Print Email Facebook Twitter Automated offline detection of disruptions using smart card data Title Automated offline detection of disruptions using smart card data: A case study of the metro network of Washington DC Author Jasperse, Faye (TU Delft Civil Engineering and Geosciences; TU Delft Transport and Planning) Contributor Cats, Oded (mentor) Snelder, Maaike (mentor) Huang, Yilin (mentor) Krishnakumari, Panchamy (mentor) Degree granting institution Delft University of Technology Programme Transport, Infrastructure and Logistics Date 2020-01-30 Abstract Service reliability is one of the most important performance measures to public transport users. Detecting disruptions helps to measure service reliability, which can be used by public transport operators to improve this reliability. In this thesis, a methodology is described to automatically detect disruptions offline, using smart card data. The day-to-day regularity of delays is investigated using hierarchical clustering on a training set, to distinguish between regular and irregular delays. The clustering result is used to create a probabilistic classifier. This classifier is applied to the test set to find days that do not correspond to a regular pattern: irregular days. After that, disruptions are detected within the irregular days. The outcomes of this study can be applied in multiple ways. Locations where disruptions have occurred can be found and the related passenger delay can be calculated. This can help public transport operators to prioritise which locations to focus on to reduce passenger delays. Furthermore, not only public transport networks, but also other networks can benefit from the outcomes of this study. Speed data of road networks could be used to find disruptions that are caused by accidents, instead of regular traffic jams. On top of that, this study could be used as a step towards real-time disruption detection, for both public transport and road networks. Subject Public transportOffline disruption detectionSmart card dataHierarchical clusteringProbabilistic classificationMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:251de9e9-5f83-45c8-a5b7-dc682c2102d7 Part of collection Student theses Document type master thesis Rights © 2020 Faye Jasperse Files PDF Thesis_Faye_Jasperse.pdf 13.33 MB Close viewer /islandora/object/uuid:251de9e9-5f83-45c8-a5b7-dc682c2102d7/datastream/OBJ/view