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:为克服这一限制,我们调查传统客运车与能够收集详细流动需求数据的新兴来源之间的潜在结合。这一新的数据来源来自赫尔辛基区域运输管理局(HSL)牵头的TravelSense试点项目,该项目利用蓝牙灯和HSL移动电话应用软件追踪从原籍到目的地的匿名客运多式联运轨迹,通过将TravelSense数据与APC数据结合起来,我们通过确定出发地和目的地、进入通勤火车站的运输方式以及每个站点的登机和照明号码,能够更好地了解火车使用者的旅行结构。这些见解有助于公共运输规划决策,最终通过推广使用无缝和无害环境的交通选项,帮助促进可持续城市和社区的目标。