Simulation and Evaluation of Double Lane Traffic Running Rules with Uncertainty of Driver Parameters

摘要

Double lane freeways often employ the keep-right-expect-to-pass rule or the fast and slow lanes classification rule. This paper established a mathematical model of double lane freeways under two kinds of rules in simulation technologies according to improved cellular automata theory and analyzed the performance of two kinds of rules through reasonable assumptions and simplifications by taking into account the efficiency, safety, and applicability. Since the model was developed in simulation technologies, visualization and parameter adjustment functions can be fully realized. Besides, the model simulated the real situation approximately by adding some human factors like drivers' mentality by introducing randomization deceleration probability and drivers' reaction time. In addition, this research analyzed the impact of intelligent transportation systems on the performance through assigning a zero value and used randomization deceleration probability to describe the uncertainty of driver-related parameters. Finally, this research found that within the impact of ITS, freeways employing the fast and slow lanes classification rule have higher flow and safety.