基于新老司机道路选择行为的交通流均衡研究

针对新上路司机数量的爆发式增长带来的交通流均衡变化问题,本文在对司机进行分类和道路选择行为分析的基础上,应用决策树方法构建了基于新老司机道路选择行为的交通流均衡模型,并用实际案例研究了主要参数的敏感性。研究发现:路况通过联合系统车流量的分配情况、车速预期和司机学习过程等因素共同对司机比例产生影响;当某一条道路达到均衡时,车流量对司机比例的敏感程度受到路况差异的影响;司机在驾驶经验、对道路的熟悉程度以及学习能力等方面存在差异,但这种差异仅存在于对交通信息的感知方面。该研究有助于加深对城市交通流变化规律的认识和理解,对于发展出有效的交通管制措施有积极的理论价值和现实意义。

Research on Traffic Flow Equilibrium Based on Road Choice Behavior of New and Old Drivers

For the new driver brought by the explosive growth in the number of road traffic flow is balanced, classifying drivers and road is presented in this paper based on the analysis of the choice behavior. A decision tree method is applied to build the path choice behavior based on the new and old driver equilibrium model of traffic flow and through an actual case the sensitivity of main parameters is studied. The study finds: the road through the joint system, the speed of the distribution of traffic forecast and the driver's learning process factors such as the common affect driver ratio; when a certain road reaches equilibrium, the sensitivity of traffic flow to the proportion of drivers is affected by the difference of road conditions. Drivers differ in driving experience, road familiarity and learning ability, but this difference only exists in the perception of traffic information. This research is helpful to deepen the understanding of the law of urban traffic flow change, and has a positive theoretical value and practical significance for the development of effective traffic control measures.