PREDICTION OF FUEL ANALYSIS USING URBAN BIG DATA

Abstract

Ability to model and predict the fuel consumption is vital in enhancing fuel economy of vehicles in transport management. There are several internal factors such as distance, load and vehicle characteristics, as well as external factors such as road conditions, traffic, and weather on which fuel consumption of a vehicle is dependent. However, not all these factors may be available or measured for the fuel consumption. Providing real time traffic information in metropolitan cities is desired since it not only helps to manage the traffic management but also save the time of travelers and reduces the vehicle fuel consumption. To obtain the traffic information from number of sensors on every road segments or intersections is difficult due to large number of installations. Getting the accurate information of current and near term future traffic flows of different road links in a traffic network has a wide range of applications which includes the forecasting of the traffic flow, navigation of vehicles and traffic congestion management. We considered a case where only subset of three factors is easily available which are vehicle characteristics, traffic dataset and road distance. Hence, the challenge is to model and/or predict the fuel consumption only with available data, and also taking as much as influence from other internal and external factors. Machine Learning (ML) is suitable in such analysis, as the model can be developed by learning the patterns in the available data. In this paper, we use algorithm that is used in google maps such as Gaussian Naive Bayes and Page Rank which provide the different routes and methods like image processing of maps to extract the different RGB values of different routes which helps to predict the fuel consumption of the vehicle. Finally, after predicting the fuel consumption for different paths, the best path gets generated in terms of less fuel consumptions.