Characterizing the integrated traffic and parking dynamics in a coupled road-parking system is challenging due to the interactions between parking and non-parking vehicles and the presence of cruising-for-parking. In this paper, we propose a new trip-based macroscopic model of parking considering various sources of user heterogeneity that are key to modeling parking. The macroscopic or network fundamental diagram is used to describe the accumulation-based network speed at which vehicles are traveling. An efficient event-based simulation algorithm is proposed as the resolution scheme for the trip-based model. Numerical results reveal that after calibration, the proposed trip-based model yields similar results to those obtained using an accumulation-based approach and microscopic simulation. Taking the trip-based model as the simulation engine, we formulate a simulation-based robust optimization problem of equitable duration-based parking pricing. To solve this problem with simulation stochasticity, a new global optimizer termed NoisyDIRECT is proposed as an open-source solution algorithm. This algorithm automatically identifies the level of simulation stochasticity for each decision vector across the search space, based on which the variable number of simulation runs per point is determined. Thus, the computational resource can be better allocated so that the optimal solution can be found in a more computationally efficient and reliable manner. Numerical results demonstrate that NoisyDIRECT provides better solutions than those yielded by fixed-number sample-path optimization and non-robust optimization methods.

由于停车和非停车车辆之间的相互作用以及巡航停车的存在，表征耦合道路停车系统中的综合交通和停车动态具有挑战性。在本文中，我们提出了一种新的基于行程的停车宏观模型，考虑了对停车建模至关重要的各种用户异质性来源。宏观或网络基本图用于描述车辆行驶的基于累积的网络速度。提出了一种有效的基于事件的仿真算法作为基于行程的模型的解决方案。数值结果表明，在校准后，所提出的基于行程的模型产生的结果与使用基于累积的方法和微观模拟获得的结果相似。以出行为基础的模型作为仿真引擎，我们制定了一个基于仿真的公平基于持续时间的停车定价稳健优化问题。为了解决这个具有模拟随机性的问题，提出了一种名为 NoisyDIRECT 的新全局优化器作为开源解决算法。该算法自动识别搜索空间中每个决策向量的模拟随机性水平，并根据该水平确定每个点的可变模拟运行次数。因此，可以更好地分配计算资源，从而可以更高效和可靠的方式找到最优解。数值结果表明，NoisyDIRECT 提供了比固定数量样本路径优化和非鲁棒优化方法更好的解决方案。