a partof the STP project, an urban transport planning model (UTP Model) was developed and used toforecast future travel demand resulting from different land use scenarios and transport strategiesand to predict the performance of the existing, committed and alternative development strategiesfor Dhaka‟s urban transport network infrastructure, services and policies (STP, 2005). The UTPmodel developed in the STP study (STP, 2005) has some critical weaknesses in assumptions,principles, framework and methods. The following considerations, used in the modelThe model uses a three-dimensional matrix balancing to produce balanced matrices that satisfytrip productions and attractions and trip length frequency distributions. These distributions areproduced for trips by each Trip Purpose and by members of each Household Income Group. Thetrip purposes are Home to Work , Home to Education, Home to Other ,Non-Home Based Triplength frequency distributions for motorized person-trips for each Trip Purpose weredetermined from data obtained from the Household Interview Survey. The trip generationproductions and attractions predicted for the Traffic Analysis Zone (TAZ) together with thetrip length frequency distributions determined from the complete Household Interview Surveyresults were input to the Three Dimensional Matrix Balancing module which created triporigin and destination matrices for each of the four trip purposes and three Household IncomeGroups. 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-tripsidentified by the Household Interview Survey from TAZ of origin to TAZ of destination.21
- 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 partlybecause 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 aslive elsewhere and it is assumed that employment will be provided to accommodate thepopulation. 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 didnot attempt to derive any trip attraction model for zones by using any conventional variables onthe ground of their unavailability. Instead, for trip attractions, the number of total trip productionsfor the study area was allocated to TAZs based on TAZ population modified by judgment of thesignificance 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 productionused trip rates and household number and prediction of trip attraction used population datamaking a large difference between production and attraction. Since UTP model could not make itstrip attraction analysis based on land use category of Dhaka, the Land Use Scenario predicted bySTP 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 theextensive growth of a mixed land use pattern, the heterogeneous socio-economic structure andtravel behaviors and the mixed transportation system. A comprehensive strategic modelingframework, Dhaka Strategic Transport Model (DSTM), is developed and implemented to simulatethe travel demand behavior of Dhaka city. It comprises a system of models for simulating thetravel behavior by applying the four-step modeling process. The framework is developed based ona number of principles such as having strategic perspective; being market oriented; addressingheterogeneous demand characteristics; following disaggregate approach and capturing the multi-modal nature of the transport system of Dhaka.. The complexity of the travel demand situation of22
- 23. Dhaka is addressed by following a simple, modular and flexible structure so that individual modelelements can be refined, enhanced and applied easily.Trip attraction model of DSTM is based on a GIS map containing land use features classified ascommercial, mixed, public and housing area and the number of the stories of the buildings foreach land use feature. An assessment of gross floor area (GFA) of different land use features hasbeen made. From the survey and literature review trip rates have been established. Gross floorarea 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 constructinga simple linear regression equation whose explanatory variables are each correspondingpopulation (no constant term). Also the prediction models for each low-, medium-, high-incomestrata 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 PopulationTrip 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, tripgeneration/attraction was forecasted. According to the results of forecasts in all trip purpose, allincome group, trip generation/attraction will significantly increase in eastern fringe area locatedin the border area of DCC and DMA23
- 24. 2.3.4 Integrated Environmental Strategies (IES) Study in Hyderabad, IndiaIES (2004) study developed a 4-step transport demand model for Hyderabad, which is one of thefastest growing centers of urban development in India. The massive growth of the city hasbrought with it air quality and congestion problems. For various reasons, motorized two wheelers,auto rickshaws and private passenger cars, have displaced trip making traditionally accomplishedby public transport and bicycle. Traffic congestion, the predominance of two-stroke vehicles inthe traffic mix and inability of public transport to attract significant ridership have all beenconsidered responsible for the severe air quality problems in Hyderabad. The objective of thisstudy was to perform analysis policies to address these important issues in Hyderabad‟s transportsector. The study process consisted of development of models, forecast of future travel demandand analysis of alternative strategies for handling the demand. In this particular study, an attemptwas made to develop operational models using normally available variables, which can beforecasted with reasonable degree of accuracy. The standard and easily available planningvariables at zonal levels such as population, employment, number of workers residing, number ofstudents residing and student enrollment, etc. collected as a part of household survey andsecondary data collected were used in the analysis.Regarding trip attraction part, various trip attraction equations or models were developed forwork, education and other purposes by relating the purpose wise trips attracted to zone withindependent variables such as zone wise employment, student enrollment, and distance fromCBD, accessibility rating and population. Work trips attracted to a zone were found to besignificantly related to the zone wise employment, while zone wise student enrollment was founda significant variable in estimating education trips attracted to a zone. Employment andaccessibility rating were found to be most significant in estimating one-way other purpose tripsattracted to each zone. Accordingly, the most significant trip attraction models were used alongwith projected values of the selected independent variables for estimating future year zone wisetrip productions and attractions. The model predicts 5.45 million daily home-based inter-zonaltrips 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, DelawareThis report presents the trip attraction rates of the shopping centers in Northern New CastleCounty 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, atotal of eighteen shopping centers were surveyed, for which the number of vehicles entering andleaving the shopping center in every fifteen minutes interval and the number of people visitingeach store in the shopping center along with their movement patterns were measured. Based onthe surveyed data and the aerial photographs, two approaches, microscopic and macroscopic, aredeveloped to compute the trip attraction rate. The microscopic approach deals with therelationship between the trip attraction rates of individual stores and the shopping center as awhole. The macroscopic approach relates the trip attraction of the shopping center as a function ofthe physical features of the shopping center, e.g. total parking spaces, total floor area, and thenumber of stores in the shopping center. The study shows that microscopic approach gives abetter estimate of trip attraction compared with the macroscopic approach. The proposed modelsincorporate the factors that have been neglected in ITE Trip Generation Manual. These modelsshould be useful for estimating the traffic volume to/from a new shopping center which, is beingplanned and to assess the traffic impact of the shopping center on the geometric design ofroadways in the surrounding area. The report consists of the description of the analyticalapproach, survey methods, the data collected from the survey and the analysis of the data usingthe models proposed.2.3.6 Trip Attraction Development Statistical Modeling Dohuk City Residential AreaAccording to Khalik & Taher, trip attraction phenomenon was studied for 20 residential out of 28traffic 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 datacollected to perform the trip attraction analysis .Attraction trips were classified into seven typesand 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 most25
- 26. statistically well accepted predicted attraction trip models. Some models like HBW trips areconstant eliminated with good (R2 ) value. HBSH and HBOH trips are showing weak correlationwith their independent variables like amount of CBD area and number of retail sales locatedwithin CBD area.2.3.7 Deciding where to shop: disaggregate random utility destination choice modeling ofgrocery shopping in canton ZurichAxhausen and Bodenmann (2008) developed the choice process of individuals deciding where toshop for groceries. Two main datasets were used: a trip dataset from the Micro-census forSwiss travel behavior 2005 in which all locations were geo-coded; and a store attributedataset compiled by the author from various sources. Choice sets were generated based to thetravel time budget of individuals and a random sample with a fixed number of alternatives wasdrawn for the discrete choice modeling process. Multinomial logit (MNL) models were run fortrips by car and by walking.For the car model, the most relevant store attributes in the destination choice process werefound to be the store size and distance required to reach the store in home and non-homebased trips. Regarding socio-demographic characteristics of the individuals, income andhousehold size were found to drive the decision over age and gender.Given the number of available observations, (810 identified shopping trips locations for allmodes, 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 wereestimated, one for trips by car, and another one for trips done by walking. The results for the CARmodel 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 inattracting more customers.Contrary to the CAR model, the value of the parameter for the WALK model results in thedistance from home to shop as an (insignificant) incentive to shop for groceries. It is against26
- 27. common sense to believe people will prefer to travel further away from home to shop forgroceries, especially when they have to walk to the store. A possible explanation for this resultin the model is the store size, where people might walk past by many small stores with alimited offer of goods to shop in larger stores where the offer might be larger. Anotherexplanation might be linked to the data: many very small stores, which have none, or in thebest case, just a few shopping destinations attached to them, were included in the choice set. Atotal 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 forsimpler models with linear interactions between the socio-demographic terms and the storeattributes, rather than for more complex models with non-linear interactions. This resultmight 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 AnalysisBhat et al. formulated and estimated disaggregate attraction disaggregate attraction-end choicemodel that will facilitate the replacement of the aggregate trip attraction models and aggregatetrip distribution model currently used by most metropolitan planning organization. The researchestimated attraction end model for two work purpose: home based work and home basedshopping and personal business. Six sets of explanatory variables were included for each workpurpose 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 ManilaFillon & Tescon developed multiple regression models that estimate the volume of person tripsand vehicles attracted to condominiums catering to mix uses in the study. Thirty condominiumswithin Metro Manila were randomly selected and their attributes such as the available residentialfloor 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 wasconducted. 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 totalnumber 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 sitecharacteristics. Multiple linear regression models were then developed separately on the casualrelationship between peak people and vehicular attraction as related to site characteristics. Theresulting equations showed that the residential floor area was strongly related to the peakvehicular attraction as well as volume of people entering the condominium.2.4 Summery28
- 29. CHAPTER THREESTUDY FRAMEWORK AND DATA COLLECTION3.1 Overview:The main purpose of this study is to analysis the current shopping mall trip rate for Dhanmondiarea of Dhaka city. For the purpose of this study, trip is defined as a journey made by peoplefrom their accommodations and working place to the shopping mall in the Dhanmondi area orvice 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 theshopping 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 inBangladesh. The study tends to get the recent trip attraction pattern of the Dhaka city with thenew 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 primaryschool trip generation model building. This chapter will discuss in sequence the procedureapplied in achieving the objectives of the study at next page in Figure 3.129
- 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 chart30
- 31. The first phase of this study involves initial study about the fundamental of trip generation andtrip attraction model. Through this initial study, a basic concept or idea about the fundamental oftrip attraction rate calculation method can be understood. This first phase is important in order toapply the concept in the detailed analysis in phase three.Primary and secondary data were collected. For data such as Dhanmondi area population, landuse, employment, land value and other physical features of shopping centers in Dhanmondi areawere collected. The data of shopping mall was obtained for the year 2011. This surveycan be done by site visit or comparing the data with local plan, reports or refer to the localauthority. The number of incoming people and car attracted to the shopping mall were collectedduring peak period at 15 minutes interval.In the third phase, the data will be analyzed to calculate the trip attractionrate. Through the calculation, relationship between every parameter will be determined andsignificance of every parameter also can be defined. Furthermore the attraction of shopping tripgeneration can be determined for Dhanmondi area.As a final product of this study, shopping trip attraction rate in Dhaka city will be determinedand the mathematical relationships that synthesis trip attraction pattern on the basis ofobserved trips will be produce. The trip rates of the shopping centers will indicate therelationship between three parameters that used in the model which is accessibility, holdingcapacity and cost index.3.2 Study areaOnce the nature of the problem at hand is identified, the study area can bedefined to encompass the area of expected. Study areas are geographic boundariescreated to define the extent of the study analysis. The boundary of this study area toas the external cordon, include the develop area. The location of the boundary alsoimportant to defined the location of the shopping centers in Dhanmondi area of Dhaka city. Inrecent decades there are significant numbers of shopping mall had been constructed inDhanmondi area, which affects the travel pattern in the area. The shopping mall growth rate inthe area is higher. Six different shopping mall on Mirpur road was selected for calculating the31
- 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 studyThe survey was conducted for six Shopping Centers (Shopping Centers) in Dhanmondi Mirpurroad, Dhaka. This section describes how the Shopping Centers are categorized into differentgroups for the purpose of analysis and the characteristics of the Shopping Centers belonging tothese groups. The Shopping Centers are classified into 4 groups based on the composition of thestores in the Shopping Center.Type 1: This is a large Shopping Center with a large supermarket, a large discount retail store, oneor two restaurants, a bank, and many small stores are located. The Shopping Center in this categoryare New market, Gulshan shopping center, Mohakhali Municipal Market ,Bashundhara CityType 2: This is a medium size Shopping Center where a medium sized supermarket, a mediumsized discount retail store and many smaller stores are located. The Shopping Centers in thiscategory are Rapa plaza, Metro Shopping Mall.Type 3: This is a small Shopping Center where one supermarket and several small stores arelocated. 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 discountretail 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 ofthe 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 ,Dhaka34
- 35. 3.4 Time of SurveyThe data was collected on different days of the week and different times of the day for aperiod of 1 month (Late Autumn of 2011 during October) all the data was collected for every 15minutes time interval. This interval is chosen because Highway Capacity Manual usesthis interval as the base unit for capacity calculation, and also it is rather practical fromthe standpoint of the person collecting the data. The typical duration of a survey wasthree hours. The smaller Shopping Centers were observed between 4 p.m. and 7 p.m., usuallyduring the weekdays. The larger Shopping Centers were observed during the peak hour traffic onFridays (4 p.m.-7 p.m.)3.5 Data requiredBased on the models proposed in Chapter 2, the data required for the analysis is divided into twogeneral categories The trip attraction rate (Trip Attraction Rate) of the whole Shopping Center in terms of thenumber 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 totalfloor area of Shopping Center (cumulative floor area of all stores in the Shopping Center), numberof parking spaces, no of employee, total number of shops and total site area.3.6 Initial SurveyDhanmondi in Dhaka is located just beside the busy roads (Mirpur-Azimpur). Analysis ofexisting 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 sixshopping mall were selected for calculating trip attraction rate. The shopping malls are Orchardpoint, ARA Center, Metro Shopping Mall, Rapa plaza, Plaza AR and Adel Plaza. From initialsurvey it was found that. The peak period generally occurs at evening. So data collection timewas set from (4:00 pm -7 pm). The physical features of the shopping centers like no of entrygate, no of storey, parking space availability, area of each floor space were also estimated initialsurvey.35
- 36. 3.7 Data collection3.7.1. Physical features of shopping mallSix Shopping Center (Shopping Center) were selected from initial survey. The ShoppingCenter‟s are Orchard point, ARA Center, Metro Shopping Mall, Rapa plaza, Plaza AR and AdelPlaza.The Shopping Center site area was calculated from using website like Wikimapia. As theShopping Center‟s are geometrically rectangle shapes the length and width of each shoppingmall was measured using odometer. Then floor area is calculated multiplying the length andwidth. The Gross Floor Area (Gross Floor Area) of each Shopping Center was calculated bymultiplying the floor area and number of storey.Data regarding Number of storey, number of shops, total number of employee and number ofentry and exit gate were collected by visual observation and through the consultation ofShopping Center authority.The Shopping Centers contain stores and parking spaces. The number of parking spaces is basedupon accessibility characteristics, e.g., pedestrian orientation and transit availability. Theminimum standard for parking space for the Shopping Center is 5 spaces per 1,000 square feetretail area (Calthorpe, 1993; Steiner, 1998). A rule of thumb parking requirement is twohundred square feet per vehicle. The scale of the Shopping Center, the distance betweenestablishments (stores), the vast parking lots to cross, and lack of direct pedestrianconnections discourage the visitor to travel from store to store on foot (Campoli, Humstoneand McLean, 2002). Based on our observation, in all the Shopping Center‟s surveyed there areplenty of parking spaces. The data is obtained from the Shopping Center authority. Thephysical 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 2Table 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 1037
- 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 20Table 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 13038
- 39. Table 3.5 Table showing the floor area, total number of floor and gross floor area of eachshopping 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 546003.7.2 Trip Attraction at 15 minute intervalThe Trip Attraction of the Shopping Centers is obtained from the number of people and vehiclesentering the Shopping Center in every 15 minutes interval. Number of incoming shoppers andcar trips were counted by survey for every 15 minute intervals during the peak period. The peakperiod time range was (4:00 pm- 7:00 pm). Incoming shopping trip counts were collected byvisual observation. One surveyor was appointed in each gate of the shopping center. Data werecollected for typical week day and week end day (Friday, Saturday). For each shopping centerdata 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 onthe number of the time of the day, day of the week and the season. The fluctuations in the TripAttraction Rate of the stores and the Shopping Center on the whole show the complexity involved instudying the trip attraction of Shopping Centers. Table shows the number of vehicles and peopleentering the Shopping Center during a three-hour survey period for two different days. Thegraphs showing variation of trips for weekday and weekends for six Shopping Centers included inthe survey in the following pages.39
- 40. Table 3.6: Number of incoming persons and incoming vehicles to the Rapa Plaza in fifteenminutes 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 14Table 3.7: Number of incoming persons and incoming vehicles to the Rapa Plaza in fifteenminutes 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 170 840
- 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 100Graph 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 (at15 min interval)41
- 42. Table 3.8: Number of incoming persons and incoming vehicles to the Orchard point infifteen 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 11Table 3.9: Number of incoming persons and incoming vehicles to Orchard point in fifteenminutes 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 136 842
- 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 60Graph 3.3 graph showing the variation of person trip attraction in day 1 and day 2 at OrchardPoint (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 0Graph 3.4 graph showing the variation of car trip attraction of day 1 and day 2 at OrchardPoint(at 15 min interval)43
- 44. Table 3.10: Number of incoming persons and incoming vehicles to the Metro ShoppingMall 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 5Table 3.11: Number of incoming persons and incoming vehicles to the Metro ShoppingMall 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 178 644
- 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 100Graph 3.5 graph showing the variation of person trip attraction in day 1 and day 2 at MetroShopping 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 0Graph 3.6 graph showing the variation of car trip attraction of day 1 and day 2 at MetroShopping mall (at 15 min interval)45
- 46. Table 3.12: Number of incoming persons and incoming vehicles to the A.R.A Center infifteen 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 3Table 3.13: Number of incoming persons and incoming vehicles to the A.R.A Center infifteen 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 20 546
- 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 0Graph 3.8 graph showing the variation of car trip attraction of day 1 and day 2 at A.R.ACenter(at 15 min interval)47
- 48. Table 3.14: Number of incoming persons and incoming vehicles to the Plaza A.R. infifteen 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 7Table 3.15: Number of incoming persons and incoming vehicles to the Plaza A.R. infifteen 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 125 648
- 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 60Graph 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 0Graph 3.10 graphs showing the variation of car Trip Attraction Rate day 1 and day 2 at PlazaA.R. (at 15 min interval)49
- 50. Table 3.16: Number of incoming persons and incoming vehicles to the Adel Plaza in fifteenminutes 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 12Table 3.17: Number of incoming persons and incoming vehicles to the Adel Plaza in fifteenminutes 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 110 850
- 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 40Graph 3.11 graphs showing the variation of person trip attraction in day 1 and day 2 at AdelPlaza. (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 0Graph 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 FOURANALYSIS AND FINDINGS4.1 OverviewThe 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 appliedfor calculating Trip Attraction Rate of the studied shopping center. At first PM peak hourincoming trip will be calculated from the collected data both person trip and car trip for eachshopping center. After the estimation of pm peak hour trip rate, the trip rate will be used tocalculate Trip Attraction Rate with respect to the physical features of Shopping Centers. At last avariation of Trip Attraction Rates among different shopping malls will be shown. The TripAttraction Rate will be calculated both for typical week day and week end for each ShoppingCenter. From the Trip Attraction Rates of day 1 and day 2 an average trip rate will be calculated.4.2 Peak hour trip rate calculationSix different Shopping center of Dhanmondi areas were studied. Data were collected for eachShopping Center from (4 pm – 7 pm.) which is the peak period for Shopping Centers. Thenumber of incoming people and number of car was counted for every 15 minutes interval. Thesum of every 4 consecutive interval incoming trip is then counted for calculating peak hourincoming trip rate. Summation of every 4 interval data will be calculated for hourly trip rate. Thehighest hourly data will be considered as peak hour trip rate for each Shopping Center. Thisprocedure will be done for both day1 and day 2 data. In the next consecutive pages peak hourtrip rate calculation will be shown in tabular format.52
- 53. Table 4.1: 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) 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 127 4:15pm-4:30pm 144 595 4:30pm-4:45pm 160 650 4:45pm-5:00pm 164 715 5:00pm-5:15pm 182 722 5:15pm-5:30pm 209 731 5:30pm-5:45pm 167 721 5:45pm-6:00pm 173 666 6:00pm-6:15pm 172 694 6:15pm-6:30pm 154 706 6:30pm-6:45pm 195 6:45pm-7:00pm 185Peak Hour Trip Attraction from (5:00 PM - 6:00PM ) 731 TRIP /HOUR Time interval Car 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 Trip(in) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 8 4:15pm-4:30pm 9 46 4:30pm-4:45pm 13 38 4:45pm-5:00pm 16 40 5:00pm-5:15pm 11 36 5:15pm-5:30pm 9 31 5:30pm-5:45pm 11 44 5:45pm-6:00pm 13 54 6:00pm-6:15pm 12 62 6:15pm-6:30pm 9 65 6:30pm-6:45pm 17 6:45pm-7:00pm 14Peak Hour Trip Attraction from (6:00 PM - 7:00PM ) 65 Car/Hr53
- 54. Table 4.2: Peak hour trip rate calculation both for person and car trip of Orchard Point Day: 2 Week Day: Thursday Date: 27-10-2011 Time interval Person 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 Trip(in) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 122 4:15pm-4:30pm 137 567 4:30pm-4:45pm 149 613 4:45pm-5:00pm 159 654 5:00pm-5:15pm 168 662 5:15pm-5:30pm 178 678 5:30pm-5:45pm 157 697 5:45pm-6:00pm 175 670 6:00pm-6:15pm 187 678 6:15pm-6:30pm 151 673 6:30pm-6:45pm 165 6:45pm-7:00pm 170Peak Hour Trip Attraction from (5:15 PM - 6:15PM ) 697 TRIP /HOUR Time interval Car 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 Trip(in) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 7 4:15pm-4:30pm 8 36 4:30pm-4:45pm 9 29 4:45pm-5:00pm 12 33 5:00pm-5:15pm 12 37 5:15pm-5:30pm 13 36 5:30pm-5:45pm 11 49 5:45pm-6:00pm 13 64 6:00pm-6:15pm 15 66 6:15pm-6:30pm 12 63 6:30pm-6:45pm 15 6:45pm-7:00pm 8Peak Hour Trip Attraction from (5:45 PM - 6:45PM ) 66 Car/Hr54
- 55. Table 4.3 Peak hour trip rate calculation both for person and car trip of Orchard Point. Day: 1 Week day: Thursday Date: 20-10-2011 Time interval Person 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 Trip(in) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 103 4:15pm-4:30pm 101 426 4:30pm-4:45pm 107 453 4:45pm-5:00pm 115 458 5:00pm-5:15pm 130 439 5:15pm-5:30pm 106 421 5:30pm-5:45pm 88 405 5:45pm-6:00pm 97 432 6:00pm-6:15pm 114 484 6:15pm-6:30pm 133 491 6:30pm-6:45pm 140 6:45pm-7:00pm 104Peak Hour Trip Attraction from (6:00 PM - 7:00PM ) 431 TRIP /HOUR Time interval Car 4:00 4:15 4:30 4:45 5:00 5:15 5:30 5:45 6:00 Trip(in) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) - - - - - - - - - 5:00 5:15 5:30 5:45 6:00 6:15 6:30 6:45 7:00 (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) (pm) 4:00pm-4:15pm 8 4:15pm-4:30pm 10 42 4:30pm-4:45pm 14 34 4:45pm-5:00pm 10 37 5:00pm-5:15pm 13 38 5:15pm-5:30pm 15 47 5:30pm-5:45pm 19 62 5:45pm-6:00pm 15 75 6:00pm-6:15pm 13 76 6:15pm-6:30pm 13 71 6:30pm-6:45pm 16 6:45pm-7:00pm 14Peak Hour Trip Attraction from (5:45 PM - 6:45PM ) 76 Car/Hr55

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment