Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling

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dc.contributor.advisorZuidgeest, Marken_ZA
dc.contributor.authorWillenberg, Darrenen_ZA
dc.date.accessioned2018-02-07T09:03:32Z
dc.date.available2018-02-07T09:03:32Z
dc.date.issued2017en_ZA
dc.description.abstractThe MyCiTi is currently generating large volumes of raw transactional information in the form of commuter smartcard transactions, which can be considered Big Data. Agent Based modelling (ABM) has been applied internationally as a means of deriving actionable intelligence from Big Data. It is proposed that ABM can be used to unlock the hidden potential within the aforementioned data. This paper demonstrates how to go about developing and calibrating a MATSim-based ABM to analyse AFC data. It is found that data formatting algorithms are critical in the preparation of data for modelling activities. These algorithms are highly complex, requiring significant time investment prior to development. Furthermore, the development of appropriate ABM calibration parameters requires careful consideration in terms of appropriate data collection, simulation testing, and justification. This study serves as strong evidence to suggest that ABM is an appropriate analysis technique for MyCiTi data systems. Validation exercises reveal that ABM is able to calculate on board bus usage and system behaviour with a strong degree of accuracy (R-squared 0.85). It is however recommended that additional research be conducted into more detailed calibration activities, such as fine-tuning agent behaviour during simulation. Ultimately this research study achieves its explorative objectives of model development and testing, and paves a way forward for future research into the practical applications of Big Data and ABM in the South African context.en_ZA
dc.identifier.apacitationWillenberg, D. (2017). <i>Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Civil Engineering. Retrieved from http://hdl.handle.net/11427/27362en_ZA
dc.identifier.chicagocitationWillenberg, Darren. <i>"Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Civil Engineering, 2017. http://hdl.handle.net/11427/27362en_ZA
dc.identifier.citationWillenberg, D. 2017. Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Willenberg, Darren AB - The MyCiTi is currently generating large volumes of raw transactional information in the form of commuter smartcard transactions, which can be considered Big Data. Agent Based modelling (ABM) has been applied internationally as a means of deriving actionable intelligence from Big Data. It is proposed that ABM can be used to unlock the hidden potential within the aforementioned data. This paper demonstrates how to go about developing and calibrating a MATSim-based ABM to analyse AFC data. It is found that data formatting algorithms are critical in the preparation of data for modelling activities. These algorithms are highly complex, requiring significant time investment prior to development. Furthermore, the development of appropriate ABM calibration parameters requires careful consideration in terms of appropriate data collection, simulation testing, and justification. This study serves as strong evidence to suggest that ABM is an appropriate analysis technique for MyCiTi data systems. Validation exercises reveal that ABM is able to calculate on board bus usage and system behaviour with a strong degree of accuracy (R-squared 0.85). It is however recommended that additional research be conducted into more detailed calibration activities, such as fine-tuning agent behaviour during simulation. Ultimately this research study achieves its explorative objectives of model development and testing, and paves a way forward for future research into the practical applications of Big Data and ABM in the South African context. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling TI - Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling UR - http://hdl.handle.net/11427/27362 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27362
dc.identifier.vancouvercitationWillenberg D. Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Civil Engineering, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27362en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Civil Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherTransport Studiesen_ZA
dc.titleQuantifying MyCiTi supply usage via Big Data and Agent Based Modellingen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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