Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones

Abstract

Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.

Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods

“…As in many other areas, the popularity of these models in estimating fuel consumption has greatly increased in recent years. Some prominent modeling approaches include Random Forests [41,42] and Neural Networks [3,33] have been exploited the most, while some of the most sophisticated approaches include LSTM networks and other Deep Learning architectures [42]. In most of the referred cases, linear models are utilized as a baseline model for evaluating more complex ones.…”

“…Moreover, there are several other factors that are less commonly reported and have a minor but important impact on fuel consumption. Initially, research has shown that monitoring fuel consumption through a system installed in the vehicle or a smart application pushes drivers to generally have a more ecological behavior [41,47].…”

Fuel Consumption Prediction Model using Machine Learning

“…In the last decade, scholars talked about the importance of predicting the consumed fuel percentage depending on some of the sophisticated algorithms from both Data Mining (DM) and Machine Learning (ML). However, in an earlier time, scholars had discussed the prediction of fuel consumption with different algorithms, including Neural Networks (NN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) [9,10].…”

Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks

“…Yao et al. in [ 9 ] used smartphones to collect vehicle mobility data based on their global positioning system (GPS) combined with data from on-board diagnostics (OBD) terminals to predict fuel consumption based on taxi-drivers’ driving styles. They compared ANN, SVR, and RF and showed that all of them reach satisfactory prediction performances.…”

“…The goals of these works were to reduce costs and to obtain better routing of the fleets even though they found it difficult to determine an accurate estimation of the fuel level. Yao et al in [9] used smartphones to collect vehicle mobility data based on their global positioning system (GPS) combined with data from on-board diagnostics (OBD) terminals to predict fuel consumption based on taxi-drivers' driving styles. They compared ANN, SVR, and RF and showed that all of them reach satisfactory prediction performances.…”