Reducing Vehicle Emissions via Machine Learning for Traffic Signal Program Selection (Extended Abstract)
Abstract
Real-time optimization of traffic flow addresses important practical
problems: reducing a driver's wasted time, improving city-wide
efficiency, reducing gas emissions, and improving air quality. Many
current implementations and research studies that address traffic
signal control construct a light controller's program (whether
adaptive or static) by segmenting the day into divisions in which
distinct traffic patterns are expected: rush hours,
weekends, nights, etc.
We consider the problem of automatically adapting a set of traffic
lights to changing conditions based upon the distribution of observed
traffic-density in surrounding areas. Unlike previous techniques
which specify an a priori set number of unique flow patterns,
we assume an over-complete set of traffic patterns. A combination of
machine learning approaches are used to create a diverse set of
traffic-light programs that can be instantiated when new traffic flow
patterns are recognized. We have observed significant reduction in
expected emissions and delays, while being agnostic to the number of
underlying distinct patterns in the traffic.
Research Areas
