标题： 一种用于链路相关起讫矩阵估计的原始对偶算法 显示英文标题

标题： A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

摘要： 交通出行矩阵（ODM）估计是交通工程中的经典问题，旨在从测量的交通流量和先验模型信息中恢复从每个起点到每个终点的流量。 除了交通流量外，本研究还利用了由新技术捕获的探测器轨迹。 它将ODM的概念扩展为链路依赖的ODM（LODM），保留关于链路上流量分布的信息，并包含ODM分配。 此外，提出了一种从交通流量和探测器轨迹出发的LODM估计的原创公式，作为优化问题，其中要最小化的函数由五个凸函数组成，每个函数模拟交通问题的一个约束或特性：与交通流量的一致性，与采样探测器轨迹的一致性，与交通守恒（基尔霍夫定律）的一致性，具有接近起点和终点的流量的相似性，交通流量的非负性。 设计了一种原始对偶算法来最小化设计的函数，因为相应的目标函数不一定可微。 一个案例研究，在模拟网络和交通上验证了该方法的可行性，并详细说明了其在估计符合真实网络约束和观测的LODM中的优势。 显示英文摘要

摘要： Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations.