This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in New South Wales (NSW), connected by public transport (PT) networks. The SA2 regions of New South Wales connected by Sydney trains (T1-T9) and Metro services (Metro North West line) have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here. Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets. The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here. Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets. The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file here. Human mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets. The Mobility Australia project received investment (https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). The original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

This dataset supports measure M.A.3 of SD 2023. The source of the data is the Listening to the Workforce Survey, an annual survey conducted by the City of Austin of it's employees. Each row represents the overall average mode split among City of Austin employees based on the responses to the survey. This dataset can be used to understand the trend in predicted and actual average travel time. View more details and insights related to this measure on the story page : https://data.austintexas.gov/stories/s/39pk-y8ma

This dataset presents the Spatial Network Analysis for Multimodal Urban Transport Systems (SNAMUTS) indicators by activity node locations for the year of 2011. SNAMUTS Australia is a multiple indicator tool to assess public transport network performance and land use-transport integration in Australia's five largest metropolitan areas (Adelaide, Brisbane, Melbourne, Perth, Sydney), at SA1 level and for specified activity nodes in the census years 2011, 2016 and 2021. SNAMUTS is a decision-making support tool designed to inform strategies for public transport infrastructure and service improvements, land use intensification and overall metropolitan strategic planning. It also allows for timeline and city-to-city comparisons on public transport performance and accessibility indicators between about 30 cities on four continents. SNAMUTS has been developed and refined with funding from the Australian Research Council (DP110104884), the European Science Foundation (COST TU1002), the Cities of Melbourne, Manningham and Perth, internal support through RMIT University, Curtin University and University of Melbourne, the Sustainable Built Environment National Research Centre (SBENRC), AURIN and in-kind support by the University of Porto, Goudappel Coffeng, University of Amsterdam, University of Gothenburg, Norwegian University of Life Sciences, Western Norway University of Applied Sciences, Concordia University and University of Westminster. These are known as the eight key SNAMUTS indicators, they include: - Transfer intensity (degree centrality) - Ease of movement (closeness centrality), - Geographical reach of public transport journeys (30-minute contour catchment) - Spatial concentration of public transport journey paths (betweenness centrality and transfer propensity) - Flexibility of movement (nodal connectivity) - Future-proofness of the network (nodal resilience) - A composite index for overall public transport accessibility. It is based on public transport network and timetable information available publicly through transport agencies (Adelaide Metro, Translink, PTV, Transperth, TfNSW) and ABS census data (usual residential population and journey-to-work destinations) for the three years (2011, 2016, 2021). This data can be viewed in conjunction with the corresponding routes dataset: "SNAMUTS - Route Segment (Polyline) 2011". For more information, please refer to the SNAMUTS website. A full list of SNAMUTS publications can be found at http://www.snamuts.com/publications.html.

Kilometers travelled data (km) from residential and work transport across all modes within and to City of Sydney local government area from baseline 2005/06 to data available for the latest financial year. This data is estimated using statistical datasets provided by the NSW government and other datasets.

This dataset provides an indicator of historic rail occupancy for individual rail services from July 2019. Data included in ROAM - Rail Opal Assignment Model - includes Sydney Trains (T1 - T9) and Metro (Metro North West line) services. Note: With the introduction of the new City and Southwest Metro line, Transport is moving to a new Opal Allocation Model to handle the complexity that these new services will bring. The legacy Rail Opal Allocation Model (ROAM) data will be replaced with a new Near Real Time (NRT) ROAM from May 2024.

No notes provided

This data is part of the strategic transport modelling undertaken for Urban Transport Crowding and Congestion, a supplementary report of the Australian Infrastructure Audit 2019. The report looks at historical data from 2016, and also provides a projection of what access to jobs and services in Sydney, the Hunter and Illawarra could look like in 2031, if infrastructure investment do not keep up with the pace of demand. Network performance in Sydney, the Hunter and Illawarra showcases their findings as interactive maps. The following resources are available, links will be provided that lead you directly to the current source of data. * Access to jobs by car within 30 minutes * Access to jobs by public transport within 30 minutes * Access to hospitals