交通仿真对城市公共交通基础设施干预措施的规划至关重要，其依赖于按车辆类型划分的起讫点（OD）数据。现有数据源均存在局限：稀疏分布的收费站传感器可提供准确的分车型车辆计数，而基于蜂窝网络活动的大规模移动性数据虽能捕捉人群聚合流动，却缺乏出行方式的细分，且存在系统性偏差。本研究构建了一个机器学习框架，利用稀疏的收费站计数作为真实值，对蜂窝网络数据进行校正与细分。该模型通过时间与空间特征，学习聚合移动性数据与车辆数据之间的复杂关系；并基于公交线路推断目的地，结合路径分配逻辑，将校正后的流量在OD对之间进行分配。该方法应用于挪威特隆赫姆市一处公交场站扩建项目，生成了按车辆长度类别划分的逐小时OD矩阵。结果表明，有限但精确的传感器测量值可用于校正大规模但聚合的移动性数据，从而产出具有实证基础的背景车流估计。此类宏观尺度估计可在目标位置进一步细化为微观尺度分析。该框架提供了一种可推广的方法，用以从蜂窝网络数据生成起讫点数据，从而支持下游任务——例如在数据匮乏情境下开展详尽的交通仿真，辅助城市规划者做出科学决策。

Traffic simulations, essential for planning urban transit infrastructure interventions, require vehicle-category-specific origin-destination (OD) data. Existing data sources are imperfect: sparse tollbooth sensors provide accurate vehicle counts by category, while extensive mobility data from cellular network activity captures aggregated crowd movement, but lack modal disaggregation and have systematic biases. This study develops a machine learning framework to correct and disaggregate cellular network data using sparse tollbooth counts as ground truth. The model uses temporal and spatial features to learn the complex relationship between aggregated mobility data and vehicular data. The framework infers destinations from transit routes and implements routing logic to distribute corrected flows between OD pairs. This approach is applied to a bus depot expansion in Trondheim, Norway, generating hourly OD matrices by vehicle length category. The results show how limited but accurate sensor measurements can correct extensive but aggregated mobility data to produce grounded estimates of background vehicular traffic flows. These macro-scale estimates can be refined for micro-scale analysis at desired locations. The framework provides a generalisable approach for generating origin-destination data from cellular network data. This enables downstream tasks, like detailed traffic simulations for infrastructure planning in data-scarce contexts, supporting urban planners in making informed decisions.