Learning the Road Conditions

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http://urn.fi/URN:NBN:fi:hulib-201908133192
Julkaisun nimi: Learning the Road Conditions
Tekijä: Martikainen, Jussi-Pekka
Muu tekijä: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
Julkaisija: Helsingin yliopisto
Päiväys: 2019
Kieli: eng
URI: http://urn.fi/URN:NBN:fi:hulib-201908133192
http://hdl.handle.net/10138/304704
Opinnäytteen taso: pro gradu -tutkielmat
Koulutusohjelma: Datatieteen maisteriohjelma
Master's Programme in Data Science
Magisterprogrammet i data science
Opintosuunta: ei opintosuuntaa
no specialization
ingen studieinriktning
Oppiaine: none
Tiivistelmä: Wood is the fuel for the forest industry. Fellable wood is collected from the forests and requires transportation to the mills. The distance to the mills is quite often very long. The most used long- distance transportation means of wood in Finland is by road transportation with wood-trucks. The poor condition of the lower road network increases the transportation costs not only for the forest industry but for the whole natural resources industry. Timely information about the conditions of the lower road network is considered beneficial for the wood transportation and for the road maintenance planning to reduce the transportation related costs. Acquisition of timely information about the conditions of the lower road network is a laborious challenge to the industry specialists due to the vast size of the road network in Finland. Until the recent development in ubiquitous mobile computing collecting the road measurement data and the detection of certain road anomalies from the measurements has traditionally required expensive and specialized equipment. Crowdsensing with the capabilities of a modern smartphone is seen as inexpensive means with high potential to acquire timely information about the conditions of the lower road network. In this thesis a literature review is conducted to find out the deteriorative factors behind the conditions of the lower road network in Finland. Initial assumptions are drawn about the detectability of such factors from the inertial sensor data of a smartphone. The literature on different computational methods for detecting the road anomalies based on the obtained accelerometer and gyroscope measurement data is reviewed. As a result a summary about the usability of the reviewed computational methods for detecting the reviewed deteriorative factors is presented. And finally suggestions for further analysis for obtaining more training data for machine learning methods and for predicting the road conditions are presented.


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