The ubiquity of personal cellular phones in society has led to a surging interest in using Big Data generated by mobile phones in transport research. Studies have suggested that the vast amount of data could be used to estimate origin–destination (OD) matrices, thereby potentially replacing traditional data sources such as travel surveys. However, constructing OD matrices from mobile phone data (MPD) entails multiple challenges, and the lack of ground truth hampers the evaluation and validation of the estimated matrices. Furthermore, national laws may prohibit the distribution of MPD for research purposes, compelling researchers to work with pre-compiled OD matrices with no insight into the methods used. In this paper, we analyse a set of such pre-compiled OD matrices from the greater Oslo area and perform validation procedures against several sources to assess the quality and robustness of the OD matrices as well as their usefulness in transportation planning applications. We find that while the OD matrices correlate well with other sources at a low resolution, the reliability decreases when a finer level of detail is chosen, particularly when comparing shorter trips between neighbouring areas. Our results suggest that coarseness of data and privacy concerns restrict the usefulness of MPD in transport research in the case where OD matrices are pre-compiled by the operator.

个人蜂窝电话在社会中的普遍使用已经引起了人们对在交通运输研究中使用手机产生的大数据的浓厚兴趣。研究表明，大量数据可用于估计起点-目的地（OD）矩阵，从而有可能替代传统数据源，例如旅行调查。但是，从移动电话数据（MPD）构造OD矩阵会带来多种挑战，并且缺乏地面真理会妨碍对估计矩阵的评估和验证。此外，国家法律可能禁止出于研究目的分发MPD，这迫使研究人员在不了解所使用方法的情况下使用预编译的OD矩阵进行工作。在本文中，我们分析了来自奥斯陆大区的一组此类预编译的OD矩阵，并针对多个来源执行了验证程序，以评估OD矩阵的质量和健壮性以及它们在运输规划应用中的实用性。我们发现，当OD矩阵以低分辨率与其他来源的相关性良好，可靠性相邻区域之间的比较短的行程特别是当当选择细节的一个更精细的水平降低，。我们的结果表明，在运营商预先对OD矩阵进行编译的情况下，数据的粗糙性和隐私问题限制了MPD在运输研究中的实用性。我们发现，当OD矩阵以低分辨率与其他来源的相关性良好，可靠性相邻区域之间的比较短的行程特别是当当选择细节的一个更精细的水平降低，。我们的结果表明，在运营商预先对OD矩阵进行编译的情况下，数据的粗糙性和隐私问题限制了MPD在运输研究中的实用性。我们发现，当OD矩阵以低分辨率与其他来源的相关性良好，可靠性相邻区域之间的比较短的行程特别是当当选择细节的一个更精细的水平降低，。我们的结果表明，在运营商预先对OD矩阵进行编译的情况下，数据的粗糙性和隐私问题限制了MPD在运输研究中的实用性。