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Transportation data is arguably one of the largest assets to our infrastructure, government agencies, and businesses both large and small. Commercial air travel alone accounts for over 6 billion passenger miles per day. The amount of commercial vehicle traffic on major U.S. roadways is astronomical with estimates being upwards to 6 billion truck-miles hauled per year.
Commercial waterways make up over 66,000 miles of navigable routes , and our local transit systems carry more than 1.6 billion riders per year .
Due to the sheer amount of data created by this traffic, it is crucial that we have access to a platform that can provide us with the tools necessary to manage, analyze, and visualize these datasets in meaningful ways.
Real-Time Transportation Visibility Platforms (RTTVPs) are designed to manage traffic data in real-time, whether locally or remotely sourced. RTTVPs offer the capability to create, edit, and update data rapidly without impacting the integrity of the platform's performance capabilities. They enable both users inside an organization (typically transit agencies) as well as external providers to dynamically modify data through easy-to-use user interfaces.
As the RTTVPs platform handles more traffic, the more robust its features must be. The more users that are active on a given platform, the higher the frequency of edits and updates that need to be handled in real time without degradation of platform performance. The core capabilities an RTTVPs platform should offer are outlined below:
User Management/Authorization: RTTVPs platforms should provide granular user management out of the box, allowing for roles-based access control (RBAC) to users depending on their responsibilities within their organization or agency. This includes the ability to assign user access within groups or at the individual level, set up access rights to repositories and datasets for each group or individual, define reporting levels that control which reports can be generated by users at different privilege levels, define dashboards that are only accessible to certain users/groups based on their role, generate audit trails of all edits made by users, and manage logins/logoffs.
Audit Trails: RTTVPs platforms should generate a complete audit trail of all user actions against the dataset or data repository whether it's an edit to a single field or the creation of a new object entirely. The platform should provide the capability for users with certain permission levels to request an audit trail of all records (or object states) edited by another user either within the platform or via third-party system.
Data Quality: It is important that RTTVPs platforms provide the capability for organizations to define data quality rules and metrics against specific datasets or repositories. The platform should allow for users/groups at certain permission levels to define these rules/metrics, set up automatic alerts when pre-defined thresholds are passed, and automatically run certain functions against the dataset if the threshold is reached.
With this feature included in an RTTVPs platform, organizations can easily monitor how their data fares over time against these predefined metrics. This allows for proactive identification of potential issues before they arise, as opposed to reactive actions that may be required after data issues have been identified.
Data Versioning: RTTVPs platforms should allow for the creation of multiple versions of a dataset so that organizations can maintain a historical record of changes made to a given version. This is especially important when it comes to leveraging machine learning against traffic data for predictive purposes. Organizations need to be able to test machine learning algorithms against historical versions of the dataset so they can accurately measure the algorithm's accuracy at predicting traffic conditions in real-time.
Data Profiles: RTTVPs platforms should allow organizations/agencies to define what types of datasets they want on their repositories, regardless of whether they are sourced from internal or external providers. The platform should provide the ability to set up different profiles for specific datasets based on their lineage (i.e., what data mart/database it originated in), granularity (i.e., at what level of aggregation the information is derived), time sensitivity (i.e., how "hot" the data is in terms of freshness), and presentation (i.e., whether it is presented as an object, aggregate or summary).
Metadata Repositories: The platform should allow organizations to maintain their metadata, regardless of where it is stored, in a centralized location so that all users have access to the information. This includes the ability to update the schema of a given dataset so that it can be ingested by the platform.
Dashboards/Visualization: With the increased emphasis on data-driven decision making, RTTVPs platforms must provide organizations with new ways to visualize their travel-related datasets within the platform for both internal and external audiences. Dashboards should be able to show a single view of all datasets on the platform and users should be able to customize these views/dashboards by adding/removing chart components such as gauges, dials, tables, etc.
External Data Connections: RTTVPs platforms should provide the ability for organizations to connect their own systems (i.e., third party and internal) to their repositories and visualize the datasets within the platform. This will provide organizations with a centralized location to access all of their data, regardless of where it is stored.
Geolocation: The platform should offer users the capability to geolocate their datasets so that organizations can work with these data in an accurate context. Whether this is done by importing coordinates from a file, manually entering them or drawing a geofence on the map to determine the data's location, RTTVPs platforms should offer users a way to work with their data in an accurate context.
Geospatial Data: RTTVPs platforms should provide the ability for organizations to work with data that has been joined from one or more third party datasets, or to create a new dataset based on these merged datasets. For example, RTTVPs platforms should allow users the ability to join their own travel-related data with traffic incident/event data and work with this combined dataset within the platform.
Machine Learning: RTTVPs platforms should offer an advanced machine learning capabilities that allow users to easily integrate their own datasets (both internal and third party) for specific purposes (i.e., prediction, optimization, etc.). These capabilities should allow users the ability to experiment with multiple models, configurations and algorithms in order to accurately determine which model performs best at a given task.
Real-Time Data Aggregation: RTTVPs platforms should provide the ability to aggregate datasets in real-time, which is especially important for organizations with operational/mission critical applications or when they need to ingest data from multiple agencies at once. This aggregation process should include an easy way to define parameters for this data ingestion, such as the time period, type of data (i.e., vehicle, transit, pedestrian), etc.
Scalability: RTTVPs platforms should be scalable enough to work with large datasets without impacting system performance. They must also be able to ingest data from different sources, including third-party systems and internal operational systems that have different data formats or structures.
Speed: The platform should provide users with the ability to easily work with large datasets (e.g., multi-modal, real-time) without impacting system performance and minimize their waiting time in order to maximize their productivity. As such, they should use parallelization when appropriate for better throughput and faster results.
The platform should allow organizations to work with their datasets in an accurate context by geolocating them, allowing for multiple data visualization options and providing the ability to join/ingest data from third party sources. It should also provide the ability to aggregate real-time data in order to minimize waiting times for users and maximize productivity. Furthermore, the platform should be scalable enough to work with large datasets without impacting system performance and offer an advanced machine learning capability that allows organizations to easily integrate their own data.
How is your platform performing regarding these capabilities? Is there something specific you'd like to see added or improved regarding real-time transportation visibility platforms? Let us know in the comments!