Abstract
Traffic jams are a threat to safety and can result in economic losses. Traffic jams that occur for no specific reason are called phantom traffic jams. If there are many cars on the road, all the cars try to choose a faster route. In this case, the following cars have no choice but to slow down to avoid an accident, resulting in congestion. In this paper, an artificial intelligence model based on image deep learning for vehicle lane-change detection in phantom traffic jams is proposed. Pictures were captured every 0.2 s from a real-time CCTV video of one position in Seoul. The precision of the proposed model for determining and detecting vehicle lane change was found to be 0.842. The proposed model is expected to help analyze the impact of phantom traffic jams on traffic flow.
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