The continuity of acceleration changes is often overlooked by existing car-following models, leading to a limitation in capturing realistic driving dynamics for emission estimation, which are essential for the application in microscopic traffic evaluations. This paper investigated and modeled the jerk-constrained acceleration stochastic process using the Markov model. A new car-following model considering the acceleration stochastic process was proposed, which incorporated two modes of unconscious following and active acceleration approaching. Additionally, a bi-objective model calibration framework was introduced to balance the trade-off between traffic-related performance and emission estimation performance. Numerical simulation was conducted to compare the performance of the new model with the conventional Wiedemann model. Results demonstrated that the proposed model provides more realistic vehicle dynamics and accurate emission estimations. Specifically, compared to the Wiedemann model, the new model reduced the root-mean-square error (RMSE) of spacing headway by 0.73 m and the RMSE of vehicle-specific power (VSP) distribution by 11.57%.

中文翻译：

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考虑加加速度约束加速度随机过程的跟车模型用于排放估算

现有的跟车模型经常忽视加速度变化的连续性，导致在捕捉实际驾驶动态以进行排放估算方面受到限制，而排放估算对于微观交通评估的应用至关重要。本文使用马尔可夫模型研究并建模了加加速度约束的加速度随机过程。提出了一种考虑加速度随机过程的新型跟车模型，该模型融合了无意识跟车和主动加速逼近两种模式。此外，还引入了双目标模型校准框架来平衡交通相关性能和排放估算性能之间的权衡。通过数值模拟来比较新模型与传统Wiedemann模型的性能。结果表明，所提出的模型提供了更真实的车辆动力学和准确的排放估算。具体来说，与Wiedemann模型相比，新模型的车头时距均方根误差（RMSE）降低了0.73 m，车辆特定功率（VSP）分布的RMSE降低了11.57%。