Global
Road Infrastructure Manager: Travel Time ServiceOverview
Development of a data-driven ML model for real-time travel time prediction, based on traffic, flow, speed and density data
Background
Outdated data-driven travel model, lacking transportation logic and sensor network, to be adapted to traffic dynamics.
Challenge
Training a data-driven ML model based on transportation logic, improving the ability to accurately represent traffic.
Solution
Approach:
Advanced Machine Learning models with transportation principles lead to develop a more accurate and reliable traffic forecast. Targeted analysis of the most suitable AI architectures and transportation study to correlate flow, speed and density data defined the resulting model. The outcome, based on data-driven techniques, was trained to interpret traffic dynamics and predict the propagation of flows along the network, improving time accuracy and operational efficiency.
Project Details
Team Involved:
Project Manager (PM) | Subject Matter Expert (SME) – Transportation | Subject Matter Expert (SME) – Machine Learning | Subject Matter Expert (SME) – Google Cloud Technologies | Data Scientist (2x) | Consultant
Insights and Thoughts
Best Practices:
"The integration of IoT and analytics proved essential for optimizing production processes."
Data-driven decisions increased by
+30%
ROI achieved within the first year.
150%
Carbon footprint reduced by
+25%
Waste production decreased by
-22%
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