Abstract: Constrained by road infrastructure and the growing number of non-motorized vehicles, the coexistence of motorized and non-motorized vehicles has become a typical feature of urban roads in China. The modeling of this mixed traffic flow is highly complex due to the shared use of lanes. To address the shortcomings in existing research regarding the analysis of dynamic changes in lateral interference, the influence of various traffic factors on lateral interference in mixed traffic was investigated, which was based on the accelerated failure time (AFT) model in survival analysis method. Secondly, the IKKW model was introduced to optimize the NaSch model. Subsequently, the survival analysis method was integrated with the improved NaSch model to construct a mixed traffic flow model for motorized vehicles and non-motorized vehicles that quantified lateral interference. Finally, the characteristics and spatial distribution of mixed traffic flow were analyzed through simulation and actual data analysis. The research results indicate that mean the critical lateral distance between motorized and non-motorized vehicles follows a Weibull distribution. Motor vehicle speed, non-motorized vehicle load status, and traffic volume significantly affect the lateral interference probability, which increases with the increase of motor vehicle speed, non-motorized vehicle speed and speed difference, but decreases with the increase of non-motor vehicle load status and traffic volume of motorized and non-motorized vehicles. When the density of non-motorized vehicles is low, motorized vehicles are mainly subject to lateral interference, leading to a decrease in the critical density of traffic flow. When the density of non-motorized vehicles is high, motorized vehicles are mainly subject to longitudinal interference, leading to an increase in the critical density of traffic flow. The free-flow speed of motorized vehicles is synchronized with that of non-motorized vehicles, and the number of lateral interference events is significantly higher under low-density conditions than under high-density conditions. The actual data shows that mean the absolute percentage errors (EMAP) of non-motorized vehicle flow and lateral interference frequency are 16.98% and 8.72% respectively, indicating that simulation can effectively simulate complex mixed traffic environments.

Key words: traffic engineering; mixed traffic of motorized vehicle and non-motorized vehicle; traffic flow theory; survival analysis method; NaSch model

摘要： 受限于道路基础设施和非机动车数量增长，机非混行已成为我国城市道路的典型特征，其混行交通流建模因共用车道而十分复杂。为克服现有研究对横向干扰动态变化分析的不足，基于生存分析法中的加速失效时间模型，探究不同交通因素对混行交通横向干扰的影响；引入IKKW模型优化NaSch模型，进而融合生存分析法与改进NaSch模型，构建量化横向干扰的机非混行交通流模型；通过仿真和实际数据分析混行交通流特性与空间分布。研究结果表明：机非临界横向距离服从威布尔分布，机动车速度、非机动车载物状态及交通流量显著影响横向干扰概率，该概率随机动车速度、非机动车速度及速度差增大而上升，随非机动车载物状态、机动车和非机动车流量增加而下降；当非机动车密度较低时，机动车主要受横向干扰，交通流临界密度降低；当非机动车密度较高时，机动车主要受纵向干扰，交通流临界密度上升，机动车自由流速度与非机动车同步，且在低密度状态下横向干扰次数显著高于高密度状态；实际数据显示，非机动车流量和横向干扰次数的绝对百分比误差分别为16.98%和8.72%，仿真模拟可有效模拟复杂混行环境。

关键词: 交通工程； 机非混行交通； 交通流理论； 生存分析法； NaSch模型