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Learning Traffic Light Phase Schedules from Velocity Profiles in the Cloud

Author(s): Markus Kerper, Christian Wewetzer, Andreas Sasse, Martin Mauve.
Title: Learning Traffic Light Phase Schedules from Velocity Profiles in the Cloud
Published: NTMS - Mobility and Wireless Networks Track (NTMS'2012 - Mobility & Wireless Networks Track), pp. --, Istanbul, Turkey, May 2012
Abstract: Traffic lights strongly impact vehicle movement and fuel consumptionin cities. If drivers were aware of the traffic light phase schedule,they could predict the traffic light state at arrival time and couldreduce fuel consumption.To acquire information like traffic light phase schedules, our visionis that drivers share their velocity profiles in a digital cloud, andin return benefit from smart algorithms evaluating the collected data.We present one such algorithm, Traffic Light State Estimation (TLSE),that operates on the velocity profiles to backward-estimate phaseschedules of traffic light signal groups operating with fixed cyclelength (representing about 80% of all traffic lights in the US).We present simulation results showing that phase schedule predictionon the base of TLSE is correct more than 90% of the time.
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