Accurate measurement of traffic volumes and flows is vital for modern intelligent transportation. However, despite recent technological advances in sensor devices, it is still expensive to install and maintain fixed traffic counters. Therefore, it is restricted to a small portion of location points where the counters can be installed, which severely limits the possibility of grasping and predicting the total traffic volume at a city-wide level. By contrast, devices with location history such as smartphones and connected vehicles are now widely used and provide much wider spatial coverage. However, the data from these devices are usually partial and noisy, so they are not enough to directly estimate total traffic volumes and flows. In this paper, we use the information from these widely available devices to help decide where to place additional traffic counters, and we study how selecting new measurement locations can improve city-wide traffic estimation performance. To achieve this, we propose an algorithm that chooses additional counter locations to increase the diversity of observed traffic signal patterns, rather than simply spreading counters evenly over space. The goal is to capture traffic-pattern types that are rare in the current counter set and to make the collected observations more representative for later estimation and forecasting. We also present a real-world evaluation; in a target city, we select new locations expected to improve traffic prediction, and we then commissioned new field measurements at those locations at our expense. The resulting data led to an improvement in traffic volume estimation accuracy across different fidelities.

翻译：准确测量交通量和交通流对现代智能交通至关重要。尽管传感器设备技术近期取得进步，安装和维护固定交通计数器仍然成本高昂。因此，计数器仅能安装在少量位置点，这严重限制了在城市层面掌握和预测总交通量的可能性。相比之下，智能手机和联网车辆等具有位置历史记录的设备现已广泛使用，提供了更广阔的空间覆盖。然而，这些设备的数据通常不完整且含有噪声，不足以直接估计总交通量和交通流。本文利用这些广泛可用设备的信息，辅助决定新增交通计数器的部署位置，并研究如何选择新的测量位置以改善城市范围的交通估计性能。为此，我们提出一种算法，通过选择新增计数器位置来增加观测到的交通信号模式的多样性，而非简单地将计数器均匀分布。其目标是捕获当前计数器集合中稀有的交通模式类型，使收集的观测数据对后续估计和预测更具代表性。我们还进行了真实世界评估：在目标城市中，我们选取了预期能改善交通预测的新位置，并自费在这些位置开展了实地测量。所得数据在不同精度层级上均提升了交通量估计的准确性。