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Designing multimodal public transport networks using metaheuristics
Dissertation (MSc)--University of Pretoria, 2009.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613521964957696 |
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| access_status_str | Open Access |
| author2 | Joubert, Johan W. |
| author_browse | Joubert, Johan W. |
| author_facet | Joubert, Johan W. |
| collection | Thesis |
| dc_rights_str_mv | ©University of Pretoria 2008 E1191/ |
| description | Dissertation (MSc)--University of Pretoria, 2009. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/23417 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:28.861Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/23417 Designing multimodal public transport networks using metaheuristics Joubert, Johan W. mfletterman@hotmail.com Fletterman, Manuel Metaheuristic Operations research Simulated annealing Geographic information system (GIS) Multimodal network design Bus stop placement Transit network design problem Transport system Transport network Public transport UCTD Dissertation (MSc)--University of Pretoria, 2009. The public transport system in South Africa is in a precarious state, capturing no more than 50% of the passenger market. The three public transport modes that are currently utilized—train, bus, and minibus-taxi—are competing for market share instead of complementing one another. Furthermore, most public transport networks have not been properly redesigned over the past three decades. Improvements were initiated reactively in the past: transit stops and routes were added or removed from the network when demand fluctuated. This reactive process has diminished the confidence of commuters in the public transport networks, forcing commuters to use private transport. A proactive redesign method is needed—one that includes all the modes of public transport, and anticipates an increase in demand and rapid development in geographic areas, while ensuring good accessibility to the network. Current network design models do not include multiple modes of public transport, and are based on the geographical layout of developed cities and their particularities, which makes them unsuitable for the South African environment with its unique land use disparities. This dissertation proposes a multimodal network design model that is capable of designing real world and large scale networks for the South African metropolitan areas. The City of Tshwane Metropolitan Municipality (CTMM) transport network area was used to develop and test the model, which consists of four components. The Geographic Information System (GIS) component has a central role in storing, manipulating, and exchanging the geographic data within the model. For the GIS the appropriate input data is identified, and a design for the geo-database is proposed. The Population Generation Algorithm (PGA) component translates the demographic data into point data representing the transit demand in the study area. The Bus Stop Placement Algorithm (BSPA) component is a metaheuristic that searches for near-optimal solutions for the placement of bus stops in the study area. A novel solution approach proposed in this dissertation uses geographic data of commuters to evaluate the bus stop placement in the study area. The Multimodal Network Design Algorithm (MNDA) component also employs a metaheuristic, enabling the design of near-optimal multimodal networks. The addition of multiple modes to the Transit Network Design Problem (TNDP) is also a novel and significant contribution. The two metaheuristic components are first tested on a test network, and subjected to a comprehensive sensitivity analysis. After identifying suitable parameter values and algorithm settings, the components are applied to the entire CTMM. Industrial and Systems Engineering unrestricted 2013-09-06T15:17:34Z 2009-04-08 2013-09-06T15:17:34Z 2008-09-02 2009-04-08 2009-01-16 Dissertation 2008 E1191/gm http://hdl.handle.net/2263/23417 http://upetd.up.ac.za/thesis/available/etd-01162009-154801/ ©University of Pretoria 2008 E1191/ application/pdf University of Pretoria |
| spellingShingle | Metaheuristic Operations research Simulated annealing Geographic information system (GIS) Multimodal network design Bus stop placement Transit network design problem Transport system Transport network Public transport UCTD Designing multimodal public transport networks using metaheuristics |
| title | Designing multimodal public transport networks using metaheuristics |
| title_full | Designing multimodal public transport networks using metaheuristics |
| title_fullStr | Designing multimodal public transport networks using metaheuristics |
| title_full_unstemmed | Designing multimodal public transport networks using metaheuristics |
| title_short | Designing multimodal public transport networks using metaheuristics |
| title_sort | designing multimodal public transport networks using metaheuristics |
| topic | Metaheuristic Operations research Simulated annealing Geographic information system (GIS) Multimodal network design Bus stop placement Transit network design problem Transport system Transport network Public transport UCTD |
| url | http://hdl.handle.net/2263/23417 http://upetd.up.ac.za/thesis/available/etd-01162009-154801/ |