Recently, I worked on a smart mobility initiative where data analytics played a central role in tackling urban congestion. We harnessed real-time traffic data, GPS patterns, and IoT sensor inputs to develop predictive models using AI and machine learning. These models enabled dynamic traffic light adjustments, optimized public transit routes, and smart parking solutions. We also introduced a gamified commuter app that nudged users toward off-peak travel through rewards. By visualizing congestion trends on interactive dashboards, city planners made faster, data-driven decisions. This holistic integration of analytics significantly reduced bottlenecks and improved mobility flow.

Data analytics can provide a traffic flow timeline which identifies major traffic tie-ups and their characteristics. This timeline can further: *categorize highway inflow and outflow from intersections *identify the varying speeds at locations with some vehicles travelling at high speeds in the fast lines while creeping to 0 in ingress and egress lanes (the Grady Curve in Atlanta is a prime example) *identify locations for speed and traffic violation traps or slowdowns (placement of cones or police at certain times), possibly curtailing those cutting in line *provide data for preparing for special event traffic *identify parking logistics *aid in understanding when area permit parking might be necessary *id'ing residential needs