Introduction

The automotive industry is undergoing a data-driven transformation, using vast volumes of geospatial, telematics, and sensor data to power AI innovation. As vehicles, infrastructure, and mobility systems become increasingly connected, this data is helping address challenges like traffic safety, predictive maintenance, EV infrastructure planning, and sustainable logistics.

With a projected TAM growing from $53.79B in 2024 to $165.67B by 2032, the opportunity is enormous. For example, the U.S. Department of Energy notes that aggressive driving—like rapid acceleration and hard braking—can boost fuel consumption by up to 33%. Telematics-based driver coaching helps reduce these behaviors and cut fuel costs. Predictive maintenance powered by telematics has also slashed vehicle downtime by up to 80%, boosting operational efficiency.

Databricks empowers organizations to tap into these opportunities by offering scalable, real-time geospatial analysis. With built-in geospatial functions, AI integrations, and support for modern architectures, the platform enables the creation of location-aware applications that deliver real business impact.

This two-part blog covers (Part 1) key geospatial use cases and datasets in automotive and mobility, and (Part 2) how Databricks accelerates production-grade geospatial analytics and AI with code samples and best practices.

Understanding the Problem

Automotive organizations face growing complexity in managing and analyzing the flood of data from connected vehicles, infrastructure, and platforms. Siloed teams, fragmented architectures, and limited real-time capabilities often block access to predictive insights.

Databricks addresses this by offering a unified, scalable platform for processing geospatial data, training AI models, and enabling cross-functional collaboration. Among these capabilities, Geospatial Analytics emerges as a key enabler to unlock smarter, location-driven decision-making across high-impact use cases.

The rest of this blog explores how Databricks helps automotive organizations use geospatial analytics to overcome these challenges and drive measurable outcomes.

Geospatial Analytics Use Cases

Organizations that fail to harness geospatial analytics often face inefficiencies like poor route planning, unexpected vehicle downtime, and reactive decision-making. By integrating geospatial data with AI, businesses can unlock real-time insights to improve safety, efficiency, and service quality across their operations. The key use cases include:

• Road Safety and Risk Prediction: deliver AI‑powered geospatial analytics solution that fuses accident‑hotspot, weather, and traffic data with real‑time vehicle telematics to predict high‑risk road segments and proactively inform drivers of accident‑prone spots along their route, helping prevent incidents.
• Smart Mobility: apply AI‑powered geospatial analytics to optimize routes, deliver public amenities on demand, and improve logistics efficiency, thereby enhancing citizens’ quality of life.
• Driving-Based Insurance: enable insurers to accurately assess driver risk profiles using geospatial and mobility data, providing better rates for safer drivers.
• EV Infrastructure Optimization: leverage location-based insights to strategically place charging stations in high-demand areas, improving station utilization.
• Predictive Maintenance: use real-time telematics and AI-driven analytics to anticipate maintenance needs, reducing fuel consumption, downtime and operational costs.

Core Datasets for Geospatial Analytics and AI

Data is central to automotive innovation, particularly when combined with geospatial analytics and artificial intelligence. Utilizing relevant datasets with clearly defined ontologies enables automotive companies to derive actionable insights, enhance decision-making, and improve their bottom line. These are some of the recommended dataset to build geospatial analytics in automotive and mobility.

In this post, we reference publicly available data from New York City due to its accessibility and comprehensiveness. These datasets represent foundational building blocks for analyzing traffic behavior, road safety, and environmental impacts. The blog uses NYC data as a demonstration, but the approach is flexible and can be scaled with larger and organization-owned datasets.

• The Collisions dataset provides detailed records of road incidents, including dates, locations, and contributing factors such as driver behavior or road conditions, facilitating safety analysis and risk mitigation.
• The Traffic Volume dataset delivers historical traffic density and congestion information, aiding efficient route planning and traffic forecasting.
• The Road Condition dataset offers real-time and historical data on road closures, construction, and incidents, enabling proactive management of transportation safety and efficiency.
• The Weather data, including temperature, precipitation, wind speed, and visibility, supports the correlation of weather conditions with driving patterns and safety outcomes.
• The Trips dataset captures individual trip records such as pickup/dropoff times, distances, fares, ZIP codes, and trip types (e.g., Taxi, Ride Sharing), serving as a foundational dataset to analyze mobility patterns, passenger behavior, and service efficiency across transportation modes.
• The Telematics data, providing driving metrics such as speed, acceleration, braking, and route choices, allows organizations to develop analytical models on driving behavior safety and predictive maintenance.

Together, these datasets empower automotive businesses to optimize operations, enhance customer experience, and drive measurable business impact. In short, to support rich geospatial analytics, customers should consider a plethora of data points, combining proprietary datasets and public sources. Below is a sample bronze data model with the datasets discussed above:

Example of Bronze Tables Used in Road Safety Medallion Pipeline

Understanding Telematics and CAN - The Backbone of Vehicle Telemetry

Telematics data refers to vehicle information collected via onboard sensors, GPS, and integrated systems. It includes details like location, speed, acceleration, braking, fuel efficiency, and diagnostics—enabling insights into vehicle performance, driver behavior, and fleet management.

At the heart of telemetry lies the Controller Area Network (CAN)—a widely used communication protocol that connects Electronic Control Units (ECUs) within a vehicle. Originally designed to simplify in-vehicle wiring, the CAN bus enables efficient, reliable, and prioritized message-based communication using differential signals to reduce electrical noise.

Variants like CAN 2.0, CAN FD, and CAN XL offer differing speeds and payload capacities, suited for a range of automotive applications.

For data engineers, this presents a challenge: ingesting high-frequency, bursty, and prioritized telemetry streams, decoding raw CAN frames into usable insights, and supporting real-time processing for downstream analytics and applications.

Understanding Scalable Geospatial Analytics

Scalable geospatial analytics refers to the ability to process and analyze massive volumes of location-based data—often at petabyte and exabyte scales—in real time, enabling organizations to extract meaningful insights from complex spatial patterns across large geographic areas.

The Databricks’ Data Intelligence Platform combines powerful geospatial analytics and AI to deliver scalable, real-time insights. With features like Liquid Clustering and H3 spatial functions, it enables fast and efficient processing of massive geospatial datasets. Built-in geospatial functions simplify spatial tasks such as mapping traffic patterns or assessing road risk. AutoML accelerates model development for use cases like predicting aggressive driving by factoring in weather, traffic, and road conditions. The platform also ensures strong governance through Unity Catalog (UC), which manages data access and sharing securely. Tools like AI Query and UC-governed functions make it easy to extract structured geolocation data from unstructured sources, enhancing both precision and productivity.

Enable Scalable Governance & Open Collaboration

Databricks delivers powerful geospatial analytics by combining high-performance compute, flexible architecture, and built-in spatial functions. Photon-powered execution and Serverless provide fast, cost-efficient processing—ideal for large-scale spatial joins, aggregations, and point-in-polygon queries on datasets like GPS traces, traffic flows, and environmental data.

Native support for H3 indexing, spatial functions, and time-series forecasting enables teams to analyze mobility patterns, detect anomalies, and forecast trends using both real-time and historical data.

Scalable geospatial analytics requires strong governance. Unity Catalog (UC) centralizes metadata and access controls, ensuring secure, compliant management of spatial datasets, models, and dashboards. UC supports fine-grained access, data lineage, and trusted collaboration across teams.

UC also integrates with Delta Sharing, enabling secure, real-time data collaboration across OEMs, suppliers, and mobility partners—without copying data. For example, a carmaker can share road hazard data with a navigation provider to enhance routing, while maintaining strict governance. Together, UC and Delta Sharing support open yet controlled data exchange—critical for today’s data-driven mobility solutions.

Pipeline for Smart Mobility & Road Safety

We showcase below a geospatial analytics pipeline built on the Databricks Data Intelligence Platform. It integrates telematics, traffic, and weather data with capabilities like Automated Liquid Clustering, H3 indexing, AutoML, and AI functions to support real-time use cases such as road safety. It illustrates a medallion pipeline combining geospatial data, LLMs, and Genie for conversational insights.

The Importance of Synthetic Data

Synthetic telematics data—artificially generated datasets mimicking real-world scenarios—offers a safe, effective way to test and develop systems without exposing personally identifiable information (PII). It enables privacy-safe analytics, model development, and system validation while ensuring regulatory compliance.

Telematics is a prime use case for synthetic data, as it supports realistic testing without accessing sensitive vehicle data. While developers can generate such data using SQL or Python, the Databricks Labs Data Generator (dbldatagen) simplifies the process with a declarative interface for creating large, scalable datasets on Spark.

In Part 2, we’ll show how to create telematics data with dbldatagen —featuring GPS location, speed, acceleration, timestamps, seat belt usage, wiper status, and others—ideal for modeling, validation, or pipeline testing without relying on production data.

The Impact

By leveraging geospatial analytics alongside artificial intelligence and real-time data processing capabilities, Databricks enables the automotive industry to reduce maintenance costs, improve road safety by uncovering transformative insights. By utilizing Databricks’ scalable and unified architecture, organizations can effectively enhance road safety through advanced analytics, optimize electric vehicle infrastructure by accurately predicting demand and usage patterns, and improve fleet operational efficiency through real-time monitoring and predictive maintenance capabilities.

Organizations aiming to secure a competitive edge should consider exploring Databricks’ advanced geospatial analytics and artificial intelligence capabilities, which have enabled companies to achieve up to 30% improvements in fleet efficiency, reduce infrastructure costs by as much as 25%, and enhance road safety outcomes through predictive insights that decrease accident rates by up to 20%.

Putting It into Practice: Next Steps

In Part 2 of this series, we demonstrate how to bring these insights to life using Databricks - covering scalable pipelines, synthetic data, route generation, time series forecasting, and LLM-based geospatial enrichment.