76: Reducing Consecutive Red Lights: A Distribution-Based Approach to Signal Timing

Thomas M. Menino Convention & Exhibition Center 

Americans spend approximately one-third of a year of their lifetime waiting at red lights. Current traffic light systems in many suburban areas operate on timers but ignore actual vehicle arrival patterns, causing frustrating consecutive red light stops.

This study proposes a low-cost probabilistic approach to optimize traffic light timing without requiring sensors or smart technology. We model vehicle arrival times at sequential lights by treating delayed start, acceleration, and cruising speed as normally distributed variables. Using these distributions and known distances between lights, we calculate arrival time distributions at each subsequent intersection. Green light timing is then optimized to maximize the probability that vehicles stopped at the previous red light encounter green at the next light.

This approach reduces consecutive red lights, a primary source of driver frustration and road rage, while requiring only one-time adjustments to existing timer-based systems at virtually no cost. Simulation results demonstrate meaningful improvements over baseline synchronized timing.

Traffic signal optimization

Probability distributions

Applied statistics

Simulation

Transportation modeling

Applications in public policy 

Transportation Statistics Interest Group