Learning the Road Conditions

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dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta fi
dc.contributor University of Helsinki, Faculty of Science en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten sv
dc.contributor.author Martikainen, Jussi-Pekka
dc.date.issued 2019
dc.identifier.uri URN:NBN:fi:hulib-201908133192
dc.identifier.uri http://hdl.handle.net/10138/304704
dc.description.abstract 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. en
dc.language.iso eng
dc.publisher Helsingin yliopisto fi
dc.publisher University of Helsinki en
dc.publisher Helsingfors universitet sv
dc.title Learning the Road Conditions en
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.type.ontasot pro gradu-avhandlingar sv
dc.subject.discipline none und
dct.identifier.urn URN:NBN:fi:hulib-201908133192
dc.subject.specialization ei opintosuuntaa fi
dc.subject.specialization no specialization en
dc.subject.specialization ingen studieinriktning sv
dc.subject.degreeprogram Datatieteen maisteriohjelma fi
dc.subject.degreeprogram Master's Programme in Data Science en
dc.subject.degreeprogram Magisterprogrammet i data science sv

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