Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions
نویسندگان
چکیده
Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which used to reduce congestion. But the avail of data with temporal features and periodic are susceptible weather conditions, making TFP a challenging issue. process significantly influenced by several factors like accident weather. Particularly, inclement conditions may have extreme impact on travel time flow. Since most existing techniques do not consider TF, it needed develop effective consideration conditions. In this view, paper designs artificial intelligence based (AITFP-WC) for smart cities. The goal AITFP-WC model enhance performance inclusion related proposed technique includes Elman neural network (ENN) predict Besides, tunicate swarm algorithm feed forward networks (TSA-FFNN) employed periodicity analysis. At last, fusion WPA processes takes place using FFNN determine final output. order assess enhanced predictive outcome model, extensive simulation analysis carried out. experimental values highlighted over recent state art methods.
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2022
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2022.022692