Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

Abstract

This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation.

Research on trend prediction of component stock in fuzzy time series based on deep forest

“…This research is based on fuzzy logic to predict time series data. Information is extracted from stock price data and candlestick chart, and fuzzy time series data is obtained by fuzzy processing of time series‐related attributes [23–30], which is converted into a rule base file that can be saved for query, to carry out trend prediction. For the result prediction problem, input the index data of the sample and output the prediction result.…”

“…μ F ðxÞ is the membership degree of the element x in S to the fuzzy set F. This research is based on fuzzy logic to predict time series data. Information is extracted from stock price data and candlestick chart, and fuzzy time series data is obtained by fuzzy processing of time series-related attributes [23][24][25][26][27][28][29][30],…”