Bus lanes are sign-posted or marked as bus lanes. They are provided primarily for buses, but can also be used by: \* Taxis \* Hire cars (but not rental cars) \* Motorcycles and bicycles \* Emergency vehicles \* Special purpose vehicles and vehicles also operated by or under the direction of Roads and Maritime Services. This dataset is supplied by Transport Planning and contains all known Bus Lanes within the Greater Sydney Metro area, Newcastle and Wollongong. The data files are provided in shapefile format.

CEEDAR is a Data Asset Register logging relevant datasets with accompanying information and metadata (where available).

Commuter car parking facilities are located close to many public transport hubs throughout NSW, and help you to connect easily to Metro, Train, Bus and Ferry services. This dataset captures the location of the current and proposed locations of the various commuter car parks in NSW. It shows the number of standard, accessible and motorcycle spaces at each location. * Standard CCP (Commuter Car Park) spaces refer to your standard length and are available for the everyday car user. * Accessible Commuter Car Park spaces are designated car spaces for users with a accessibility parking permit only. * Motorcycle spaces are designated spaces for motorcycle parking only. * 2017-2020 changes shows how many spaces were added or removed in each location (where applicable). * Planned/Future spaces shows how many standard Commuter Car Park and accessible Commuter Car Park spaces are planned for each location, but yet to have a confirmed implementation date.

The data set contains information about the TEM images of hydrochar alone, with phosphate (b), and with montmorillonite (c) at a pH of 6.0; SEM images of hydrochar alone (a, b), with montmorillonite (c, d), and with a combination of montmorillonite and phosphate (e, f) and the corresponding EDX spectra (g, h) at a pH of 6.0 (a, c, e, g) and 9.0 (b, d, f, h); .Capillary pressure curves (a,b) and pore size distribution determined by means of mercury intrusion porosimetry (MIP) (c,d) for the hydrochar samples prepared at pH 6.0 (a,c) and 9.0 (b,d); .FTIR spectra of the synthesized hydrochar in solutions with different pH values; Zeta potentials of hydrochar with and without montmorillonite (M) and/or phosphate (P), quartz sand and aluminum oxide-coated sand in a 10 mM NaCl solution as a function of the pH (a) and the concentrations of NaCl solution at pH 6.0 (b) and 9.0 (c); .Hydrodynamic radius of hydrochar with and without montmorillonite (M) in the absence or presence of phosphate (P) under different NaCl concentrations at pH 6.0 (a) and 9.0 (b); Observed (dots) and fitted (lines) breakthrough curves (BTCs) of 0.2 g L-1 hydrochar under different NaCl concentrations in uncoated sand (a, b) and aluminum oxide-coated sand (c, d) at pH 6.0 (a, c) and 9.0 (b, d), respectively. This dataset is associated with the following publication: Yang, J., M. Chen, H. Yang, N. Xu, G. Feng, Z. Li, C. Su, and D. Wang. Surface Heterogeneity Mediated Transport of Hydrochar Nanoparticles in Heterogeneous Porous Media. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH. Ecomed Verlagsgesellschaft AG, Landsberg, GERMANY, 27(26): 32842-32855, (2020).

This dataset provides results data from the Fare Compliance Survey Results reports from November 2012 to the latest report (the surveys were not conducted in 2020 due to Covid). The fare compliance survey is conducted twice yearly in May and November, and is designed to measure the incidence of non-compliance and associated revenue loss across the public transport network. A report from each survey is also published on: [https://www.transport.nsw.gov.au/news-and-events/reports-and-publication...](https://www.transport.nsw.gov.au/news-and-events/reports-and-publications/fare-compliance-survey-results).

FEHM (Finite Element Heat and Mass Transfer Code) is a continuum-scale simulator developed by Los Alamos National Laboratory. FEHM is used to simulate groundwater and contaminant flow and transport in deep and shallow, fractured and un-fractured porous media.

A shapefile of gridded squares for French urban centre 200 metre grid with following fields:id: Identifier unique to this urban centre population: Global Human Settlement Layer population estimate within 200 metre cellgeometry: Grid polygons within_uc: Whether the 200 metre cell is within the defined urban centreaccess_pop: The median sum population that can be reached from the centroid of the 200 metre cell within a 45 minute commute by walking, bus and trainproxim_pop: The sum proximal population within an 11.25 kilometre radius of the centroid of the 200 metre celltrans_perf: The transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nm: Name of the urban centrecountry_nm: Name of the country that the urban centre belongs to

Google Environmental Insights Explorer (EIE) data on transportation showing statistics on total emissions and distance travelled by mode of transport and journey type (inbound, outbound and within boundary) in Birmingham. Note: Google Environmental Insights Explorer (EIE) data is for all trips that start or end within the city boundary and based on estimates from aggregated, anonymized location history data.

HE providers currently submit an annual data return with information about their estates. The data relates to the academic year 1 August to 31 July and is submitted in the winter following the end of the academic year. We quality assure and process the data which we then publish in the following spring/summer. Published tables cover buildings and spaces, energy, emissions and waste, transport and environment, and finances and people.

Some Intercity trains may be longer than the platform at their destination. This dataset has detailed information on the Cars (carriages) that customers can alight from at stations on the following Intercity lines: \* Blue Mountains \* Central Coast & Newcastle \* Hunter \* South Coast \* Southern Highlands To safely exit the train you must travel in the correct car when travelling. It’s important that you check and travel in the correct car so you can exit the train at your destination. Keep in mind, Car One is always at the front of the train. This dataset captures all the Intercity Train Platforms, the maximum Cars per train, if the train aligns with the front or rear of the platform, and how many cars are off the platform for that respective station. This data is also available in GTFS-Vehicles format in the [Sydney Trains GTFS bundle](https://opendata.transport.nsw.gov.au/node/332/exploreapi#!/sydneytrains/GetSydneyTrains) Please refer to the [Sydney Trains Realtime GTFS & GTFS- R Technical Document](https://opendata.transport.nsw.gov.au/sites/default/files/Real%20Time%20Train%20Technical%20Document%20v3_2_open%20data.pdf) in order to determine the set type and number of cars for each timetabled trip.

**This information is provided by Liverpool City Council.** Liverpool City has almost 5000 car spaces available in or near the city centre. They include free and low-cost options, short and long-stay. Use the Go to Resource to view the data source.

Surfactant concentration is one of the important factors in determining foam generation and propagation.When surfactant solutionis flowing in a reservoir formation, surfactants will be diluted by flow dispersion, retained in dead-end pores, adsorbed on rock surfaces, or precipitated due to ion exchange. All these physical and chemical aspects complicate the problem of foam. The loss of surfactant will be detrimental to the performance of gas foam. Information of surfactant concentration profiles in reservoir formations is essential for gas foaming technique development. This research was designed to investigate the transport and adsorption phenomena of surfactants in porous media. The major objective of this research is to investigate with mathematical models the transport and dynamic adsorption of surfactants in porous media. The mathematical models have taken into account the convection, dispersion, capacitance, and adsorption effects on concentrations of surfactants. Numerical methods and computer programs have been developed which can be used to match experimental results and to determine the characterization parameters in the models. The models can be included in foam simulation programs to calculate surfactant concentration profiles in porousmedia. A flow experimental method was developed to measure the effluent surfactant concentration, which will be used to determine the model parameters. Commercial foaming agent Alipal CD-128 was used in this study. Equilibrium adsorption and surfactant precipitation have been tested. Tracer solutions with a nonadsorbing solute such as dextrose and sucrose were used to determine the dispersion parameters for the experimental sandpack; thus, the adsorption of the surfactant in the test sand can be identified with an adequate model.

What would happen to Alaska's natural gas once it reaches the end of the proposed pipeline, 1,700 miles from Prudhoe Bay? The gas would flow into a vast network of Canadian and U.S. pipelines assembled over the past 60 years. Some key components of that network were built or expanded in the early 1980s in anticipation of Alaska gas starting to flow back then. Those components went into service without Alaska gas and helped Canada double its natural gas exports to the United States in the 1980s, then double them again in the 1990s. In all, the entire network today can move 15 billion to 20 billion cubic feet a day of natural gas, roughly three to four times the volume the Alaska pipeline would deliver to the British Columbia-Alberta border northwest of Edmonton. Of course, the network still moves billions of cubic feet of gas daily. But the volume it handles has been declining, leaving room for Alaska gas, and even if the flow is relatively flush when the Alaska pipeline is finished, the network's capacity could be expanded. No longer is there serious talk of needing a pipeline stretching all the way from Prudhoe Bay to Chicago. But why end the Alaska pipeline near the B.C.-Alberta border as opposed to somewhere else? The answer is simple: Three major North American gas pipeline systems converge there, in the heart of some of Canada's hottest natural gas plays.

This data is maintained by the DCS Spatial Services. If you have any questions with regards to this dataset, please contact [https://www.spatial.nsw.gov.au/contact\_us](https://www.spatial.nsw.gov.au/contact_us) Traffic control devices are used to control, calm, slow or impede the movement of traffic. Sub types that form this feature class includes: \* Gate - a structure used to regulate movement associated with road or rail \* Impediment - a structure generally associated with a road that controls or varies the normal travel of vehicles or pedestrians. \* Level Crossing - a place where a road and railway intersect at the same level \* Toll Booth - a structure on a road which requires the user to pay a toll or fee to use the road \* Cattle Grid - an open floored structure designed to be crossed by motor vehicles \* Roundabout The notional midpoint of a roundabout that has been constructed to allow smooth integration of traffic. Generally only roundabouts forming a road centreline and associated with Road Segments classified as 'Roundabout' will be identified under this classification. Click on "Go to Resource" to view the data source.

NUFT-C (Nonisothermal, Unsaturated Flow and Transport with Chemistry) is a continuum-scale simulator developed by Lawrence Livermore National Laboratory. NUFT-C is used to simulate coupled fluid movement (multiple liquids and gas) and chemical reactions in saturated or unsaturated porous media.

In Sydney, the Blue Mountains, Central Coast, the Hunter and the Illawarra area, fares are calculated based on the distance travelled from tap on to tap off, payment method, the mode of transport, concession eligibility (or free travel) and any Opal benefits such as discounts and capped fares that apply. This dataset provides the Opal Distance for all modes of transport and the Opal Fare Values for each mode of transport. The Opal Fares Business Rules and Information document provides the latest supporting information. For additional information about Opal Fares, Default fares and how Opal Fares are calculated, visit the [Transport Fares and Payments](https://transportnsw.info/tickets-opal/opal/fares-payments) website.

This data is part of the strategic transport modelling undertaken for Outer Urban Public Transport Maps, which was released in October 2018. The [Outer Urban Public Transport Maps](https://www.infrastructureaustralia.gov.au/outer-urban-public-transport-maps-sydney) showcases through interactive maps, the comparative public transport network performance for Greater Sydney. The following resources are available, links have been provided that direct you to the current source of data. **Walking access to medium- to high-frequency public transport** This layer presents the proportion of people within walking distance to high-medium frequency public transport stops/stations in 2017. Walking distance is defined as 800 metres for heavy rail, and 400 metres for all other modes. High-frequency public transport is defined as having at least four services per hour during AM peak. This analysis was performed using 2017 timetables. **Public transport travel times to Sydney CBD** This layer presents travel times, by public transport during AM peak, to a series of destinations. This analysis was performed using 2017 public transport timetables. The layers represents the geographical extent of the inner, middle, and outer sectors used to perform the analysis in the report. **Public transport service frequency** This layer presents public transport stop frequency during weekday AM peak (8-9am) and weekday off peak (11am-12am). This analysis was performed using 2017 public transport timetables.

Operator contact details and location facilities for train stations, ferry wharves and bus interchanges. Gateway API is provided for legacy applications, this data is no longer updated and API will be retired in the the near future. To use these resources in your application please follow the CKAN api instructions in the resource description.

Realtime alerts at either the stop, trip, or service line level in GTFS-realtime format for Bus, Train, Ferry, Light Rail, Metro and Coaches.

Stop time updates for active trips, replacement vehicles, and changed stopping patterns in GTFS-realtime format for Buses, Ferries, Light Rail, Trains, Metro and Regional Bus Services (our regional services are at times referenced as "TCB" - Transport Connected Bus). An up to date list of all TCB services can be found [on the forum](https://opendataforum.transport.nsw.gov.au/t/new-real-time-regional-bus-data-is-now-available/2060).

Stop time updates for active trips, replacement vehicles, and changed stopping patterns in GTFS-realtime format for Metro and Sydney Trains.

Current vehicle positions in GTFS-realtime format for Buses, Ferries, Light Rail, Trains, Metro and Regional Bus Services (our regional services are at times referenced as "TCB" - Transport Connected Bus). An up to date list of all TCB services can be found [on the forum](https://opendataforum.transport.nsw.gov.au/t/new-real-time-regional-bus-data-is-now-available/2060).

Current vehicle positions in GTFS-realtime format for Metro and Sydney Trains.

Static timetables, stop locations, pathways for trains, and route shape information in GTFS format for operators that support realtime. Covers Buses, Ferries, Light Rail, Trains, Metro and Regional Bus Services (our regional services are at times referenced as "TCB" - Transport Connected Bus). An up to date list of all TCB services can be found [on the forum](https://opendataforum.transport.nsw.gov.au/t/new-real-time-regional-bus-data-is-now-available/2060).

Static timetables, stop locations, pathways for trains, and route shape information in GTFS format for operators that support realtime.

Throughout NSW there are buses that run within and between regional centres and towns, the fares for these buses are calculated based on the Fare Band and Number of sections travelled. This dataset provides the regional bus fares for each fare band and section, what the maximum adult single trip fare is and the corresponding adult daily ticket for the fare band and section. Bus operators may choose to set fares below these limits. Eligible concession holders will pay half the adult fare. Eligible concession card holders can access discounted bus fares in regional New South Wales with a [Regional Excursion Daily (RED) ticket](https://transportnsw.info/tickets-opal/regional-tickets-fares/regional-bus-tickets-fares#accordion-regional-excursion-daily-red-tickets-content). The RED ticket provides unlimited local daily bus travel for just $2.50. **The RED ticket is not valid on NSW TrainLink train or coach services.** For Timetables and route maps visit [Transport NSW](https://transportnsw.info/tickets-opal/regional-tickets-fares/regional-bus-tickets-fares).

The Regional Day Return indicator is a measure of regional centre connectivity under the [Future Transport Strategy](https://future.transport.nsw.gov.au/plans/regional-nsw-services-and-infrastructure-plan/customer-outcomes-for-regional-nsw). It aims to provide new connections for regional communities for commuting, attending medical or business appointments, shopping, recreational activities and visiting family and friends. Below is an example of the Regional Day Return indicator catchments  For more information about the trials, the process and datasets used please refer to the Regional Day Return Indicator document.

The Sydney light rail network is a light rail system serving the Australian city of Sydney. The network currently consists of three passenger routes, the L1 Dulwich Hill, L2 Randwick and L3 Kingsford lines. This dataset provides WCAG 2.0 compliant wayfinding maps for 42 Sydney light rail stops. Each map displays the local area map and the stop map providing information such as transport connections, customer assistance and tickets. **Current Sydney light rail network** 

This dataset captures the following public transport performance reports. Each resource is displayed as an interactive chart with filter options. The [TfNSW passenger travel](https://www.transport.nsw.gov.au/data-and-research/passenger-travel#Performance_reports) provides more information. * Sydney Metropolitan and Outer Metropolitan Bus Service Contract on time running results * Ferries service Reliability and On-Time Running Results * NSW Trains service reliability and punctuality results on all regional lines in New South Wales. * Sydney Trains and NSW TrainLink (Intercity) performance reports * New Customer On-Time measure – measuring our customer’s experience * Sydney Network - Historical Trains Punctuality Performance report * Performance Reporting for the Sydney Light Rail network Use the **GO TO RESOURCE** Option to view the individual reports.

The official Transport for NSW trip planning widget is available to display on your own website. Your customers can plan their own trip and be directed to transportnsw.info. To see this widget's Terms of Use please click [here](/trip-planning-widget-terms-use).

About the Project: We developed the KAPSARC Energy Model for Saudi Arabia (KEM-SA) to understand the dynamics of the country’s energy system. It is a partial equilibrium model formulated as a mixed complementarity problem to capture the administered prices that permeate the local economy. KEM-SA has been previously used to study the impacts of various industrial fuel pricing policies and improved residential efficiency on the energy economy. The passenger transportation model presented in this paper helps understand more of the end-use energy demand, and it is being integrated into KEM-SA. Key Points: Empirical estimates of fuel demand changes to price variation are based on historical consumption and prices, and can be applied as a single point estimate to a wide range of price movements. However, if fuel prices are set outside the boundaries of historical changes, policymakers may be concerned as to the validity of the empirically assessed price elasticity. We have developed a transport model to provide a techno-economic estimate of the price elasticity of fuel demand. It incorporates consumers’ choices as a result of several factors, including fuel substitutes, available transport modes, income, value of time and magnitude of price change Our findings from the application of this transport model to Saudi Arabia show that policymakers can have confidence that the empirical estimates are broadly valid, even for large changes and if prices move outside historical variations. In general, gasoline demand in Saudi Arabia is price inelastic due to the lack of fuel and modal substitutes. However, our approach suggests that the response may become more pronounced when the magnitude of the change increases. The cross-price elasticity of diesel is not constant. Demand for diesel will increase if gasoline price is raised significantly. Motorists may, for example, opt for diesel-based public transport, such as trains or buses, for long distance local trips. The change in jet-fuel use is negligible.

Thirty Minute City and Metro Strategic Centre Catchments is a foundation of the [Future Transport Strategy](https://future.transport.nsw.gov.au/future-transport-strategy/greater-sydney-network). Thirty Minute City establishes a metropolitan transport network which reinforces the metropolis of three cities, particularly the delivery of a 30-minute city where most residents in each city can access their metropolitan centre or cluster within 30 minutes by public transport. Metro Strategic Centre Catchments develops a network of 34 strategic centres with jobs, goods and services supported by a public transport, walking and cycling network. This would provide residents with a 30-minute public transport service to their nearest strategic centre seven days a week. The image below shows the thirty minute city catchments to the different strategic centre catchments.  The Thirty Minute City and Metro Strategic Centre Catchments Document provides you with detailed information regards the background of this initiative and the datasets used.

Static timetables, stop locations, and route shape information in General Transit Feed Specification (GTFS) format for all operators, including regional, trackwork and transport routes not available in realtime feeds. Returns ZIP file containing CSV files **Please note:** due to the large file size, the API explorer will not work for this resource, ie. 'EXPLORE API' function. To use this dataset please download the zip file using the 'DOWNLOAD' button below or use cURL to get directly. TfNSW GTFS Pathways extension as part of the GTFS Timetables Complete bundle released 2 June 2023.

Static timetables and stop locations in TransXChange (TXC) format for all operators, including regional and private operators and routes not currently available in realtime feeds. TransXChange is an implementation of the Transmodel open standard for public transport information. Further information is available at [www.transxchange.org.uk](http://www.transxchange.org.uk) Returns ZIP file containing XML files. **Please note:** due to the large file size, the API explorer will not work for this resource, ie. 'EXPLORE API' function. To use this dataset please download the zip file using the 'DOWNLOAD' button below or use cURL to get directly.

Transport Park&Ride provides up to 18 hours free parking each day. Customers are eligible to free parking by completing a public transport journey by tapping on and off using an accepted Opal card, then use that Opal card when exiting the car park. This dataset includes the co-ordinates of the main car park entries using the main driving entry for each Park&Ride car parks. Selected Park&Ride car parks are available in our realtime [car park API](https://opendata.transport.nsw.gov.au/dataset/car-park-api).

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in France, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: ·        Python Package: https://github.com/datasciencecampus/transport-network-performance ·        Docker Image: https://github.com/datasciencecampus/transport-performance-docker  Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: ·        Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): ·        Does not include effects due to delays (such as congestion and diversions). ·        Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.  Transport Network Routing: ·        “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details.   How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: ·        Python Package: https://github.com/datasciencecampus/transport-network-performance ·        Docker Image: https://github.com/datasciencecampus/transport-performance-docker  Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: ·        Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): ·        Does not include effects due to delays (such as congestion and diversions). ·        Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.  Transport Network Routing: ·        “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details.   How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Create your own personal public transport trip planner. APIs interact with the transportnsw.info trip planner and provide the ability for NSW public transport trip planning, departure board, travel alerts, real-time transport services and walk and drive legs. Trip Planner API to interact with the Transport for NSW trip planner. This allows users to search for trips, stops, service alerts, places of interest. The Trip Planner API offers five different endpoints: Stop Finder, Trip Planner, Departure, Service Alerts and Coordinate Request APIs. You can find more information about each of these below. Before you start using the API, we encourage you to read all available documentation at [https://opendata.transport.nsw.gov.au/documentation#9](https://opendata.transport.nsw.gov.au/documentation#9) For tips and tricks please visit our Troubleshooting page at [https://opendata.transport.nsw.gov.au/troubleshooting](https://opendata.transport.nsw.gov.au/troubleshooting)

A shapefile of gridded squares for UK urban centre 200 metre grid with following fields:id: Identifier unique to this urban centre population: Global Human Settlement Layer population estimate within 200 metre cellgeometry: Grid polygons within_uc: Whether the 200 metre cell is within the defined urban centreaccess_pop: The median sum population that can be reached from the centroid of the 200 metre cell within a 45 minute commute by walking, bus and trainproxim_pop: The sum proximal population within an 11.25 kilometre radius of the centroid of the 200 metre celltrans_perf: The transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nm: Name of the urban centrecountry_nm: Name of the country that the urban centre belongs to

The vast supply of geothermal energy stored throughout the Earth and the exceedingly long time required to dissipate that energy makes the world's geothermal energy supply nearly limitless. As such, this resource holds the potential to provide a large supply of the world's energy demands; however, like all natural resources, it must be utilized in an appropriate manner if it is to be sustainable. Understanding sustainable use of geothermal resources requires thorough characterization efforts aimed at better understanding subsurface properties. The goal of this work is to understand which critical subsurface properties exert the most influence on sustainable geothermal production as a means to provide targeted future resource characterization strategies. Borehole temperature and reservoir pressure data were analyzed to estimate reservoir thermal and hydraulic properties at an active geothermal site. These reservoir properties then served as inputs for an analytical model which simulated net power production over a 30-year period. The analytical model was used to conduct a sensitivity analysis to determine which parameters were most critical in constraining the sustainability of a geothermal reservoir. Modeling results reveal that the number of preferential flow pathways (i.e. fractures) used for heat transport provides the greatest impact on geothermal reservoir sustainability. These results suggest that early and pre-production geothermal reservoir exploration would achieve the greatest benefit from characterization strategies which seek to delineate the number of active flow pathways present in the system.

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 crowding on buses and suburban trains 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](https://www.infrastructureaustralia.gov.au/urban-crowding-congestion-maps-sydney-network) 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. **Crowding on buses** \* Crowding on buses during 2016 AM peak \* Crowding on buses (projected) during 2031 AM peak \* Crowding on buses during 2016 PM peak \* Crowding on buses (projected) during 2031 PM peak **Crowding on suburban trains** \* Crowding on suburban trains during 2016 AM peak \* Crowding on suburban trains (projected) during 2031 AM peak \* Crowding on suburban trains during 2016 PM peak \* Crowding on suburban trains (projected) during 2031 PM peak

Licensed vehicles at the end of the quarter by postcode district 1 and body type, 2020

The 2022 Climate and Energy Benchmark on the Transport Sector measures the world’s 90 most influential companies in the transport sector on their alignment with the Paris Agreement goal of limiting global warming to 1.5° Celsius and their contributions to a just transition. The Transport Benchmark is the first comprehensive assessment of companies across air, rail and road, as well as sea freight (shipping) using the ACT Assessment along with just transition and social scoring.