Bachelor’s Degree In Engineering, Science, Economics .

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AH2170 Transport Data collection and Analysis 7.5 creditsTransport Data collection and AnalysisIntended learning outcomesThis course aims to provide knowledge on data collection and analysis methods as well as selectionand interpretation of appropriate statistical tests that are relevant to the solution of the studiedproblem.The intended learning outcomes are as follow: Identify appropriate methods for transportation, traffic and spatial data collection.Understand transportation and geoinformation data needsUnderstand the role sampling the data collectionUse descriptive statistics for the analysis and preparation of dataPerform outlier analysisPerform statistical inference for hypothesis testing and interval estimationsSpecify and estimate linear regression models and discrete choice modelsApply methods and interpret results using statistical softwareDesign and perform stated-preference studyDiscuss and compare linear regression models and discrete choice models and their attributesCourse main content Transportation and geoinformation data needsSampling and sample statistics.Descriptive statistics and outliersHypothesis testing and confidence IntervalsLinear regression and applications (in transport and traffic)Maximum estimation likelihood method and applicationsOther data analysis and model building methodsThe content of the course is presented and trained in tutorials. Applications are in trafficstudies, transport planning, safety studies and spatial analysis. Further training in field surveysand data analysis, model building and interpretation is carried out in the form ofcomprehensive project work.The project covers all the major steps that have to be undertaken including report preparation,discussion of the results. The students will also present their results for discussion.EligibilityBachelor’s degree in engineering, science, economics, planning or a similar degree, with atleast 60 cr (ECTS) in mathematics, physics, statistics and/or computer science, as defined inthe admission requirements for the Master’s programme in Transport and GeoinformationTechnology. Together with documented proficiency in English corresponding to English B.

LiteratureS. Washington, M. Karlaftis, F. Mannering, Statistical and Econometric Methods forTransportation Data Analysis, Second Edition (2011).Supplementary literature is being made available.Other useful books: M. Ben-Akiva, S. Lerman, Discrete Choice Analysis: Theory and Application to TravelDemand, MIT Press, 1987.J. de D. Ortúzar and L.G. Willumsen, Modelling Transport (2002).O’Flaherty (ed.), Transport Planning and Traffic Engineering, chapter 12-13, 1997.Lab time will be used to a) elaborate on various points made during the lectures, b) expand upontheory covered in lectures, c) work on various exercises with the assistance of a teacher, and d) discussissues related to the current project assignment. The lab sessions take place in laboratories equippedwith computers. Students may also bring their laptops to the lab sessions.

Projects/Case Study ElementLectures and LabsIt is expected that students participate actively in lectures. Active participation in labs is highlyrecommended. Lab time will be used to a) elaborate on various points made during the lectures,b) expand upon theory covered in lectures, c) work on various exercises with the assistance of ateacher, and d) discuss issues related to the current project assignment. The lab sessions takeplace in laboratories equipped with computers. Students may also bring their laptops to the labsessions.ProjectsStudents are expected to provide results (for both individual and team work) in both written and oralform as well as provide quality feedback to other students. There are 3 case studies with the followingweights: Case Study 1 (individual): 25%In this case study, the student need to be able to demonstrate the ability to handle the observationdata (travel time) collected from sensors in the street. Furthermore the student is required toprovide descriptive analyses of the data, recognizing patterns, and infer reasons underlie thepatterns. Furthermore, the student is required to provide analytical results and variance from thepatterns, find correlations between variables, and draw appropriate conclusions. Through thiswork, the students are expected to demonstrate their understanding about hypotheses tests,comparing and analysing confidence intervals and the effects of sample size. Case Study 2 (team): 25%In this case study, the students are given mission to develop their own regression models givenspecific objectives. The students then need to reflect the model fitness based on different visualtests and check whether they have violated any basic principles of the regression model. Thenusing the given model, the students are expected to be able to do some forecasting analyses,presenting the results in written and plots, and suggest a quantitative error measurements that canbe used for the given case. Case Study 3 (team): 50%In this case study, using the national travel survey, the students learns how to estimatemultinomial logit model. The students need to provide basic descriptive analyses, formulate apriori about the variable, check the correlation between variables, and report the importantpatterns among the variables. Then, using MATLAB, the students should propose their best twomodels, with justification of those selections. The students need to present and write a reportbased on their case study results.Individual projects: Although we encourage student cooperation and discussion, each individualstudent must write a report in his or her own words, presenting his or her own analyses.Team projects: Teams should be comprised of two students. Students may choose teams; however, theteaching staff reserves the right to change the composition of the teams.ILOs and examinationIntended Learning ObjectivesExamCaseStudy ICase StudyIIIdentify appropriate methods fortransportation, traffic and spatial datacollection.Understand transportation andgeoinformation data needsUnderstand the role sampling the datacollectionUse descriptive statistics for the analysisand preparation of dataPerform outlier analysisPerform statistical inference forXXXXXXXXXXXXXXCaseStudy IIIX

hypothesis testing and intervalestimationsSpecify and estimate linear regressionmodels and discrete choice modelsApply methods and interpret resultsusing statistical softwareDesign and perform stated‐preferencestudyDiscuss and compare linear regressionmodels and discrete choice models andtheir attributesXXXXXXXXXHow the ILOs in particular are addressed through case studies’ exercises?Intended LearningObjectivesIdentify appropriate methodsfor transportation, traffic andspatial data collection.Understand transportationand geoinformation dataneedsUnderstand the role samplingthe data collectionUse descriptive statistics forthe analysis and preparationof dataPerform outlier analysisPerform statistical inferencefor hypothesis testing andinterval estimationsCase Study ICase Study IIUse differenttransport data tounderstand themovement patternwithin the giventransport networkAnalyse differentdata from differentsources and timeslices and reflect theimpactsUtilising partialvideo observationdata to estimatetravel timeEstimate mean,mode median from anumber of selecteddatasets, comparethe plots andcalculate variancecovariance matricesCompare differentobserved andestimated variables,create a scatter plot,and discussCompare thecorrespondingintervals and reflecton the effect of thesample size of theused datasetsSpecify and estimate linearregression models anddiscrete choice modelsApply methods and interpretresults using statisticalsoftwareDevelop two modelsbased on differentperiod of theobserved data andanalyse and comparethe resultsConstruct twodifferent modelsbased on the givendata, analyse theresults, and reflectUse excelspreadsheet to dorandom sampling,descriptive analysisand statistical testsUse Excel, MATLABor any other softwarethat can runregressions in orderto produce expectedresultsCase Study IIIDevelop a-priorihypotheses aboutvariables whichlikely to beimportantin explaining thebehavioursExplore the dataand report averagevalues, standarddeviations, andranges of values forthe variablesSelect the bestmodel based on apriori hypotheses,statistics, andcausalrelationships.Formulate andestimate andpresent your besttwo modelspecificationsUsing MATLAB,estimate andpresent your besttwo modelspecifications,and interpret theestimation resultsof the two models.

Design and perform stated‐preference studyDiscuss and compare linearregression models anddiscrete choice models andtheir attributesReport modelcomparison,explain which onebetter and whyGrading Scheme:In general, as this course is teaching analytical skill to solve the problems, the grading scheme isdesigned based on the student ability to demonstrate their understanding of the methods and solve thetested problems given. Thus, the exam and exercises are both graded independently according to thefollowing criteria:A : The student has presented solutions to all parts of the problem. The solutions are clearly motivated,correct and the results are discussed thoroughly and quantitatively. Minor obvious typos can beaccepted.C: The student's solutions treat most of the problem and is largely correct but may containcomputational errors and lack motivation of a few steps. A qualitative discussion of the results ispresent. Faulty arguments and inconsistent results can be accepted to a minor degree.E: The student's exam demonstrates a basic understanding of the major issues and concepts treatedin the problem. The student has attempted to make proper progress towards a solution to the problem.A discussion at the basic level is present.F: A grade F is given if the criteria for a grade E are not achieved.Examination PRO1 - Project Assignments, 3.5, grading scale: A, B, C, D, E, FX, FTENA - Written Examination, 4.0, grading scale: A, B, C, D, E, FX, FA mandatory written examination equivalent to 4.0 cr with grading scale A‐FA mandatory project assignment equivalent to 3.5 cr with grading scale A‐FEXAMThe course then will have grading scale A‐F, where the course grade will be determined by the grade onthe written examination and the project work.ABCDETeaching StaffAABBCDBABCCEPROJECTCBCCDEAK: Anders Karlström, amail@kth.seDCDDDEECDDEE

FN: Fatemeh Naqavi, naqavi@kth.seOV: Oskar Västberg, oskarbg@kth.seExaminerAnders Karlström, amail@kth.se

Specify and estimate linear regression models and discrete choice models . S. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, 1987. . II Case Study III Identify appropriate methods for transportation, traffic and spatial data

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