无需仿真，用稀疏数据估计交通流量变化模式。
Simulation-Free Estimation of Traffic Flows from Sparse Count Data
- 分区域构建可行路线，加权最小二乘优化分配车流。
- 在布鲁塞尔数据上复现日周期流量，计算成本仅为基线的几分之一。
- 适合交通规划与实时监控，尤其适用于传感器稀疏场景。
本文提出一种从稀疏聚合车辆计数中估计时变交通流量模式的方法。将研究区域划分为空间单元，构建可行的区域间路径集合，并通过加权最小二乘优化求解每条路径上的车辆分配数量。加权贡献矩阵编码传感器覆盖信息，引导优化器选择可被传感器直接观测的流量配置。随后基于候选路径与区域传感器计数的时间和体量特征进行评分，推导出边级轨迹。在布鲁塞尔道路网络上使用真实与合成数据进行评估，结果表明该方法能准确复现输入数据中的日周期交通特征，且计算成本远低于基线方法。
原文摘要 · Abstract (English)
We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into spatial regions, constructs a set of feasible region-to-region routes, and solves a weighted least-squares optimization problem to determine the number of vehicles to allocate on each route. A weighted contribution matrix encodes sensor coverage, steering the optimizer toward flow configurations that are directly observable by sensors. Edge-level trajectories are then derived by scoring candidate routes against the temporal and volumetric profiles of aggregated regional sensor counts. The method is evaluated on the Brussels road network using real and synthetic traffic data. Results show that the proposed approach reproduces the daily traffic profile in the input data and outperforms the baseline methods at a fraction of the computational cost.
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