Evaluation of performance of a smartphone application for measuring bike paths’ condition
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
There are several methods to measure surface evenness for car roads, but almost none for bike paths. Accordingly, VTI (the Swedish National Road and Transport Research Institute) have created a smartphone application which uses the accelerometers in the phone to measure the vibration from the road. This report’s aim is to analyze the data collected using this application, investigate if the data is repeatable, to find factors that are important for evenness and perform classification of bike paths as even or wiggly.
Two main methods were used, Gaussian process and wavelets. Gaussian process was used to classify bike paths and wavelets to investigate the repeatability and see how many trips are needed to get a consistent result.
The results show that the two different smartphones gave quite different results; one smartphone indicated almost twice as high RMS values (measure of vibration) than the other. The GPS positions of smartphones were quite good, except under a tunnel and close to high buildings. Some short section of the road gave very high or very low RMS values, but the general standard of all investigated bike paths were too even to detect any significant differences between the paths. The results show that there’s some unexplained variance in the turns, but the effect of the turns hasn’t been tested. The wavelets analysis show that around 15 trips were needed to get a consistent result.
The report contains a description of a designed experiment that will continue this project. This new data will be collected in a more carefully to make a better separation between good and bad cycle routes by the RMS value.
Place, publisher, year, edition, pages
2013. , 68 p.
Statistics, smartphone application, bike path, RMS, Gaussian process, Wavelets
Statistik, smartphoneapplikation, cykelvägar, RMS, Gaussian process, Wavelets
Other Social Sciences not elsewhere specified
IdentifiersURN: urn:nbn:se:liu:diva-95588ISRN: LIU-IDA/STAT-A--13/006—SEOAI: oai:DiVA.org:liu-95588DiVA: diva2:636647
Subject / course
Program in Statistics and Data Analysis
2013-06-04, Linköping, 09:15 (English)
Sysoev, OlegVillani, Mattias, Professor
Uppdragsgivare: VTI (Anna Niska och Leif Sjögren)2013-08-012013-07-102013-08-01Bibliographically approved