Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using KNN and Neural Network For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used kmeans clustering method to cluster condition of traffic flow on road segments.xc2xa0 Then, we applied the kNearest Neighbour (kNN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of kmeans and kNN method produced better results than using both, correlation method and using the kmeans method only.

Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on SpatioTemporal Correlation By analyzing and predicting the traffic states of urban road network, the formation of traffic congestion can be effectively alleviated, so as to improve the traffic capacity of urban road network. In this paper, firstly, we analyze and study the spatiotemporal correlation characteristics of traffic states based on the existing floating car data. At the same time, we extend the traffic conditions of urban road network from the upstream and downstream interaction to the global road network and complete the traffic congestion states discrimination of urban road network based on the spatiotemporal correlation. Secondly, according to the traffic jam aggregation and diffusion characteristics of local Moranxe2x80x99s I, a mixed forest prediction method considering the spatiotemporal correlation characteristics of urban road traffic state is constructed by improving the existing random forest algorithm. Finally, an example is given to verify the effect of the prediction method on the shortterm prediction of urban road network traffic states.