Understanding the mobility patterns of commuter train passengers is crucial for developing efficient and sustainable transportation systems in urban areas. Traditional technologies, such as Automated Passenger Counters (APC) can measure the aggregated numbers of passengers entering and exiting trains, however, they do not provide detailed information nor passenger movements beyond the train itself. To overcome this limitation we investigate the potential combination of traditional APC with an emerging source capable of collecting detailed mobility demand data. This new data source derives from the pilot project TravelSense, led by the Helsinki Regional Transport Authority (HSL), which utilizes Bluetooth beacons and HSL's mobile phone ticket application to track anonymous passenger multimodal trajectories from origin to destination. By combining TravelSense data with APC we are able to better understand the structure of train users' journeys by identifying the origin and destination locations, modes of transport used to access commuter train stations, and boarding and alighting numbers at each station. These insights can assist public transport planning decisions and ultimately help to contribute to the goal of sustainable cities and communities by promoting the use of seamless and environmentally friendly transportation options.

翻译：理解通勤列车乘客的移动模式对于发展城市高效且可持续的交通系统至关重要。传统技术（如自动乘客计数器 APC）能够测量进出列车的乘客总量，但无法提供详细信息或列车之外的乘客移动轨迹。为克服这一局限，我们探索将传统 APC 与一种能够收集详细出行需求数据的新兴数据源相结合的潜在方法。该新数据源源自赫尔辛基区域交通管理局（HSL）主导的 TravelSense 试点项目，该项目利用蓝牙信标和 HSL 手机购票应用程序，追踪从出发地到目的地的匿名乘客多模式出行轨迹。通过将 TravelSense 数据与 APC 相结合，我们能够识别乘客出行的出发地与目的地位置、前往通勤列车站所使用的交通模式以及各站的上车与下车人数，从而更深入地理解列车乘客出行结构。这些洞察可为公共交通规划决策提供支持，并通过推广无缝衔接且环境友好的出行方式，最终助力实现可持续城市与社区的目标。