Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management.
This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights. Meanwhile, allowing for the impact of vehicles and roads, the system trains an artificial neural network with excellent drivers' driving data in order to predict the ideal fuel consumption of specific maneuvers. The driving pattern score can be validated with the fuel score that is obtained from the comparison between actual and ideal fuel consumption. Finally, an assessment system of eco-driving patterns for transport vehicles is established. The results show potential of this system to be used in big-data from connected vehicles in the future.
