Abstract
Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodic features are susceptible to weather conditions, making TFP a challenging issue. TFP process are significantly influenced by several factors like accident and weather. Particularly, the inclement weather conditions may have an extreme impact on travel time and traffic flow. Since most of the existing TFP techniques do not consider the impact of weather conditions on the TF, it is needed to develop effective TFP with the consideration of extreme weather conditions. In this view, this paper designs an artificial intelligence based TFP with weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusion of weather related conditions. The proposed AITFP-WC technique includes Elman neural network (ENN) model to predict the flow of traffic in smart cities. Besides, tunicate swarm algorithm with feed forward neural networks (TSA-FFNN) model is employed for the weather and periodicity analysis. At last, a fusion of TFP and WPA processes takes place using the FFNN model to determine the final prediction output. In order to assess the enhanced predictive outcome of the AITFP-WC model, an extensive simulation analysis is carried out. The experimental values highlighted the enhanced performance of the AITFP-WC technique over the recent state of art methods.