English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Road roughness is a very important road condition for the infrastructure, asthe roughness affects both the safety and ride comfort of passengers. The roadsdeteriorate over time which means the road roughness must be continuouslymonitored in order to have an accurate understand of the condition of the roadinfrastructure. In this paper, we propose a machine learning pipeline for roadroughness prediction using the vertical acceleration of the car and the carspeed. We compared well-known supervised machine learning models such as linearregression, naive Bayes, k-nearest neighbor, random forest, support vectormachine, and the multi-layer perceptron neural network. The models are trainedon an optimally selected set of features computed in the temporal andstatistical domain. The results demonstrate that machine learning methods canaccurately predict road roughness, using the recordings of the costapproachable in-vehicle sensors installed in conventional passenger cars. Ourfindings demonstrate that the technology is well suited to meet future pavementcondition monitoring, by enabling continuous monitoring of a wide road network.

Road Roughness Estimation Using Machine Learning