Technology
Innovation in road operation and traffic management lies at the core of the effort to create safer, greener, and faster roads for the future. We are developing a modular ecosystem of algorithmic, integration, IoT, cloud, and user experience technology that empowers control centers.
Our highly available, mission-critical, Cloud software ingests, organises and processes Big Data 24/7. Exposed through an intuitive web interface and API, we utilise AI, Machine Learning and unique algorithmic techniques to drive intelligent Traffic Management.
AI & Machine Learning
Computer Vision
Microscopic Data
Intuitive Design
Secure & Scalable
Cloud Agnostic
REST API
Event-driven Architecture
Artificial Intelligence & Machine Learning
We employ advanced AI and ML algorithms to analyze real-time vehicle data, validate events, and predict high-risk areas. Machine Vision identifies objects and road conditions, while Natural Language Processing (NLP) extracts actionable insights from social media.
Data Fusion
Over and over, we have witnessed Road Network Operators reacting to incomplete or confusing data in partial darkness. Traffer shines a light on the road through our proprietary Real-Time Fusion Engine, delivering to users the most complete picture of the current road conditions.
Information from these sources about the condition of the road exists in all shapes and forms—cameras, reports on weather conditions, social media, radar loops, IoT sensors, navigation apps, and connected vehicles. Any single source of data is inherently limited in scope, uncertain, and feature-blind. Combine them, and a pretty clear picture begins to develop about what's happening on the road. Data Fusion lets road network operators leverage this certainty. We leverage custom Knowledge Graphs and the Optimized Estimation Technique to assimilate data, measurements, information, and related uncertainties from various sources to derive real-time insights of roads and their users.
Information from these sources about the condition of the road exists in all shapes and forms—cameras, reports on weather conditions, social media, radar loops, IoT sensors, navigation apps, and connected vehicles. Any single source of data is inherently limited in scope, uncertain, and feature-blind. Combine them, and a pretty clear picture begins to develop about what's happening on the road. Data Fusion lets road network operators leverage this certainty. We leverage custom Knowledge Graphs and the Optimized Estimation Technique to assimilate data, measurements, information, and related uncertainties from various sources to derive real-time insights of roads and their users.
Big Data
Enabling new, hitherto unreachable strategies, decisions, and optimizations for road operators from the wealth of data that streams in from roads. Organizing and making sense out of big data to empower action, both in real time and for retrospective analysis.
With our scalable, event-driven architecture, data ingestion, processing, and storage are carried out in a performant, cost-effective manner with maximum.
Our system streams unlimited cameras, consuming open data and sensor signals to fuel algorithms for accident detection, verification, and automated response—all at 30 frames per second—straight to the Cloud. Furthermore, we will back up all 2.5 billion frames every day, and these can be replayed from any date in the past. We are constantly running pattern detection algorithms on the data, so as to send up alerts for present traffic anomalies to the operators. Our goal is to strip high-volume, high-velocity data of the overhead in order for value to be realized.
With our scalable, event-driven architecture, data ingestion, processing, and storage are carried out in a performant, cost-effective manner with maximum.
Our system streams unlimited cameras, consuming open data and sensor signals to fuel algorithms for accident detection, verification, and automated response—all at 30 frames per second—straight to the Cloud. Furthermore, we will back up all 2.5 billion frames every day, and these can be replayed from any date in the past. We are constantly running pattern detection algorithms on the data, so as to send up alerts for present traffic anomalies to the operators. Our goal is to strip high-volume, high-velocity data of the overhead in order for value to be realized.