Modeling of fuel consumption in a road network

Sammanfattning: The fuel consumption accounts for a large portion of operational cost for logistics companies and hence building an accurate fuel prediction model is of the key importance. Recently machine learning methods have been widely used in this area, and data like historical GPS data, road conditions, weather conditions are involved when building such models. This study aims at investigating the possibility to replace the road condition features with an index that is constructed by aggregating the data collected and maintained by Scania. Two normalization methods are used for building fuel consumption index, some commonly used models including Support Vector Machine, Random Forest and Gradient Boosted Machines are trained and evaluated both with and without it. The experimental results show that the Random Forest model outperforms the others in most cases. By comparing the results with the previous studies, we can see that replacing road conditions by a fuel consumption index can lead to almost the same performance of machine learning models. To guarantee the reliability of this index, approximately 4000 to 5000 samples are expected for each road segment, this is however not realistic for many of them. When predicting fuel consumption for a given route, the more road segments with adequate samples it contains, the higher predictive accuracy we can expect.