Abstract Real-time and accurate short-term traffic flow prediction results can provide real-time and effective information for traffic information systems. Based on classic car-following models, this paper establishes differential equations according to the traffic state and proposes a car-following inertial gray model based on the information difference of the differential and gray system, in combination with the mechanical characteristics of traffic flow data and the characteristics of an inertial model. Furthermore, analytical methods are used to study the parameter estimation and model solution of the new model, and the important properties, such as the original data, inertia coefficient and simulation accuracy, are studied. The effectiveness of the model is verified in two cases. The performance of the model is better than that of six other prediction models, and the structural design of the new model is more reasonable than that of the existing gray models. Moreover, the new model is applied to short-term traffic flow prediction for three urban roads. The results show that the simulation and prediction effects of the model are better than those of other gray models. In terms of the traffic flow state, an optimal match between short-term traffic flow prediction and the new model is achieved.

中文翻译：

---

一种新的跟驰惯性灰色模型及其在预测短期交通流量中的应用

摘要 实时准确的短期交通流预测结果可以为交通信息系统提供实时有效的信息。本文在经典跟驰模型的基础上，根据交通状况建立微分方程，提出一种基于微分和灰色系统信息差异的跟驰惯性灰色模型，结合交通流数据的力学特性和惯性模型的特征。进一步利用解析方法研究了新模型的参数估计和模型求解，研究了原始数据、惯量系数和仿真精度等重要性质。在两种情况下验证了模型的有效性。该模型的性能优于其他六种预测模型，新模型的结构设计比现有的灰色模型更加合理。此外，将新模型应用于三个城市道路的短期交通流量预测。结果表明，该模型的模拟和预测效果优于其他灰色模型。在交通流状态方面，实现了短期交通流预测与新模型的最优匹配。