Transport Performance and Analytics (TPA) provides projections of employment at the small area (Travel Zone or TZ) level for the Sydney Greater Metropolitan Area (GMA). The GMA includes the Sydney Greater Capital City Statistical Area (GCCSA), the Southern Highlands and Shoalhaven SA4, Illawarra SA4, Newcastle and Lake Macquarie SA4, and Lower Hunter, Port Stephens, and Maitland SA3s, as defined by the Australian Bureau of Statistics (ABS). There are 2,949 Travel Zones in the Sydney GMA.

TPA provides projections of workforce, employment, and population at Travel Zone level for the Sydney Greater Metropolitan Area (GMA).

The Transport Performance and Analytics (TPA) produces travel forecasts using the Strategic Travel Model (STM). This model is a world class tool that projects travel patterns in the Sydney Greater Metropolitan Area under different land use, transport and pricing scenarios. It can be used to test alternative settlement, employment and transport policies, to identify likely future capacity constraints, or to determine potential usage levels of proposed new transport infrastructure or services. The STM is built largely in the EMME transport modelling software. It is comprised of a series of models and processes that attempt to replicate, in a simplified manner, people’s travel choices and behaviour under a given scenario. The STM combines our understanding of travel behaviour with likely population and employment size and distribution, and likely road and public transport networks and services to estimate future travel under different strategic land use and transport scenarios. The STM produces travel forecasts by origin (2,690) and destination (2,690) STM zones for: The Sydney Greater Metropolitan Area which includes the Sydney Statistical Division, Newcastle Statistical Subdivision and Illawarra Statistical Division. 5 yearly intervals from the latest Census year up to a 35-year horizon 9 travel modes: Car driver, Car passenger, Rail, Bus, Light rail, Ferry, Bike, Walk and Taxi 7 purposes: Work, Business, Primary/Secondary/Tertiary education, Shopping, Other 24 hour, average workday (Monday to Friday excluding public holidays) am/pm peak, interpeak and evening travel

Travel Zones (TZs) are the spatial unit of geography defined by Transport Performance and Analytics (TPA), a business unit within Transport for NSW (TfNSW). The TZ spatial layer is applied to data sources used by TfNSW for transport modelling and analysis, including the Household Travel Survey and the Census 2016 Journey to Work data. The Australian Bureau of Statistics (ABS) Statistical Area boundaries form the foundation of the TZ. Generally, a TZ is larger than a Statistical Area Level 1 or Mesh Block, both ABS geography definitions. The ABS Statistical Areas are based on population counts whereas TZ boundaries are defined using population, employment, housing and transport infrastructure. TZs are designed to have standardised trip generation levels across all zones. This causes zones to be different sizes across the metropolitan area. As with many other spatial boundaries, TZs tend to be small in areas with high land-use densities and larger in areas of lower density.

The BTS is the primary source of forecasts of population and dwellings at the small area (travel zone) level for the Sydney Greater Metropolitan Area. This area includes the Sydney Greater Capital City Statistical Area, and the Illawarra and Hunter regions. There are 2,949 travel zones in the Sydney GMA. The latest September 2014 Release Population Forecasts provide forecasts at travel zone level for the following variables: Population (Estimated Resident Population) by 5-year Age categories by Sex Occupied Private Dwellings (Households) Population in Occupied Private Dwellings Population in Non-Private Dwellings The forecasts in this release are five-yearly, from 2011 to 2041.

Transport Performance and Analytics (TPA) has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991-2006 - Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA) Note that GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

Transport Performance and Analytics (TPA) has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991-2006 - Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA) Note that GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

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 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 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.