Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm
Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and ad...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
MDPI - Multidisciplinary Digital Publishing Institute
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/74682 |
| _version_ | 1848763343199797248 |
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| author | Hasan, U. Whyte, Andrew Al Jassmi, H. |
| author_facet | Hasan, U. Whyte, Andrew Al Jassmi, H. |
| author_sort | Hasan, U. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development's public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents' mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment. |
| first_indexed | 2025-11-14T11:01:57Z |
| format | Journal Article |
| id | curtin-20.500.11937-74682 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:01:57Z |
| publishDate | 2018 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-746822019-03-14T03:48:58Z Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm Hasan, U. Whyte, Andrew Al Jassmi, H. Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development's public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents' mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment. 2018 Journal Article http://hdl.handle.net/20.500.11937/74682 10.3390/buildings9010001 http://creativecommons.org/licenses/by/4.0/ MDPI - Multidisciplinary Digital Publishing Institute fulltext |
| spellingShingle | Hasan, U. Whyte, Andrew Al Jassmi, H. Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title | Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title_full | Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title_fullStr | Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title_full_unstemmed | Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title_short | Life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| title_sort | life-cycle asset management in residential developments building on transport system critical attributes via a data-mining algorithm |
| url | http://hdl.handle.net/20.500.11937/74682 |