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Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling
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 propose...
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| Format: | Thesis |
| Language: | English |
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Department of Civil Engineering
2018
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| _version_ | 1867613741015629824 |
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| access_status_str | Open Access |
| author | Willenberg, Darren |
| author2 | Zuidgeest, Mark |
| author_browse | Willenberg, Darren Zuidgeest, Mark |
| author_facet | Zuidgeest, Mark Willenberg, Darren |
| author_sort | Willenberg, Darren |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27362 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:57.774Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Department of Civil Engineering |
| publisherStr | Department of Civil Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/27362 Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling Willenberg, Darren Zuidgeest, Mark Transport Studies 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. 2018-02-07T09:03:32Z 2018-02-07T09:03:32Z 2017 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/27362 eng application/pdf Department of Civil Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Transport Studies Willenberg, Darren Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| thesis_degree_str | Master's |
| title | Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| title_full | Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| title_fullStr | Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| title_full_unstemmed | Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| title_short | Quantifying MyCiTi supply usage via Big Data and Agent Based Modelling |
| title_sort | quantifying myciti supply usage via big data and agent based modelling |
| topic | Transport Studies |
| url | http://hdl.handle.net/11427/27362 |
| work_keys_str_mv | AT willenbergdarren quantifyingmycitisupplyusageviabigdataandagentbasedmodelling |