Problem Statement

Equitable AI-Driven Mobility Intelligence for Communities Society faces growing mobility challenges due to congestion, unpredictable travel times, limited transport access, and unequal access to traffic information. Rural and semi-urban communities suffer from unreliable transport, high costs, and poor connectivity. Existing systems often lack short-term congestion prediction and preventive support. An affordable, inclusive system is needed to provide predictive traffic intelligence for safer, equitable, and efficient mobility.

Solution

We propose an affordable AI-powered traffic prediction and mobility intelligence system designed to improve transportation accessibility, livelihoods, safety, and community connectivity in underserved rural and semi-urban areas.

In Detail

We propose an affordable AI-powered traffic prediction and mobility intelligence system for underserved rural and semi-urban communities. The system captures real-time traffic data, detects and classifies vehicles, estimates road occupancy, and assesses current congestion. It then predicts congestion 5–15 minutes in advance and recommends effective interventions such as traffic diversion, signal-timing adjustments, or alternate routes. The system follows a Detect → Assess → Predict → Recommend → Evaluate approach, transforming real-time traffic data into actionable intelligence. It also estimates the expected impact of each recommended action, enabling authorities and communities to take preventive measures before congestion becomes severe. This solution aims to reduce travel time, transportation costs, pollution, and stress while improving access to education, healthcare, employment, and safer mobility. It also supports data-driven decision-making and better collaboration among citizens, transport authorities, local governments, and other stakeholders.