Distilling actionable insights from big travel demand datasets for city planning
Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transpo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chua, Alvin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Transformative social innovation and (dis)empowerment - Avelino, Flor ELSEVIER, 2020, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:83 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.retrec.2020.100850 |
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ELV051877589 |
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| 520 | |a Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. | ||
| 520 | |a Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. | ||
| 650 | 7 | |a Ride-hailing |2 Elsevier | |
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| 650 | 7 | |a Trip generation rates |2 Elsevier | |
| 650 | 7 | |a Land use transport interaction |2 Elsevier | |
| 700 | 1 | |a Ow, Serene |4 oth | |
| 700 | 1 | |a Hsu, Kevin |4 oth | |
| 700 | 1 | |a Yazhe, Wang |4 oth | |
| 700 | 1 | |a Chirico, Michael |4 oth | |
| 700 | 1 | |a Zhongwen, Huang |4 oth | |
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10.1016/j.retrec.2020.100850 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001186.pica (DE-627)ELV051877589 (ELSEVIER)S0739-8859(20)30046-9 DE-627 ger DE-627 rakwb eng 300 600 VZ 83.31 bkl 71.43 bkl 50.14 bkl Chua, Alvin verfasserin aut Distilling actionable insights from big travel demand datasets for city planning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Ride-hailing Elsevier Land use activities Elsevier Public transport Elsevier Trip generation rates Elsevier Land use transport interaction Elsevier Ow, Serene oth Hsu, Kevin oth Yazhe, Wang oth Chirico, Michael oth Zhongwen, Huang oth Enthalten in Elsevier Avelino, Flor ELSEVIER Transformative social innovation and (dis)empowerment 2020 Amsterdam [u.a.] (DE-627)ELV002484455 volume:83 year:2020 pages:0 https://doi.org/10.1016/j.retrec.2020.100850 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.31 Wirtschaftswachstum VZ 71.43 Technologische Faktoren Soziologie VZ 50.14 Technik in Beziehung zu anderen Gebieten VZ AR 83 2020 0 |
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10.1016/j.retrec.2020.100850 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001186.pica (DE-627)ELV051877589 (ELSEVIER)S0739-8859(20)30046-9 DE-627 ger DE-627 rakwb eng 300 600 VZ 83.31 bkl 71.43 bkl 50.14 bkl Chua, Alvin verfasserin aut Distilling actionable insights from big travel demand datasets for city planning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Ride-hailing Elsevier Land use activities Elsevier Public transport Elsevier Trip generation rates Elsevier Land use transport interaction Elsevier Ow, Serene oth Hsu, Kevin oth Yazhe, Wang oth Chirico, Michael oth Zhongwen, Huang oth Enthalten in Elsevier Avelino, Flor ELSEVIER Transformative social innovation and (dis)empowerment 2020 Amsterdam [u.a.] (DE-627)ELV002484455 volume:83 year:2020 pages:0 https://doi.org/10.1016/j.retrec.2020.100850 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.31 Wirtschaftswachstum VZ 71.43 Technologische Faktoren Soziologie VZ 50.14 Technik in Beziehung zu anderen Gebieten VZ AR 83 2020 0 |
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10.1016/j.retrec.2020.100850 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001186.pica (DE-627)ELV051877589 (ELSEVIER)S0739-8859(20)30046-9 DE-627 ger DE-627 rakwb eng 300 600 VZ 83.31 bkl 71.43 bkl 50.14 bkl Chua, Alvin verfasserin aut Distilling actionable insights from big travel demand datasets for city planning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Ride-hailing Elsevier Land use activities Elsevier Public transport Elsevier Trip generation rates Elsevier Land use transport interaction Elsevier Ow, Serene oth Hsu, Kevin oth Yazhe, Wang oth Chirico, Michael oth Zhongwen, Huang oth Enthalten in Elsevier Avelino, Flor ELSEVIER Transformative social innovation and (dis)empowerment 2020 Amsterdam [u.a.] (DE-627)ELV002484455 volume:83 year:2020 pages:0 https://doi.org/10.1016/j.retrec.2020.100850 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.31 Wirtschaftswachstum VZ 71.43 Technologische Faktoren Soziologie VZ 50.14 Technik in Beziehung zu anderen Gebieten VZ AR 83 2020 0 |
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10.1016/j.retrec.2020.100850 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001186.pica (DE-627)ELV051877589 (ELSEVIER)S0739-8859(20)30046-9 DE-627 ger DE-627 rakwb eng 300 600 VZ 83.31 bkl 71.43 bkl 50.14 bkl Chua, Alvin verfasserin aut Distilling actionable insights from big travel demand datasets for city planning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Ride-hailing Elsevier Land use activities Elsevier Public transport Elsevier Trip generation rates Elsevier Land use transport interaction Elsevier Ow, Serene oth Hsu, Kevin oth Yazhe, Wang oth Chirico, Michael oth Zhongwen, Huang oth Enthalten in Elsevier Avelino, Flor ELSEVIER Transformative social innovation and (dis)empowerment 2020 Amsterdam [u.a.] (DE-627)ELV002484455 volume:83 year:2020 pages:0 https://doi.org/10.1016/j.retrec.2020.100850 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.31 Wirtschaftswachstum VZ 71.43 Technologische Faktoren Soziologie VZ 50.14 Technik in Beziehung zu anderen Gebieten VZ AR 83 2020 0 |
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10.1016/j.retrec.2020.100850 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001186.pica (DE-627)ELV051877589 (ELSEVIER)S0739-8859(20)30046-9 DE-627 ger DE-627 rakwb eng 300 600 VZ 83.31 bkl 71.43 bkl 50.14 bkl Chua, Alvin verfasserin aut Distilling actionable insights from big travel demand datasets for city planning 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. Ride-hailing Elsevier Land use activities Elsevier Public transport Elsevier Trip generation rates Elsevier Land use transport interaction Elsevier Ow, Serene oth Hsu, Kevin oth Yazhe, Wang oth Chirico, Michael oth Zhongwen, Huang oth Enthalten in Elsevier Avelino, Flor ELSEVIER Transformative social innovation and (dis)empowerment 2020 Amsterdam [u.a.] (DE-627)ELV002484455 volume:83 year:2020 pages:0 https://doi.org/10.1016/j.retrec.2020.100850 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.31 Wirtschaftswachstum VZ 71.43 Technologische Faktoren Soziologie VZ 50.14 Technik in Beziehung zu anderen Gebieten VZ AR 83 2020 0 |
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Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. 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Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. |
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Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. |
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Working towards a more data-informed land use, amenities and infrastructure planning process, the Singapore Urban Redevelopment Authority (URA) harnesses big data and spatial analytics to deepen its understanding of urban activity and mobility patterns. Big travel demand datasets from public transport and ride-hailing services enable planners to observe mobility patterns at a high level of detail for large numbers of users, trips, and trip types. Since August 2018, the URA has been working with leading technology company and ride-hailing operator Grab to understand how daily commute patterns vary between existing and new transport modes, and how the volume of activities in each area evolves across different times of day. This paper describes the novel dataset and analytical techniques utilised to study the relationship between urban activity and mobility. It will also report how spatiotemporal characteristics of the urban environment, such as land use mix, location accessibility, and peak-hour travel demand, influence commutes by different modes in each area. By studying mobility over a range of travel modes, this method of analysis will provide city planners with richer insights to better assess infrastructure requirements for new developments. The findings are also useful for emerging transport providers, who can improve service delivery across short- and medium-term time scales. |
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