Driving behavior modeling and evaluation for bus enter and leave stop process

Abstract

Driving behavior optimization not only lowers the occurrence probability of the traffic accident and pollutant emissions, but also improves the energy efficiency level. Current studies indicate that corresponding data processing method deadly influence accuracy and rationality of driving behavior estimation results. However, due to the complexity of road environment, driving behavior estimation always faces the common difficulties of model establishment. In this paper, a new concept, critical zone, is defined to distinguish special driving areas. A typical critical zone, bus stop, is selected as modeling target area, while corresponding driving process—bus enters and leaves bus stop process—is selected to construct driving behavior model. Then, a good driving behavior parameters discovery method, which aims to provide real-time operation recommendations for bus stop accessing procedure, optimize driving technology, reduce fuel consumption and improve comfort, is proposed. A zone and time oriented slicing process is employed to mapping corresponding data into multiple distinguished procedures, in which both clustering and pruning processing are used to deal with data of each slice to get good driving behavior parameters. Moreover, an application based C/S model is used to test effectiveness of proposed method. Test results demonstrate that the developed model and parameter discovery method are effective.