论文标题
通过蜂窝自动机模型对颗粒物排放的颗粒物排放，并具有缓慢启动的效果
Study on particulate matter emissions from traffic by cellular automaton model with slow-to-start effect
论文作者
论文摘要
基于经验颗粒发射模型，我们研究了颗粒物（PM）在定期条件和开放边界条件下具有缓慢启动规则的某些典型蜂窝自动机VDR模型和TT模型的发射。通过模拟，发现缓慢启动的规则模型的发射在周期性边界条件下达到亚稳态下的最大发射。在开放的边界条件下，获得了反映交通拥堵的相图。注射概率和去除概率对PM排放有很大影响。此外，在两个不同的边界条件下研究了运动状态对VDR模型和TT模型排放的影响。数值模拟表明，在周期性边界条件下，减速流量的PM发射达到拥塞状态的最大值。在具有相同出发概率的开放边界条件下，颗粒发射的变化趋势随不同车辆运动的状态而变化。对于不同的去除概率，即使车辆处于相同的运动状态，也会发出不同的最大颗粒物浓度。
Based on the empirical particulate emission model, we studied Particulate Matter (PM) emission of some typical cellular automata VDR model and TT model with slow-to-start rules under periodic condition and open boundary condition. By simulations, it is found that the emission of the slow-to-start rule model reaches the maximum emission at metastable state under periodic boundary condition. Under open boundary condition, the phase diagram to reflect traffic congestion is obtained. The injection probability and removal probability have a great impact on PM emissions. Moreover, the effects of motion status on emissions in the VDR model and TT model are studied under two different boundary conditions. Numerical simulation shows that the PM emission of decelerating traffic flow reaches the maximum in the congestion state under periodic boundary condition. Under the open boundary conditions with the same departure probability, the variation trend of particulate emission varies with the state of different vehicle movements. For different removal probabilities, the different maximum concentration of particulate matter is emitted even if the vehicle is in the same motion state.
