In a typical North American city, car commuters spend about 45 minutes on the road each day. Roughly 15 minutes of that is spent idling – at stop lights or while waiting for someone (e.g. picking up children at school) and waiting at drive thrus. All idlng (except in electric or hybrid cars) results in hazardous emissions which contribute to poorer air quality which has significant health impacts.
Modern roundabouts drastically reduce if not eliminate the idling time at intersections which averages about 30 seconds per vehicle, counting both red and green lights, as examined in Traffic signals should get the red light or Roundabouts, Cleaner Air and Safer Intersections.
Cities can enact and strictly enforce idling control bylaws to reduce idling other than at intersections or in congested traffic and discourage drive-thrus as noted in We’re Thru.
That leaves idling at traffic lights as the major remaining challenge which can be addressed by Intelligent Traffic Light Control.
Key Quotes:
“Traffic in a city is very much affected by traffic light controllers. When waiting for a traffic light, the driver looses time and the car uses fuel”
“Intelligent traffic light control does not only mean that traffic lights are set in order to minimize waiting times of road users, but also that road users receive information about how to drive through a city in order to minimize their waiting times”
“led to a novel system in which traffic light controllers and the behaviour of car drivers are optimized using machine-learning methods.”
“the traffic light has to decide which option (ie, which lanes are to be put on green) is optimal to minimize the long-term average waiting time until all cars have arrived at their destination address.”
“To estimate the waiting times, we use ‘reinforcement learning’ which keeps track of the waiting times of individual cars and uses a smart way to compute the long term average waiting times using dynamic programming algorithms”
“results indicate that the learning controllers can reduce average waiting times with at least 10% in semi-busy traffic situations, and even much more when high congestion of the traffic occurs.”
Primary Reference:
Intelligent Traffic Light Control (31 page pdf)
More recently the following Patent application title (Nov 2006): RFID Intelligent Traffic Signaling was filed with some promising features:
Key Quotes:
“There is a recognized need for more efficient control of traffic at intersections. Efficient traffic control is becoming an urgent necessity that affects traveler stress, vehicle energy consumption, and vehicle pollution due to unnecessary vehicle idling and travel time. These consequences, when cumulated by the gross number of cars, contribute to nationally significant financial costs, environmental pollution and energy consumption figures.”
“A traffic control method and system intelligently switches a traffic signal utilizing an unused time slice. This is achieved by using a processor and an RFID reader to interrogate an RFID vehicle tag of vehicles stopped at a traffic signal controlled roadway intersection, wherein the processor calculates the signaling time more efficiently.”
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Filed under: Health Impacts, Standards/Regulations, Urban Traffic | Tagged: congestion, emissions, idling, infrastructure, urban | 7 Comments »
