On the requirements on spatial accuracy and sampling rate for transport mode detection in view of a shift to passive signalling data
- Journal Article
Rights / licenseIn Copyright - Non-Commercial Use Permitted
GPS based campaigns have been hailed as an alternative to transportation surveys that promise relativelyhigh accuracy at a relatively low burden on the participants and fewer forgotten trips. However they stillnecessitate the recruitment of participants and are thus potentially biased and certainly not encompassingsignificant parts of the population. Given the high penetration of mobile phones, passive tracking bytelephone providers would alleviate those two shortcomings at the cost of reduced sampling frequency andpositional accuracy. The trade-off in quality has not yet been quantified and therefore recommendationson sensible thresholds are not yet available. In this study therefore, instead of presenting yet anothermethod for mode of transport classification, we therefore compare the performance of existing mode detectionschemes under deteriorating sampling rates and positional accuracies. As a possibility to compensate forthe deteriorating signal we also calculate features from users’ positional histories that could be beneficial iftheir behaviour is repetitive. The evaluation is not only based on pointwise accuracy, but includes qualitymeasures that pertain to trips as a whole. We find that the necessary accuracy and sampling rate forapplications will depend on whether the information of whole trajectories can be used, or whether only thecurrent information is available. The former being relevant to ex-post analyses while the latter situationappears more frequently in near-time analyses. For segmentwise classification, there is no major impact onthe quality of the classification by the tested levels of spatial accuracies as long as the sampling intervalscan be kept at or below a minute, whereas for point based classification the sampling interval should bebetween 30 seconds and a minute and increasing spatial accuracy always improves the classification. Show more
Journal / seriesTransportation Research Part C: Emerging Technologies
Pages / Article No.
SubjectTransportation mode detection; Data quality; Passive tracking
Organisational unit03521 - Axhausen, Kay W. / Axhausen, Kay W.
02655 - Netzwerk Stadt und Landschaft D-ARCH
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