A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

AGNP:Network-wide short-term probabilistic traffic speed prediction and imputation

The data-driven Intelligent Transportation System(ITS)provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems.Hence,network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network.Abundant research works have adopted various approaches for traffic prediction and imputation.However,previous methods ignore the reliability analysis of the predicted/imputed traffic information.Thus,this study originally proposes an attentive graph neural process(AGNP)method for network-level short-term traffic speed prediction and imputation,simultaneously considering reliability.Firstly,the Gaussian process(GP)is used to model the observed traffic speed state.Such a stochastic process is further learned by the proposed AGNP method,which is utilized for inferring the congestion state on the remaining unobserved road segments.Data from a transportation network in Anhui Province,China,is used to conduct three experiments with increasing missing data ratio for model testing.Based on comparisons against other machine learning models,the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance.With the probabilistic confidence provided by the AGNP,reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.