Urbaniak Michał, Myrcik Jakub, Juda Martyna, Mandrysz Jan
Department of Transportation Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland.
Department of Radiological Informatics and Statistics, Medical University of Gdansk, 80-210 Gdansk, Poland.
Sensors (Basel). 2025 Jul 26;25(15):4635. doi: 10.3390/s25154635.
Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software-investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project "Accelerometer Measurements in Rail Passenger Transport Vehicles", pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral () and vertical () accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula-accounting for Earth's curvature-and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s, and extreme values approaching 1 m/s. The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app's data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors.
有轨电车行驶过程中出现的不平衡加速度对乘客舒适度和安全以及基础设施和车辆的磨损率都有重大影响。理想情况下，这些动态力应进行实时连续监测；然而，传统系统需要高精度加速度计和专用软件，而这对于由市政资助的有轨电车运营商来说往往成本过高。为此，作为“铁路客运车辆加速度计测量”研究项目的一部分，在波兰的格但斯克、托伦、比得哥什和奥尔什丁的有轨电车线路上开展了试点测量活动。使用了配备MEMS加速度计和GPS模块并运行Physics Toolbox Sensor Suite Pro应用程序的现成智能手机。尽管该研究采用的是广为人知的方法，但本文通过证明在未来，仅配备消费级MEMS加速度计的设备在高精度并非关键的应用中能够提供足够准确的数据，填补了经济实惠的实时监测方面的部分空白。本文对格但斯克有轨电车网络的一部分结果进行了分析。在两种有轨电车车型（Pesa 128NG Jazz Duo和Düwag N8C）内部的三个固定点记录了横向（）和垂直（）加速度，而纵向加速度由于强烈依赖驾驶员行为，在现阶段被有意省略。原始数据被导出为CSV文件，在R 版本4.2.2中进行处理和分析，然后使用ArcGIS图表进行空间映射。通过考虑地球曲率的半正矢公式以及笛卡尔近似法来计算车速。在约7公里的路线上，两种方法得出的结果几乎相同，验证了短距离情况下更简单方法的有效性。加速度直方图近似高斯分布，大多数值在0.05至0.15米/秒之间，极值接近1米/秒。结果表明，低成本移动设备在未来经过与认证加速度计校准后，可为乘坐舒适度评估提供足够丰富的数据，并有望对轨道和车辆进行经济高效的状态监测。未来的工作将集中在优化应用程序的数据收集管道、完善基于标准的分析算法以及对照基准传感器验证智能手机测量结果。
