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A Unified Framework forSelecting a Travel DemandForecasting Model forDeveloping CountriesHannibal Bwire aa Department of Transportation and GeotechnicalEngineering, Faculty of Civil Engineering and the BuiltEnvironment, College of Engineering and Technology ,University of Dar es Salaam , Dar es Salaam, TanzaniaPublished online: 20 Jun 2008.

To cite this article: Hannibal Bwire (2008) A Unified Framework for Selecting a TravelDemand Forecasting Model for Developing Countries, Transportation Planning andTechnology, 31:3, 347-368, DOI: 10.1080/03081060802087809

To link to this article: http://dx.doi.org/10.1080/03081060802087809

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ARTICLE

A Unified Framework for Selectinga Travel Demand Forecasting

Model for Developing Countries

HANNIBAL BWIRE

Department of Transportation and Geotechnical Engineering, Faculty of CivilEngineering and the Built Environment, College of Engineering and Technology,

University of Dar es Salaam, Dar es Salaam, Tanzania

(Received 27 October 2006; Revised 10 July 2007; In final form 28 March 2008)

ABSTRACT The ability to judge and select a model that is appropriate for aparticular application is considered to be one of the most important aspects incontemporary transport planning. However, there is no suitable procedure forthe systematic selection of a model that is most appropriate for meeting the needsand requirements of a particular planning task. Although there is little literatureon the criteria for model assessment and selection methodologies, none cansupport systematic evaluation of different models versus quality of obtainabledata versus efforts for data provision. Such deficiencies support the need forfurther guidance on a model selection procedure for developing countries whereefforts for data provision are highly susceptible to higher sampling andmeasurement errors. This study presents a unified framework for the systematicmodel selection process. Evaluation of the framework for a case study of Dar esSalaam city in Tanzania evidences its benefits and applicability.

KEY WORDS: Travel demand forecasting model; model selection; developingcountry; transport planning; data quality

Correspondence Address: Department of Transportation and Geotechnical Engineering, Faculty of

Civil Engineering and the Built Environment, College of Engineering and Technology, University

of Dar es Salaam, P. O. Box 35131, Dar es Salaam, Tanzania. Email: bw_hann@yahoo.co.uk

ISSN 0308-1060 print: ISSN 1029-0354 online # 2008 Taylor & Francis

DOI: 10.1080/03081060802087809

Transportation Planning and Technology, June 2008

Vol. 31, No. 3, pp. 347�368

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Introduction

With the increase in travel demand and traffic management problems,travel demand forecasting models are being employed increasingly tomake informed decisions about the operational improvements to theexisting transportation system and the design and performance offuture transportation systems. The main advantage of using traveldemand forecasting models for such purposes is that they are capable ofcapturing the interactive effects of different components of the systemunder study. For that, they provide a mechanism to predict the impactof various policies and programmes on travel. As a result, over the pastdecades a number of competing models have emerged. Their sophis-tication ranges from simple mathematical formulae to complexmodelling software.

In view of the developments in modelling approaches, several authorshave been of the opinion that some of the approaches appear to bepromising for application to cities in developing countries. However, itis also well known that none of the state-of-the-art modelling as well asstate-of-the-art of practice is free from some limitations. In fact a modelthat is appropriate to a particular application may be inappropriate in adifferent application context. Therefore before proceeding to usemodels one needs to select a model that meets the specific and uniqueanalysis requirements and constraints of the project at hand or of aparticular planning department. However, there is no procedure for thesystematic selection of a model that is most appropriate for meeting therequirements and constraints of a particular planning context.

Although there is very little literature about model assessment andselection procedure, none has identified and incorporated criteria forassessment of methods for data provision. Consequently, most paststudies do not provide a suitable procedure that can support systematicevaluation of a pool of potential models versus quality of obtainabledata versus efforts for data provision. Such deficiencies support theneed for further guidance on model selection procedure to developingcountries where efforts for data provision are susceptible to highersampling and measurement errors.

This paper bridges the above gap by presenting a unified frameworkfor a model selection procedure that can be used to support asystematic model selection process. The framework is based on a setof qualitative and quantitative criteria for assessment of models andmethods of data collection. In order for the framework to be useful todifferent applications, it is focused on a more instructive selectionprocedure rather than a prescriptive one. The selection procedure isapplied to a case study of Dar es Salaam city in Tanzania to illustrate itsbenefits and its applicability in evaluating the potential for trip

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generation modelling. Section ‘Background’ provides the backgroundto this study. Section ‘Model Selection Procedure’ presents the frame-work of the model selection procedure while Section ‘Evaluation of theFramework’ evaluates the framework based on a case study of Dar esSalaam in Tanzania. Section ‘Summary and Conclusions’ provides asummary and conclusions.

Background

Since the formal introduction of the conventional four-stage traveldemand forecasting, the model has witnessed a number of highlycritical reviews. Subsequently, improvements have been made to thefour-stage modelling approach and new modelling approaches haveemerged. Few of the approaches have already been tried in developingcountries’ cities (Stopher & Wilmot, 1979; Takyi, 1990; Vougioukaset al., 1991; Bussiere & Rice, 1995; Arasan et al., 1996) while otherapproaches appear to be promising for application (Willumsen, 1981;Khan & Willumsen, 1986; Yanaguaya, 1993; Kane & Behrens, 2002;McDonald et al., 2002). However, there is very little literature(Elangovan & Crouch, 1992) which has contributed to the criteriaand procedure for model assessment and selection for developingcountries. As a result, criteria for assessment of data quality and effortsfor data provision is lacking in existing literature. Instead availableliterature such as Elangovan and Crouch (1992) proposed criteria of‘use of secondary data’, ‘cost for whole modelling process’ and‘suitability for developing countries’ only. The selection procedureproposed by such a study does not provide for a systematic evaluationof a pool of models versus data quality versus efforts for institutingdifferent methods of data collection.

Likewise, studies from developed countries (Islam et al., 1995;Bonsall, 1997; Boyce, 1997; Lohse & Schneider, 1997; Horowitz &Farmer, 1999; Pilz et al., 1999; Yue & Yu, 2000; Ortuzar &Willumsen, 2001), which have contributed to criteria for modelassessment and selection procedure, also lack criteria for assessmentof data quality and efforts for data provision. However, the studiesprovide a broad range of other issues to consider when evaluatingmodels.

The problem of the lack of criteria for assessment of methods of datacollection (data quality and efforts for data provision) is furthercompounded by the lack of a well-established methodology forassessing methods of data collection. However, there are many studieswhich have dealt with single or few indicators of data quality whileother studies (Richardson et al., 1995; Cambridge Systematics, 1996)focused on issues that should be taken into account so as to institute

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quality methods of data collection. These studies provide a broad rangeof indicators of data quality and factors which have a profoundinfluence on data quality and efforts for data provision.

Developing countries have limited financial resources and number oftransport planning professionals, and lack experience in transportmodelling. In addition, data collection activity in these countries ishighly vulnerable to higher sampling and measurement errors. Thus,the above deficiencies argue in favour of further guidance on modelselection procedure for developing countries. This viewpoint isconsistent with the notion that the ability to judge and select a modelwhich is most suitable for a particular application is one of the mostimportant elements of contemporary transport planning process(Ortuzar & Willumsen, 2001). Despite the continued discussion andstruggle over how transport planning should be conducted in develop-ing countries, such guidance support efforts to evolve appropriateplanning techniques to these countries.

Model Selection Procedure

The main purpose of the unified model selection procedure is to enablea systematic model assessment process with a view to selecting the mostappropriate model that meets the needs and unique requirements of aparticular planning context. This can only be achieved through aframework of a model selection process that ensures consistency andlogical process in model assessment. Thus, the framework is based on aset of selection criteria. As indicated in the previous section, the criteriaare classified into two main levels of assessment: assessment of methodsof data collection and assessment of models.

The framework is best suited to evaluating a pool of known models.It is intended to be more instructive rather than prescriptive in order forit to be applicable to different planning contexts. Consequently, itsefficiency and effectiveness is mainly dependent upon the actualinformation needs of different assessment steps versus the informa-tion/data provided.

Assessment of Methods of Data Collection

The assessment of methods of data collection is subdivided into twolevels of assessment. The first level involves data quality assessmentwhile the second level focuses on assessment of efforts for dataprovision.

In the data quality assessment level, there are three sub-levels ofassessments:

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. General Assessment. This seeks to provide basic information neededto inform the qualitative and quantitative assessments of dataquality. It provides general information about methods of datacollection. It comprises criteria which are not good measures of dataquality but which seek to provide information that is directly relatedto the general usefulness of data.

. Qualitative Assessment. This examines factors that influence thequality of methods of data collection.

. Quantitative Assessment. At this level, data quality is assessed interms of quantitative measures.

The second level of assessment involves the assessment of efforts fordata provision. While the main concern of the design and institution ofmethods of data collection has been the issue of ensuring high-qualitydata, different methods of data collection need varying efforts to ensurethat each step of data production process is at high quality. This impliesthat the quality of data depends also on the quality of different steps ofdata production process. The quality of data is thus built in ordestroyed at various interrelated steps of data production. It is thereforeimportant to assess efforts required to maintain each step of datacollection activity to its highest quality. This viewpoint is in line withthe ‘Total Survey Design’ concept (Dillman, 1978 cited in CambridgeSystematics, 1996). Several methods of data collection are available fora particular data collection activity. The quality of data that can begained from each method may vary as well as the efforts required toprovide the data. Therefore, criteria for assessment of efforts for dataprovision aim at examining factors which influence the total costs forproviding the required data. This amounts to the determination of theresources, in terms of the total cost, required to provide the requireddata.

Criteria for the above assessment levels are provided in Table 1.

Assessment of Models

A distinction is made at the outset between a model and modellingsoftware/planning software package. A model is a simplified represen-tation of a real-world system, valid from a particular perspective. It isconsidered to represent a transport system reasonably well providedmost exogenous conditions remain more or less stable (Ortuzar &Willumsen, 1992). On the other hand, the terms modelling software orplanning software package represent a computer-based model(s)implemented for practical application (Bonsall, 1997). This distinctionis utilised only where the term modelling or planning software or modelsystem is used, otherwise the term model is meant to carry both

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meanings. Thus, the assessment of models can be subdivided into thefollowing five levels of assessment:

. Model Data Needs Assessment. This is concerned with data needsassessment. It focuses on assessing data input requirements ofmodels.

. Model Functional Assessment. This level seeks to ascertain majorfunctions of a model.

. Model Reliability Assessment. It aims at examining the validity ofmodel’s underlying theory and basis for the structure and development

Table 1. Criteria for assessment of methods of data collection

Assessment level Criteria

Data Quality AssessmentGeneral Assessment Survey purpose and objectives

Data typesSurvey durationConsistency of definitions and classificationsData adjustment methodsReasons for the selection of a particular method for datacollectionProblems and strategy to overcome them

Qualitative Assessment Level of aggregation of dataTarget populationSampling unitQuality of sampling frameSampling methodSample selection procedureChoice or design of measurement instrumentQuality control procedure

Quantitative Assessment Sample sizeCoverage errorSampling errorObservational errorsStatistical comparison of survey results with secondarydataCross validation of survey resultsUnit non-response

Assessment of Efforts forData Provision

Characteristics of the study’s area of interest

Experience and knowledge from past surveysQuality of potential methods for data collection versusdesirable data qualityResource requirements versus resource available

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on one hand, and the ease with which the analyst can apply the modeland properly validate application procedures on the other hand.

. Modelling Efforts Assessment. This level focuses on determiningefforts required to implement, operate, and to maintain a model.

. Modelling Software Assessment. Aims at ascertaining whethermodelling software possesses certain kind of features and propertiesthat provide ease with which the analyst can apply it to achieve theintended functions.

Criteria concerning the above assessments are listed in Table 2.

Interaction among Assessment Levels

Based on a synthesis of the criteria listed in Tables 1 and 2 forassessment of methods of data collection and models, respectively,Figure 1 postulates a framework for unifying some levels of assessmentof models and methods of data collection. While the data-needs criteriafor assessment of models seek to identify data input requirements ofmodels, the criteria for assessment of data quality of methods for datacollection seek to establish the quality of data to be provided by aparticular method for data collection. It can be noted from Tables 1and 2 that the data needs assessment level for model assessment hassome criteria for assessment in common with the data qualityassessment level for methods of data collection. On the basis of thisinteraction, the data needs assessment level for model assessmentprocess can therefore be subdivided into two sub-levels of assessment.The first sub-level involves those criteria which do not interact in anyway with the criteria for assessment of data quality of methods for datacollection. The second sub-level involves those criteria which are incommon with the data quality assessment criteria for assessment ofmethods for data collection.

Likewise, it can also be noted from Tables 1 and 2 that the level forassessment of modelling efforts has some criteria for assessment incommon with the level of assessment of efforts for data provision.While the criteria for assessment of modelling efforts seek to determineefforts required to implement, to operate, and to maintain a particularmodel; the criteria for assessment of efforts for data provision providethe former assessment level with the assessment results of efforts fordata provision.

The aforementioned interactions among the different assessmentlevels serve to illustrate the peril of emphasising quality modellingapproaches on one hand, while on the other the same emphasis is notplaced on quality methods of data collection versus efforts for dataprovision. Thus, Figure 1 postulates a framework for unifying the

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Table 2. Criteria for assessment of models

Assessment level Criteria

Model Data Needs AssessmentSub-level 1 Total number of input data variables included/required by a

modelType and number of missing or redundant input datavariablesType and number of variables that are difficult to forecastType and number of unavailable or difficult to provideinput data variables

Sub-level 2 Level of aggregation of data as compared to that ofavailable or obtainable dataQuality of available data/methods for data collectionSample size requirements of a model

Model FunctionalAssessment

Relevance of included/required variables as compared tothe desired optionsType of responses/options allowed in the model ascompared to the desired onesPossibility of a model to allow inclusion of new variablesLevel of detail of analysis permitted in the modelScenarios allowed in the model as compared to the desiredonesUsefulness of outputs to other planning tasks

Model ReliabilityAssessment

Understandable and relevant model theory, assumptionsand formulationType, number, and relevance and flexibility of defaultparameters and/or supporting functionsStrength and limitationLevel of accuracy of model outputsScope/range, type and degree of detail of the results to beachievedSensitivity tests allowed in the model

Modelling EffortsAssessment

Efforts for obtaining existing model codes

Efforts for programming or coding the modelEfforts for acquiring modelling software and licenceHardware requirementsModel system operation costsEfforts for model system integrationEfforts for model system modificationEfforts for data provisionEfforts for data integrationEstimation and calibration effortsThe availability of professionals and training needs

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process of assessment of models versus data quality versus efforts fordata provision.

Unified Framework

A unified framework for the model selection process is illustrated inFigure 2. It postulates a set of selection decisions that should be madeon various steps of the assessment process. Thick lines with arrowsindicate a link between main assessment steps or decision dialogs whilethin lines with arrows show iteration process between the steps.

Step 1: Study Area Objectives, Analysis Requirements and Constraints. Thefirst step is to specify study objectives, analysis requirements, and toidentify constraints of the study. For analysis requirements, clearspecifications have to be made with regard to the following:

. Modelling purpose

. Level of detail of analysis at which the model must operate

. Types of responses desirable in the model

. Desirable level of detail and accuracy of model outputs

Table 2 (Continued)

Assessment level Criteria

Modelling SoftwareAssessment

Modelling software design philosophy

Comprehensiveness and clarity of modelling softwaredocumentation and transparency relative to its applicationto a range of simulation conditions and in depicting inputand output dataType of models implementedFlexibility to incorporate additional modelsTypes of estimation/calibration procedures implemented orwhich can be implementedModel software run timeMaturity of modelling softwareFriendliness and flexibility of user interfaces and supportprovidedThe inclusion of input and output functions and theircapabilities to store and develop input data and outputs,respectively, at any spatial scale of analysisScale of application of modelling softwareThe relative ease with which existing data and database canbe accepted by the modelsType of data models incorporated/to be supported

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. The need to support continuous planning efforts

. Capacity building requirements of a transport-planning unit

Constraints of the study are usually multi-faceted ranging from timeavailable for the study to staff and money allocated for the study. Asindicated in Figure 2, dashed lines emanating from Step 1 with arrowspointing to various steps of the process signify that the resource andinformation base provided at this step influences the extent to whichother steps of model selection process will be.

Step 2: Available Data. This step involves examination of the available orexisting data in relation to the specified level of detail of analysis atwhich the model must operate and type of responses required in themodel. For whatever data is available, one will certainly wish toascertain its quality before deciding to acquire the data. This impliesthat the available data must be subjected to quality assessment (Step 3).

Step 3: Data Quality Assessment. This step involves a qualitativeassessment of the available data in relation to the criteria listed inTable 1.

Step 4: Pool of Relevant Models. The quality of available data andinformation base from Step 1 coupled with the user’s familiarity withdifferent models will enable the analyst to identify a pool of models thatare relevant to the study (Step 4). In addition, this step determines the

Models Assessment LevelsAssessment Levels of Data

Collection Methods

Data Quality Assessment1.General Assessment

2.Qualitative Assessment3.Quantitative Assessment

Functional Assessment

Model Reliability Assessment

Modelling Efforts Assessment Assessment of Efforts for DataProvision

Modelling Software Assessment

Data Needs Assessment

Figure 1. Interactions among assessment levels

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Step 2

Step 4

Step 3

No

Step 8

End

Steps 5 & 7

Yes Step 6

Step 1

Step 10

Step 11Step 9

Choice of Modelling Tool

Modelling Software

Programming Software

General Purpose Software

Manual Computation

Pool of Relevant Software

Modelling Software Assessment

Testing of Modelling Tool

Most Appropriate Model(s)

Models Assessment

Data Quality Assessment

Pool of Relevant Models

Available Data

Study Area Objectives, Analysis Requirements and Constraints

Assessment of Efforts for Data Provision

Acquisition & Processing of

Available DataNew Data

Some/All Suitable or Available?

Figure 2. Unified framework of model selection process

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number of models that will be subjected to further assessment in thenext steps.

Step 5: Model Assessment (Assessment of Data Needs, Functionality andReliability). This step deals with a detailed assessment of the modelsidentified in Step 4. It involves the first three levels of assessment shownin Figure 1, that is, data needs assessment, functional assessment, andmodel reliability assessment.

The first level of data needs assessment has to be performed in twosub-levels, as elaborated in the previous section. The first sub-levelinvolves the criteria (of type and number of redundant input datavariables, total number of input data variables, input data variableswhich are difficult to forecast, and unavailable or difficult to provideinput data) for assessment of models, which do not interact in any waywith those in Step 3. The second sub-level involves the remainingcriteria, which do interact with some criteria used in Step 3, as listed inTables 1 and 2. It is during this level of assessment conclusions on theresults from the assessment of quality of data in Step 3 will be referredto in order to enable the evaluation of quality of available data versusdata needs of models. An arrow from Step 5 pointing to Step 3 and viceversa illustrates this process. However, if it happens that the quality ofavailable data does not match with data needs of models then a newpool of models will be needed. An arrow pointing from Step 5 to Step 4represents such a decision. Conversely, if the quality of available datamatches with data needs of models then the assessment process willproceed to the remaining two levels of assessment, that is, functionalassessment and model reliability assessment.

Overall assessment results of Step 5 in combination with the resourceand information base provided from Step 1 will result into eitherfinding that all the identified models under assessment are notappropriate or all/some of the identified models are appropriate toenable the continuation of the selection decision process. The formerdecision is represented by an arrow from Step 5 pointing to Step 4 whilethe latter decision is represented by an arrow pointing to the dialog boxwith the question of ‘some/all suitable or available?’ The formeriteration, in effect, re-initiates the selection process from Step 4 byidentifying a new pool of models for consideration. On the other hand,the latter decision is expected to provide clear statements with regard togaps or sufficiency in data requirements. In this regard, whicheveranswer is provided to the question in the dialog box, it will lead to theassessment of efforts for data provision.

Step 6: Assessment of Efforts for Data Provision. This step involves theassessment of efforts to provide the available data or new data. Even ifsome data may be available, one will certainly wish to ascertain efforts

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required to acquire it and, if necessary, to process it to the required levelof detail. In the event that new data are required, information has to beprovided on the quality of data that can be gained from a particularmethod of data collection under consideration and on the effortsrequired to provide the data. The information need is with respect tothe criteria for assessment of data quality as shown by an arrow fromthis step pointing to Step 3 and from Step 3 pointing back to this step.Details regarding the determination of efforts required to acquire andto provide existing data or new data are with regard to the criteriaprovided in Table 1. An arrow from this step pointing to Step 7represents an optimal decision with regard to the necessary effortsrequired to provide the required data.

Step 7: Models Assessment (Assessment of Modelling Efforts). Step 7involves the assessment of modelling efforts. Feedback of informationfrom Step 6 and Step 1 allows the completion of the assessment at thisstep as per the criteria of assessment listed in Table 1. Likewise, overallassessment results of this step in combination with the resource andinformation base provided from Step 1 will influence decision to bemade at this level. In the event that the required effort is beyond reachthen a pool of other relevant models will be required as shown by anarrow from Step 7 pointing to Step 4 while an arrow from this steppointing to Step 8 represents the decision to proceed to Step 8. Theformer iteration, in effect, re-initiates the selection process from Step 4by identifying a new pool of models for consideration. Moreover, anarrow from this step pointing to Step 4 also represents a situation whennew types of models are to be considered in order to allow theconsideration of specific types of modelling tools (in the followingsteps) as well. Such a consideration will be heavily influenced by theanalyst’s familiarity with different modelling tools. It can be noted fromFigure 2 that assessment results from this step will enable the analyst toselect a particular modelling tool (Step 8) and to identify relevantsoftware (Step 9).

Step 8: Choice of Modelling Tool. The arrow pointing from Step 8 to Step11 represents a decision to apply general-purpose software or manualcomputation. Otherwise, a decision to employ specialised transportplanning modelling software or programming software amounts toidentifying a pool of relevant software (Step 9). Decision to select aparticular modelling tool will depend on the flexibility of informationbase from Step 1, assessment results from Step 7, and the analyst’sfamiliarity with different modelling tools.

Step 9: Pool of Relevant Software. This step deals with the selection of apool of relevant software which either comprises of or can support

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programming of all appropriate models, as identified in Step 7. In theevent that a pool of software is established, they should be assessed interms of the criteria for assessment of modelling software (Step 10).

Step 10: Modelling Software Assessment. This step deals with theassessment of modelling software with respect to the criteria ofassessment listed in Table 2. Thus, an arrow from this step pointingto Step 7 represents a situation when other modelling tools have to beconsidered in view of efforts required to apply them (Step 7) beforeconsidering a new pool of relevant software in Step 9. Otherwise, theselection process has to proceed to Step 11 where modelling softwarehas to be tested.

Step 11: Testing of Modelling Tool. Step 11 of the model selection processrequires testing of the selected modelling tool. In this case a modellingtool can be a tool for manual computation, general-purpose software,models implemented into programming software, or specialised trans-port planning modelling software. The purposes of testing a toolinclude to:

. Test a model solution procedure or algorithms

. Test model system run times

. Gain an understanding of a particular model with respect to aparticular function/task

. Review and check the characteristics of modelling software withrespect to the criteria listed in Table 2

Successful practical testing of the tool renders it to be the mostappropriate modelling tool and hence the models it contains. Other-wise, as indicated by an arrow from this step pointing to Step 8 andStep 9, a new choice of modelling tool or pool of software, respectively,has to be made. If a new pool of relevant software is proposed, theneffort to acquire, operate, and to maintain the software has to beassessed. This process is represented by an arrow from Step 9 pointingto Step 7. The iteration may, in effect, also re-initiate the selectionprocess back to Step 4 from Step 7 as shown by an arrow from Step 7pointing to Step 4.

Evaluation of the Framework

The assessment procedure described in the previous section was used todemonstrate how one can use the selection procedure step by step toevaluate models versus quality of data versus efforts for data provision.The demonstration is focused to evaluate the potential for applicationof trip generation models to a case study of Dar es Salaam in Tanzania.

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Two commonly used trip generation modelling approaches � categoryanalysis models and regression models � were examined and commentsand evaluation results are described below.

Step 1: Study Area Objectives, Analysis Requirements and Constraints

In this evaluation, however, no particular problem relevant to the casestudy area was identified. Therefore no specific study area objectives,analysis requirements and constraints were identified.

Step 2: Available Data

Common variables for trip generation models are attributes of socio-economic and demographic. Data available on the case study area isprovided in Table 3.

Step 3: Data Quality Assessment

In view of the criteria for assessment of data quality listed in Table 1,the criterion of level of aggregation of data was the most applicablehere with regard to the data types identified above because it was notpossible to obtain information concerning other criteria for assessmentof data quality. Table 3 presents a range of available data in relation totheir level of aggregation versus availability. As shown in Table 3,almost all the data variables that are available are limited at zonal levelonly.

Step 4: Pool of Relevant Models

In this step, the following most common approaches for trip generationmodelling were considered:

Table 3. Assessment of secondary data

Available data Zonal level Household level Person level

DemographicAge � �Gender � �Population �

Socio-economicHousehold size �Employment �

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. Regression models

. Category analysis models

Step 5: Model Assessment (Assessment of Data Needs, Functionalityand Reliability)

Data Needs Assessment. Data needs assessment was carried out at twosub-levels of assessment. The first sub-level involved the assessment of:

. Total number of input data variables included/required by themodels

. Type and number of missing or redundant variables in the modelsidentified

. Type and number of input data variables that are difficult to forecast

. Type and number of unavailable or difficult to provide input datavariables

The second sub-level of assessment involved assessing models inrelation to the following criteria which are listed in Table 2:

. Level of aggregation of data as required by the models compared tothat of available/obtainable data

. Quality of available data/methods for data collection (results of Step3)

. Sample size requirements of models (amount of data)

As far as there was no presumed model formulation, then all variablesthat are generally considered to explain trip-making behaviour ofpeople were taken into account under this assessment disregardingwhether they are available or not. Table 4 provides a summary of theassessment of variables which are commonly used in trip generationmodelling in correspondence to the information from the data qualityassessment provided in Table 3.

Double ticks in the table indicate increased difficulty. It can be notedfrom the table that both models face almost similar data problems interms of absence of data. The table also shows that both models caneasily be implemented at zonal and person levels. However, there issome flexibility of implementing a regression model at the zonal andperson levels as compared to a category model in view of the difficultyinvolved in providing and forecasting data. It is also well known thatcategory models requires extremely high amount of data (sample sizerequirements) for their calibration as compared to regression models.

Functional Assessment. The next level of assessment involves theassessment of model functionality. Table 5 presents a summary ofassessment results. Of the criteria listed in Table 2, the following

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Table 4. Data needs assessment of trip generation models

Zone Household Person

UnavailableDifficult

to provideDifficult

to forecast UnavailableDifficult

to provideDifficult

to forecast UnavailableDifficult

to provideDifficult

to forecast

Regression Analysis ModelPerson income � � � � � � � � �Household income � � � � � � � � �Car ownership � � � � �Employment � � � � � �Population � � �Household size � � � �Age �Gender �

Categories of Category ModelPerson income � � � � � � � � � �Household income � � � � � � � � � � � �Car ownership � � � � � � �Employment � � � � � � �Population � � � �Household size � � � � � �Age � � � � � � � � � � � � �Gender � � � � � � � � � � � � �

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criteria listed in Table 5 were used to perform model functionalassessment. Other criteria were found not applicable due to the factthat this evaluation process was not constrained to a particularpresumed model formulation and analysis requirements. As shown inTable 5, it is well known that regression models are more attractivewhen one considers including new variables in the models in futuretime.

Model Reliability Assessment. The most important criteria applicable herewas that of strength and limitation of models. Other criteria werefound not applicable due to the fact that this evaluation process wasnot constrained to a particular presumed model formulation. Thefollowing were identified to in order to signify the relevant strengthsand limitations of the two models:

. Demand of data

. Multicollinearity of variables

. Inclusion of new variables in the models in the future

The former and the latter issues have already been discussed in theprevious assessment. On the other hand, multicollinearity poses aproblem in regression models while in category models it is lessproblematic.

Step 6: Assessment of Efforts for Data Provision

At this stage it will have already be known or established if some or allof the required data are available and if all are suitable. Generally,efforts for acquiring existing data should be taken into account as wellas efforts for providing new data. For this particular evaluationexample, these two issues were not dealt with in detail as far as theexisting data was accessed free of charge for research purposes andsimilar resources (time and manpower) were used to collect travel datafrom home interviews using three methods of data collection (trip-based method, out-of-home activity-based method, and fully activity-based method). All the surveys were performed using same manpower

Table 5. Functional assessment of trip generation models

Regression Model Category Model

Zone Household Person Zone Household Person

Inclusion of new variables � � �Level of analysis permitted � � � � � �Usefulness of outputs � � � � � �

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on equal number of days. Analysis of travel data showed that dataobtained from full activity-based methods was of higher quality (lessobservational errors) than the other two methods.

Step 7: Model Assessment (Assessment of Modelling Efforts)

This step involves assessing models in terms of efforts required toimplement the most appropriate models. Both models can be used fortrip generation modelling. However, the implementation of categoryanalysis model requires more efforts, as noted in Step 5. This limitationmakes regression models to be more attractive and potential forapplication. This means if, in the model assessment Step 5, weightsfor each of the assessment criteria group would have been defined thenthe regression model would have been the top-rated model.

Step 8: Choice of Modelling Tool

Available general software such as SPSS and Microsoft Excel has built-in functions, which are capable to implement regression models.

Step 11: Testing of Modelling Tool

Few data were input in Excel and a multiple regression model wasobtained. The purpose of running the Excel program was mainly to testif a model can be obtained and no further efforts was made to compareand check the properties and features of the program in accordancewith other criteria listed in Step 11 and Table 2.

Evaluation

The application of the framework revealed four key issues that needattention. The first is that identifying a pool of models or software canbe very difficult, as it is constrained by time available and experience ofthe analysts.

Second, the application of the framework helps to identify deficien-cies in the present data versus the models and the consequences ofselection decisions. Not only that this allows models and data to beevaluated for their deficiencies, but also serve as a basis of needs forfurther development and research.

Third, the framework for model selection process is mainly based ona qualitative assessment process. It is only in Step 11 where the top-rated model or models can be subjected in real-world conditions. Thebenefit of this methodology is that it is less expensive to use the

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framework as different models which are not at hand can be assessedand evaluated.

Fourth, as it is noted in Step 7, it is possible to define relative weightsfor each of the criteria groups for model assessment in accordance withtheir relative importance to the whole model system. Weighting canalso be applicable to Steps 3 and 10 of the model selection process. Asan example, an agency or analyst might wish to acquire an improvedmodel system with regard to a particular model feature or property. Inthis case, a criteria group relevant for the assessment of this property orfeature would weight relatively more as compared to other criteriagroups.

Summary and Conclusions

This study has identified an improved methodological approach forselecting a model that is most appropriate for a particular planningstudy. The study highlighted a close interaction between modelassessment and assessment of methods for data collection. Throughthis interaction, the study has integrated model assessment andassessment of methods for data collection into a unified frameworkfor model selection process.

The efficiency and effectiveness of the framework to support andguide systematic selection decisions of a model that is most appropriatefor a particular application is mainly dependent upon the actualinformation needs of different assessment steps versus the informa-tion/data provided. It provides a systematic approach to the entiremodel selection process and that it helps in setting forth the myriadconsequences or the questions of ignoring the interactions inherent inthe model selection process. It also provides a basis for a systematicintegration of trade-offs between the whole cost of efforts to apply amodel and the available resources for a particular study. In otherwords, it sets forth the basis for qualifying a model as the mostappropriate for a particular application or for raising questions insearch of the appropriateness of the model.

A major strength of the model selection process is that it integratessystematically the tradeoffs of the assessment of methods of datacollection and model assessment. The process therefore offers asystematic approach to assess the credibility of any decision to adopt,to keep, or to revise a particular model. Thus, it is hoped that the11-step framework developed in this study is useful to any analyst oragency seeking to select, to acquire, or to revise a model for a particularapplication. The application of the framework revealed four key issuesthat need attention, as identified in the previous sub-section. Theframework is flexible in such a way that it is possible to modify or add

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further criteria for assessment within any assessment criteria group.This is because the criteria groups are based on either modelperformance elements, for the case of model assessment, and indicatorsof data quality and factors that affect quality of methods for datacollection.

As part of demonstration of the applicability of the model selectionprocess, the study has provided useful insights into the potentialapplicability of trip generation models to the case study area of Dar esSalaam. Deficiency in the existing secondary data has been highlightedin relation to the application of regression and category analysismodels. In view of the deficiencies in the present data, regression modelhas proved to be the most appropriate model for practical application.It should also be noted that the results indicate that these models canappropriately be implemented at zonal and person levels of analysis.

References

Arasan, V. T., Wermuth, M. & Srinivas, B. S. (1996) Modelling of stratified urban trip

distribution, ASCE Journal of Transportation Engineering, September/October, pp. 342�349.

Bonsall, P. W. (1997) Principles of transport analysis and forecasting, in: C. A. O’Flaherty (Ed.),

Transport Planning and Traffic Engineering, pp. 103�131 (London: Arnold).

Boyce, D. (1997) A practitioner’s guide to urban travel forecasting models, in: 12th International

EMME/2 Users’ Conference, October, San Francisco, CA.

Bussiere, Y. & Rice, R. G. (1995) Simplified travel demand forecasting: A comparison of

applications in developed and developing countries. Paper prepared for presentation at the

Simplified Travel Demand Forecasting Techniques Session of the 1995 Annual Conference of

the Transportation Association of Canada, October, Victoria, BC.

Cambridge Systematics Inc. (1996) Travel Survey Manual, prepared for US DOT and US EPA,

TMIP Program, Washington DC, July.

Elangovan, T. & Crouch, F. O. (1992) Towards simplified transport planning techniques for cities

in developing countries, in: J. O. Ortuzar (Ed.) Simplified Transport Demand Modelling, A

Compilation of Major Papers Selected from PTRC’s Summer Annual Meetings and Courses

1984�1989, Perspectives 2, pp. 19�32 (London: PTRC).

Horowitz, A. J. & Farmer, D. D. (1999) Statewide travel forecasting: A critical review,

Transportation Research Record, 1685, pp. 13�20.

Islam, M. N., Seneviratne, P. N. & Kalakota, K. R. (1995) Traffic management during road

closure, Transportation Research Record, 1509, pp. 46�54.

Kane, L. A. & Behrens, R. B. (2002) Transport planning models: An historical and critical review,

in: 21st South African Transport Conference South Africa, Pretoria, 15�18 July.

Khan, M. A. & Willumsen, L. G. (1986) Modelling car ownership and use in developing countries,

Journal of Traffic Engineering and Control, November, pp. 554�560.

Lohse, D. & Schneider, R. (1997) Vergleichende Untersuchungen der Aggregierten und

Disaggregierten Verkehrsplanungsmodelle, Final Report, Heft 3, TU-Dresden, Dresden.

McDonald, N. C., Porter, C. D. & Gorham, R. (2002) Availability of travel forecasting methods

and data for air quality planning in developing countries, Transportation Research Record,

1859, pp. 110�116.

Travel Demand Forecasting Model for Developing Countries 367

Dow

nloa

ded

by [

The

Uni

vers

ity o

f M

anch

este

r L

ibra

ry]

at 1

1:08

18

Nov

embe

r 20

14

Ortuzar, J. de D. & Willumsen, L. G. (1992) Monitoring of transport plans using low cost data

and advanced modelling techniques, in: J. O. Ortuzar (Ed.) Simplified Transport Demand

Modelling, A Compilation of Major Papers Selected from PTRC’s Summer Annual Meetings

and Courses 1984�1989, Perspectives 2, pp. 121�131 (London: PTRC).

Ortuzar, J., de, D. & Willumsen, L. G. (2001) Modelling Transport (3rd edn) (Chichester, West

Sussex: Willey).

Pilz, A., Plank-Wiedenbeck, U., Willems, F., Axhausen, K. W., Hayes, S., i Renau, L. I., Segarra,

E., Boltze, M., Jaehrling, H., Zhang, X., Allekotte, D., Stahnke, G., Retzko, H.-G., Bell, M.

G. H., Saleh, W., Thorpe, N. & Wright, S. (1999) Analysis and development of tools for

assessing traffic demand management strategies: Guidelines on the use of tools for assessing

TDM strategies, in: European Commission 4th Framework Programme, Brussels.

Richardson, A. J., Ampt, E. S. & Meyburg, A. H. (1995) Survey Methods for Transport Planning

(Oakland, CA: Eucalyptus Press).

Stopher, P. R. & Wilmot, C. G. (1979) Work-trip mode choice models for South Africa,

Transportation Engineering Journal of ASCE, 105(TE6), pp. 595�608.

Takyi, I. K. (1990) Trip generation analysis in a developing country context, Transportation

Research Record, 1285, pp. 9�21.

Vougioukas, E. M., Harajli, A. H. & Timberlake, R. S. (1991) Appropriate traffic modelling

techniques for developing cities: The case of the Oman Capital Area, in: M. Heraty (Ed.),

Urban Transport in Developing Countries � Lessons in Innovation, Perspectives 1, pp. 83�88

(London: PTRC).

Willumsen, L. G. (1981) Appropriate transport planning techniques for developing countries, in:

S. Yerrell (Ed.) Transport Research for Social and Economic Progress, pp. 341�352

(Aldershot: Gower Press).

Yanaguaya, W. (1993) The use of U.K. transport models in developing countries, Traffic

Engineering and Control, 10, pp. 476�479.

Yue, P. & Yu, L. (2000) Travel Demand Forecasting Models: A Comparison of EMME/2 and

QRS II Using a Real-World Network, Report No. SWUTC/00/167901-1, prepared for US

DOT, University Transportation Centers Program, Washington, DC.

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