The problem of stop detection is at the base of many current and upcoming smartphone-based travel survey technologies and directly impacts the quality of many downstream operations. The inference of departure/arrival time, mode, and purpose of a trip, for example, rely on the stop/motion patterns represented by smartphone sensor-data. As users handle smartphones for various purposes and their preferences determine different device positions while traveling, accelerometer and gyroscope, for instance, often present ambiguities that prevent accurate stop detection.

To mitigate the impact of these ambiguities, we combine spatial time-series, i.e. GPS, with spatial context information retrieved from Open Street Map, which we represent as multi-dimension tensors. This project explores simple representations, such as dummy variables, and novel multidimensional representations, which are bench-marked through the classification performance of specialized artificial neural network (ANN), as well as other machine learning (ML) baselines. Our main contribution stems from this novel multidimensional representation of time-series fusion with spatial context, combined with the corresponding specialized ANN classifier. The results show a stop detection score improvement on the baselines between 3% and 6.5%.

停止检测的问题是许多当前和即将到来的基于智能手机的旅行调查技术的基础，并且直接影响许多下游业务的质量。例如，出发/到达时间，方式和行程目的的推断取决于智能手机传感器数据代表的停止/运动模式。由于用户出于各种目的使用智能手机，并且他们的偏爱在旅行时确定不同的设备位置，例如，加速度计和陀螺仪经常会出现含糊不清的现象，从而无法进行准确的停止检测。

为了减轻这些歧义的影响，我们将空间时间序列（即GPS）与从开放街道地图中检索到的空间上下文信息（我们表示为多维张量）相结合。该项目探索了简单的表示形式，例如虚拟变量和新颖的多维表示形式，它们通过专用人工神经网络（ANN）的分类性能以及其他机器学习（ML）基准进行了基准测试。我们的主要贡献来自具有空间上下文的时间序列融合的新颖多维表示，并结合了相应的专用ANN分类器。结果显示，基线的停止检测得分提高了3％至6.5％。