GPS based campaigns have been hailed as an alternative to transportation surveys that promise relatively high accuracy at a relatively low burden on the participants and fewer forgotten trips. However they still necessitate the recruitment of participants and are thus potentially biased and certainly not encompassing significant parts of the population. Given the high penetration of mobile phones, passive tracking by telephone providers would alleviate those two shortcomings at the cost of reduced sampling frequency and positional accuracy. The trade-off in quality has not yet been quantified and therefore recommendations on sensible thresholds are not yet available. In this study therefore, instead of presenting yet another method for mode of transport classification, we therefore compare the performance of existing mode detection schemes under deteriorating sampling rates and positional accuracies. As a possibility to compensate for the deteriorating signal we also calculate features from users’ positional histories that could be beneficial if their behaviour is repetitive. The evaluation is not only based on pointwise accuracy, but includes quality measures that pertain to trips as a whole. We find that the necessary accuracy and sampling rate for applications will depend on whether the information of whole trajectories can be used, or whether only the current information is available. The former being relevant to ex-post analyses while the latter situation appears more frequently in near-time analyses. For segmentwise classification, there is no major impact on the quality of the classification by the tested levels of spatial accuracies as long as the sampling intervals can be kept at or below a minute, whereas for point based classification the sampling interval should be between 30 s and a minute and increasing spatial accuracy always improves the classification.

基于GPS的活动已被誉为交通运输调查的一种替代方案，该运输承诺以相对较低的参与者负担和较少的遗忘旅行保证较高的准确性。但是，它们仍然需要招募参与者，因此可能会产生偏见，并且肯定不会涵盖人口的很大一部分。鉴于移动电话的高度普及，电话提供商的被动跟踪将以降低采样频率和位置精度为代价来缓解这两个缺点。质量的权衡尚未量化，因此尚无关于合理阈值的建议。因此，在本研究中，除了提出另一种运输方式分类方法以外，因此，我们在采样率和位置精度不断下降的情况下比较了现有模式检测方案的性能。为了补偿不断恶化的信号，我们还根据用户的位置历史记录来计算特征，如果用户的行为是重复的，则可能会有所帮助。评估不仅基于逐点准确性，还包括与行程整体相关的质量度量。我们发现，对于应用程序而言，必要的精度和采样率将取决于是否可以使用整个轨迹的信息，或者仅当前信息可用。前者与事后分析相关，而后者的情况在近期分析中更为常见。对于分段分类，