标题： 出行起点终点交通需求估计：一种可全球扩展的方法及其在五个大都会高速公路网络中的应用 显示英文标题

标题： Origin-Destination Travel Demand Estimation: An Approach That Scales Worldwide, and Its Application to Five Metropolitan Highway Networks

摘要： 估计出行起点-终点（OD）交通需求对于有效的城市规划和交通管理至关重要。 开发普遍适用的OD估计方法面临重大挑战，这是由于高质量交通数据的普遍匮乏以及获取城市特定的先验OD估计（或种子OD）的困难，而这些通常是传统方法的前提条件。 我们提出的方法通过系统地利用Google地图交通趋势中的聚合匿名统计数据，直接估计OD交通需求，从而无需传统的普查或城市提供的OD数据。 通过构建一个单层、一维、连续非线性优化问题，并带有非线性等式和边界约束，以复制高速公路路径通行时间，从而估计OD需求。 该方法通过采用可微分的解析宏观网络模型实现效率和可扩展性。 该模型设计上计算轻量，其参数化简洁，需要最少的校准工作量，并且能够即时评估。 这些特性确保了该方法在全球多样城市中的广泛适用性和实际应用价值。 使用洛杉矶和圣地亚哥高速公路网络的路段传感器计数，我们验证了所提出的方法，结果表明在拟合路段计数数据方面比基线方法提高了三分之二到四分之三。 除了验证外，我们还建立了该方法在复制不同高速公路网络路径通行时间方面的可扩展性和稳健性能，包括西雅图、奥兰多、丹佛、费城和波士顿。 在这些扩展评估中，我们的方法不仅与基于仿真的基准一致，而且在下午高峰时段，其拟合通行时间数据的能力平均提高了13%。 显示英文摘要

摘要： Estimating Origin-Destination (OD) travel demand is vital for effective urban planning and traffic management. Developing universally applicable OD estimation methodologies is significantly challenged by the pervasive scarcity of high-fidelity traffic data and the difficulty in obtaining city-specific prior OD estimates (or seed ODs), which are often prerequisite for traditional approaches. Our proposed method directly estimates OD travel demand by systematically leveraging aggregated, anonymized statistics from Google Maps Traffic Trends, obviating the need for conventional census or city-provided OD data. The OD demand is estimated by formulating a single-level, one-dimensional, continuous nonlinear optimization problem with nonlinear equality and bound constraints to replicate highway path travel times. The method achieves efficiency and scalability by employing a differentiable analytical macroscopic network model. This model by design is computationally lightweight, distinguished by its parsimonious parameterization that requires minimal calibration effort and its capacity for instantaneous evaluation. These attributes ensure the method's broad applicability and practical utility across diverse cities globally. Using segment sensor counts from Los Angeles and San Diego highway networks, we validate our proposed approach, demonstrating a two-thirds to three-quarters improvement in the fit to segment count data over a baseline. Beyond validation, we establish the method's scalability and robust performance in replicating path travel times across diverse highway networks, including Seattle, Orlando, Denver, Philadelphia, and Boston. In these expanded evaluations, our method not only aligns with simulation-based benchmarks but also achieves an average 13% improvement in it's ability to fit travel time data compared to the baseline during afternoon peak hours.