Automation of travel diary collection, an essential input for transport planning, has been a fruitful line of research for the last years; in particular, concerning the problem of automatic inference of transport modes. Taking advantage of technological advance, several solutions based on the collection of mobile devices data, such as GPS locations and variables related to movement (such as speed) and motion (e.g. measurements from accelerometer), have been investigated. The literature shows that many of them rely on explicit initial segmentation of GPS trajectories into trip legs, followed by a segment-based classification problem. In some cases, GIS-related features are included in the classification instance, but usually in terms of distance to transport networks or to specific points of interest (POIs). The aim of this MSc Thesis is to investigate a novel transport mode inference procedure based on the generation of topological features from a multimodal map matching instance. We define topological features as the topological context of each point of a GPS trajectory. Further utilization of these features as part of a sequence classification problem leads to mode prediction and to the implicit definition of the trip legs. In addition to not depending on an explicit segmentation step, the proposed routine also has less requirements in terms of the complexity of the required GIS features: there is no need to consider distance features, and the proposed map matching implementation does not require the usage of one unified multimodal network —as other multimodal map matching approaches do. The procedure was tested with a travel diary data set collected in Stockholm, containing 4246 trips from 368 different commuters. The transport modes considered were walk, subway, commuter train, bus and tram. In order to assess the impact of the topological context, different feature set compositions were investigated, including topological and conventional movement and motion features. Three different classifiers —decision tree, support vector machine and conditional random field— were evaluated as well. The results show that the proposed procedure reached high accuracy, with a performance that is similar to the one offered by current approaches; and that the most performant feature set composition was the one that included both topological and movement and motion features. The best evaluation measures were obtained with decision tree and conditional random field classifiers, but with some differences: while both of the them presented similar recall, the former yielded better precision and the latter achieved a higher segmentation quality.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-278988 |
| Date | January 2020 |
| Creators | Salerno, Bruno |
| Publisher | KTH, Geoinformatik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-ABE-MBT ; 20623 |
Page generated in 0.0028 seconds