This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffic-theoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with $r$ links, each of space capacity $\ell$, the model has a complexity of $\mathcal{O}(r\ell)$. The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small network with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared to the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.
翻译:本文侧重于对车辆交通进行分析性概率模型的模型分析。 它开发了一个随机节点模型。 它然后通过将节点模型与Lu和Osorio(2018年)链接模型(这是交通- 理论连接传输模型的随机配方)。 拟议的网络模型可以缩放,而且计算效率很高, 适合城市网络优化。 对于一个有美元链接的网络, 每个空间能力单位为$/ ell美元, 模型的复杂度是$\mathcal{O}(r\ell) 。 网络模型产生链接状态的边际分布。 模型经过验证, 而不是模拟网络连接传输模型的网络实施。 验证实验考虑到一套交通动态复杂的小型网络。 对于所有情况, 拟议的模型都精确地捕捉了交通动态。 网络模型用于解决信号控制问题。 与卢和奥索里奥的概率链接模型( 2018年) 相比, 网络模型具有外源节点模型和基准网络装定点模型, 使用业绩网络的跟踪模型, 和计算模型 互动。