Adaptive traffic light control using a wireless sensor network (Sébastien Faye)

We consider the problem of controlling traffic lights in an urban environment composed of multiple adjacent intersections by using an intelligent transportation system to reduce congestion and delays. Traditionally, each intersection is managed statically: the order and durations of the green lights are pre-determined and do not adapt dynamically to the traffic conditions. Detectors are sometimes used to count vehicles on each lane of an intersection but the data they report is generally used only to select between a few static sequences and timings setups. Here, we detail and study TAPIOCA, a distribuTed and AdaPtIve intersectiOns Control Algorithm that decides of a traffic light schedule. After a review of relevant related works, we first expose and evaluate the TAPIOCA algorithm, using the SUMO simulator and the TAPASCologne dataset. We then study the use of a hierarchical wireless sensor network deployed at intersections and the consequences
of losses and delays it induces on TAPIOCA. Last but not least, we propose a prediction mechanism that alleviates these issues and show, using co-simulation between SUMO and OMNeT++, that such interpolation mechanisms are effectively able to replace missing or outdated data.