In this tutorial, you will learn how the platform supports data defined on road networks. You will learn how to represent your data for use with the xyzt.ai platform.

Learn about the different variants of road network defined data. This includes static road data, temporally aggregated data (e.g., traffic stats), and trips defined along a road network (e.g., trip paths).

Learn how to represent road network-defined data in the platform using Geometry, Time Series, and Movement Path data sets.

Now that you understand the different variants of road data sets, further explore the platform capabilities.

In this tutorial, you will learn how to upload your own movement data (.csv files) to the platform.

At the end of this tutorial, you will have created a new data set which can be used for analysis.

Before we can start uploading data to the platform, we will need to acquire some data.

Your csv files are stored on the platform in a data set.

In this part, you will learn how to create such a data set.

Before the platform can process your csv files, you will have to tell the platform about the structure of your data.

In this part, you will learn how to configure the processing settings so that they match the accuracy of your data and your analytical requirements.

Now that everything is configured, it is time to upload the .csv files.

The data set is now ready to be used in a project.

After finishing the data upload tutorial, discover the articles and tutorials on how to use your data.

In this tutorial, you will learn how to upload your own movement path data to the platform.

At the end of this tutorial, you will have created a new data set with road segments and a second data set with trips along those road segments.

Before we can start uploading data to the platform, we will need to acquire some data.

The roads followed by the trips in the movement path data set are stored in a GeoJSON data set.

In this part, you will learn how to create such a data set.

Before the platform can process the GeoJSON files, you need to tell the platform which properties to use.

Now that everything is configured, it is time to upload the GeoJSON file with the road segments.

Now that you have created the GeoJSON data with the road segments, you can define and upload the data set that contains the trips along this road network.

After finishing the data upload tutorial, discover the articles and tutorials on how to use your data.

In this tutorial, you will learn how to upload your own time series data (shapes as .geojson and time varying data as .csv files) to the platform.

At the end of this tutorial, you will have created a new data set which can be used for analysis.

Before we can start uploading data to the platform, we will need to acquire some data.

Your files are stored on the platform in a data set.

In this part, you will learn how to create such a data set.

Before you can upload your GeoJSON files, you will have to tell the platform about what each property represents

Now that the GeoJSON properties are configured, it is time to upload the GeoJSON file itself.

Before the platform can process your CSV files, you will have to tell the platform about the structure of your data.

Now that everything is configured, it is time to upload the CSV files.

The data set is now ready to be used in a project.

After finishing the data upload tutorial, discover the articles and tutorials on how to use your data.

In this tutorial, you will learn how to upload your own point data to the platform.

At the end of this tutorial, you will have created a new data set which can be used for analysis.

Before we can start uploading data to the platform, we will need to acquire some data.

Your files are stored on the platform in a data set.

In this part, you will learn how to create such a data set.

Before you can upload your Parquet files, you will have to tell the platform which properties to use and what each property represents

Configure the finest aggregation level for your point data.

Now that the properties and the processing settings are configured, it is time to upload the Parquet file itself.

The data set is now ready to be used in a project.

After finishing the data upload tutorial, discover the articles on how to use your data.

In this tutorial, you will learn how to upload floating vehicle data from INRIX. This data comes as waypoints CSV files and trips CSV files.

At the end of this tutorial, you will have created a new data set which can be used for analysis.

Learn what INRIX trips data is, what the directory structure looks like, and what the file contents and most important properties are.

Learn how to create and configure a data set, upload the INRIX trips data using simple drag’n’drop of the waypoints and trips files.

Learn how to use the INRIX trips data in your own projects and perform visual traffic analysis.

In this tutorial, you will learn how to upload Trip Paths data from INRIX. This data comes as trajectories CSV files, trips CSV files, and SHP geometry files.

At the end of this tutorial, you will have created a new data set which can be used for analysis.

Learn what INRIX trip paths data is, what the directory structure looks like, and what the file contents and most important properties are.

Learn how to create and configure a data set, upload the INRIX Trip Paths data using simple drag’n’drop of the trajs and trips files.

Learn how to use the INRIX trip paths data in your own projects and perform visual traffic analysis.

In this tutorial, you will learn how to analyze traffic flows using floating vehicle data. For example, to monitor the effectiveness of traffic diversions during roadworks.

At the end of this tutorial, you will have learned how to use the advanced space-time query language to compare traffic following different routes.

Learn the context of the example use-case and how floating vehicle data can be an extremely powerful data source for insight generation.

Learn how to setup a new project and attach existing data to start a traffic flow analysis.

Learn how to use the basic filtering for analyzing traffic flows.

At the end of this part, you will have learned how to filter on data attributes, how to filter data temporally, and how to use basic spatial filters.

Learn how to use the advanced filtering for analyzing traffic flows.

In this part of the tutorial, we will take the concrete use-case of the road block on the A45 and traffic diversions near Lüdenscheid in Germany and answer an example traffic analyst question using the advanced filtering capabilities.

Now that you have learned how to analyze traffic flows, find additional resources to become a real traffic analysis expert.

In this tutorial, you will learn how to use the REST API with TypeScript to automate project creation, attaching data sets to projects, creating dashboards, etc.

Use OpenAPI Generator to create convenience TypeScript code to access the xyzt.ai REST API.

Use the generated TypeScript code to interact with the xyzt.ai platform.

Further automate tasks by creating a new project, attaching a data set, and creating a dashboard with the REST API and TypeScript.

Create a new project by uploading data to the platform using the API.

Now that you know how to use the API, explore the documentation to learn about data uploads, data queries, and more.

List of known issues with the generated Typescript code, and their workaround

In this tutorial, you will learn how to use the REST API with Python to automate project creation, attaching data sets to projects, creating dashboards, etc.

Use OpenAPI Generator to create convenience Python code to access the xyzt.ai REST API.

Use the generated Python code to interact with the xyzt.ai platform.

Further automate tasks by creating a new project, attaching a data set, and creating a dashboard with the REST API and Python.

Create a new project by uploading data to the platform using the API and Python.

Now that you know how to use the API, explore the documentation to learn about data uploads, data queries, and more.