In order to make the car-following model describe the driving behavior of vehicle on urban road more accurately, existing car-following models are simulated using measured traffic data. According to the analysis of the simulation result, two new improved car-following models based on the optimal velocity model (OVM) are proposed in this paper. The lateral vehicle’s influence is introduced as the influence factor of driving behavior. By using of linear stability analysis, stability conditions of improved car-following models are obtained. Nonlinear analysis is carried out to investigate the traffic performances near the critical point. The result of numerical simulation indicates that stability of traffic flow is under the influence from lateral vehicle; the lesser the influence, the greater the stability. New cooperative car-following models are verified by the traffic flow data collected in Xi’an city. It is shown that compared with the optimal velocity model, the simulation result of the second cooperative model, respectively, gets 62.89% unbiased variance reduction, 66.39% maximum absolute error reduction, and 33.4% minimum absolute error reduction. Therefore, the second cooperative model is more suitable to describe the vehicle’s actual behavior in car-following state.

中文翻译：

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考虑横向车辆影响的微交通流建模

为了使跟车模型更准确地描述车辆在城市道路上的驾驶行为，使用测得的交通数据对现有的跟车模型进行了仿真。通过对仿真结果的分析，提出了两种基于最优速度模型（OVM）的改进的跟车模型。引入横向车辆的影响作为驾驶行为的影响因素。通过线性稳定性分析，获得了改进的汽车跟踪模型的稳定性条件。进行非线性分析以研究临界点附近的交通性能。数值模拟结果表明，交通流量的稳定性受侧向车辆的影响。影响越小，稳定性就越大。西安市收集的交通流量数据验证了新型的合作跟车模型。结果表明，与最优速度模型相比，第二种协作模型的仿真结果分别获得了62.89％的无偏方差减小，66.39％的最大绝对误差减小和33.4％的最小绝对误差减小。因此，第二种合作模型更适合描述汽车在跟车状态下的实际行为。