DrivingStyles A Mobile Platform for Driving Styles and Fuel Consumption Characterization

Abstract

Intelligent transportation systems (ITS) rely on connected vehicle applications to address real-world problems. Research is currently being conducted to support safety, mobility and environmental applications. This project presents the Driving Styles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles and fuel consumption based on driver characterization. In particular, we have implemented an algorithm that is able to characterize the degree of aggressiveness of each driver. We have also developed a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables obtained from the vehicle’s electronic control unit (ECU). In this project, we demonstrate the impact of the driving style on fuel consumption, as well as its correlation with the greenhouse gas emissions generated by each vehicle. Overall, our platform is able to assist drivers in correcting their bad driving habits, while offering helpful tips to improve fuel economy and driving safety.

Existing System

Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicles Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this project it will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles.

Proposed System

In proposed system data mining techniques to generate a driving styles of user based on their analysis of mobility traces. It is an android application that used to find out vehicle speed, acceleration, engine revolutions per minute and vehicle geographic position. A data centre offering a web interface to collect large dataset send by different user concurrently. Geographically display the summary details. Neural network automatically analyse the user data reporting the drivers in real time. If driver speed level or fuel consumption level increasing, then device will send an alert message to the user. Then driver changes their driving speed level.

Architecture

ARCHITECTURE DIAGRAM