Client Overview
A national infrastructure investment trust overseeing major road assets across the country, aiming to enhance the financial viability and long-term planning of its asset base through more accurate traffic forecasting
Problem Statement
- Traffic forecasts lacked accuracy, leading to mispriced road asset acquisitions and elevated financial exposure.
- High capital costs due to limited investor confidence in future revenue projections.
- Suboptimal resource allocation in road network operations due to unreliable demand estimates.
- Absence of long-term visibility into traffic patterns, creating uncertainty around infrastructure investments.
Solution Provided
Decimal Point Analytics deployed a suite of advanced AI and ML solutions to address the client’s forecasting challenges:
- Built AI-powered traffic forecasting models across short (1-year), medium (3-year), and long-term (20-year) horizons.
- Leveraged machine learning and deep learning models integrating real-time and historical traffic datasets.
- Implemented graph-based modeling to analyze regional traffic flows and guide road planning.
- Developed an automated engine for dynamic adjustments to economic, environmental, and behavioral factors.
Outcome
- Delivered high forecast accuracy, reducing revenue leakage from underperforming tolls.
- Enabled optimized road asset pricing and investment decisions backed by data.
- Lowered capital expenditure as investors gained visibility and confidence in future revenue streams.
- Supported long-range planning with AI-powered insights on urbanization, infrastructure usage, and economic trends.
Key Takeaway
AI-driven traffic forecasting is a powerful enabler of cost-efficient infrastructure investment, helping organizations gain foresight, reduce risk, and drive smarter decisions.
Get in touch to discover how our AI and ML solutions can enhance your organization's efficiency, boost productivity, and reduce costs.
