A reliable and accurate knowledge of the ridership in public transportation networks is crucial for public transport operators and public authorities to be aware of their network's use and optimize transport offering. Several techniques to estimate ridership exist nowadays, some of them in an automated manner. Among them, Automatic Passenger Counting (APC) systems detect passengers entering and leaving the vehicle at each station of its course. However, data resulting from these systems are often noisy or even biased, resulting in under or overestimation of onboard occupancy. In this work, we propose a denoising algorithm for APC data to improve their robustness and ease their analyzes. The proposed approach consists in a constrained integer linear optimization, taking advantage of ticketing data and historical ridership data to further constrain and guide the optimization. The performances are assessed and compared to other denoising methods on several public transportation networks in France, to manual counts available on one of these networks, and on simulated data.

翻译：公共交通网络客流量的可靠且准确认知对公交运营商及公共部门至关重要，这有助于其了解网络使用情况并优化运力配置。当前存在多种客流估算技术，其中部分可实现自动化。自动乘客计数（APC）系统能够检测车辆在沿线各站点上下车乘客数量。然而，此类系统生成的数据常存在噪声甚至偏差，导致车内载客量被低估或高估。本研究提出一种针对APC数据的去噪算法，旨在提升其鲁棒性并简化分析流程。该算法采用约束整数线性优化方法，通过整合票务数据与历史客流数据进一步约束并引导优化过程。其性能与法国多个公交网络的其他去噪方法进行了对比评估，并在其中一个网络的人工计数数据及模拟数据上进行了验证。