Machine Learning, particularly Deep Learning, necessitates an extensive collection of data points, each enriched with a broad array of dimensions or attributes. The efficacy of systems developed using Deep Learning is highly contingent on the precision of these data points.
To cater to this requirement, we have developed a data collection tool capable of capturing detailed vehicle trajectories, classifying vehicles, identifying specific characteristics, gathering weather conditions, and analysing scenes. Our development was guided by an anticipation of the future needs of Agencies who will rely on this comprehensive data to power their Deep Learning algorithms. Our data storage capacity surpasses that of existing traffic data loggers by more than a million-fold, ensuring an unparalleled depth and breadth of data availability.
Integrating radar, weather, road condition, and travel time data creates a comprehensive dataset crucial for traffic analysis and prediction. Such rich information proves indispensable for strategic traffic management and infrastructure enhancement, facilitating well-informed decisions that pave the way for smoother and safer roads. Moreover, granular traffic data enables precise enforcement of traffic regulations and informs policy creation designed to alleviate congestion and enhance air quality.
