Through years of providing big data and location services to the world’s automotive leaders, Intellias has gained first-hand insights into the dynamic challenges these companies face when trying to benefit from location intelligence.
Location data providers offer access to geodata at a scale that may appear intimidating, yet the possibilities it opens can no longer be ignored. Companies realize the ability to harness location big data fuels smart decision-making. Aiming to drive greater profitability and increase market share, companies look for customized and progressive approaches to automate spatial data processing in order to improve planning of routing, resources, and fleet planning.
With prolific experience in location intelligence analytics and advanced big data tools, Intellias can address the challenge of deriving practical insights from diverse data sets. That’s why we completed an R&D project for a location data analytics platform. To this end, we mobilized engineering professionals, who excel at data processing, storage, geospatial big data analytics, and visualization.
The main idea of our R&D project was to leverage data from various sources as well as artificial intelligence (AI) and machine learning (ML) algorithms to build an end-to-end location analytics solution. The platform we built comprises multiple components for fetching, storing, indexing, visualizing, and modeling data. It allows data analysts and business decision-makers to collect and operationalize valuable insights that help remove constraints on growth and enforce business transformation.
The solution streamlines data analysts’ work with both raw and geo-indexed data obtained from location data providers and operational business resources. It allows for accessing and processing data relative to fleet events (parking, charging, trajectories) for a specific vehicle, time interval, and geofenced area in the graphical user interface (GUI). With libraries for graphical elements, the GUI framework enables analysts to visualize events as heat, cluster, or choropleth maps so analysts are in the best position to perceive location intelligence analytics data.
The location analytics platform also provides visibility into fleet performance, traffic conditions, and operational costs. Leveraging a map matching mechanism and calculation algorithms, we developed supervised machine learning models for different in- and out-of-vehicle components that correlate with a variety of business use cases.
For example, our models combine licensed geodata on road types, slopes, and road and weather conditions with fleet data such as measured tire friction, tire type, and vehicle type. The result of this implementation is a trained ML model for predicting fuel/energy consumption for a specific road type, slope, driving speed, and weather conditions. Additionally, the results of calculations are available as a geodata layer matched on a base map with a variety of visualization effects.
The platform Intellias developed unlocks the power of location big data. Our product can drive compelling results for companies from any industry that relies on big data and location services, including insurance, logistics, IoT, machine learning, retail, and urban planning. It empowers businesses to:
- develop data-driven business strategies powered by location intelligence analytics
- keep transportation on schedule by forecasting traffic congestion and incidents
- optimize fuel and energy consumption
- reduce costs by optimizing routes
With this solution, Intellias is ready to address our clients’ needs to receive value from spatial big data and streamline business transformation with location intelligence. Our team of professional big data engineers is in a strong position to implement location data analytics platforms with custom functionality tailored for specific business needs.