Identification of Road Surface Conditions using IoT Sensors and Machine Learning

2018 ◽  
pp. 259-268 ◽  
Author(s):  
Jin Ren Ng ◽  
Jan Shao Wong ◽  
Vik Tor Goh ◽  
Wen Jiun Yap ◽  
Timothy Tzen Vun Yap ◽  
...  
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Conditions

EFFICIENCY IMPROVEMENT OF ROAD SURFACE INSPECTION USING MACHINE LEARNING TECHNOLOGY

2017 ◽  
Vol 73 (2) ◽  
pp. I_409-I_415
Author(s):  
Naoko FUKUSHI ◽  
Daishiro KOBAYASHI ◽  
Seiji IWAO ◽  
Ryosuke KASAHARA ◽  
Nobuyoshi YABUKI
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Inspection ◽  
Learning Technology ◽  
Efficiency Improvement

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

scholarly journals Penilaian Kondisi Jalan Poros Sabbang Selatan Menggunakan Metode Surface Distress Index

2021 ◽  
Vol 6 (1) ◽  
pp. 24
Author(s):  
Dewi Artika Sari ◽  
Afdal Kisman
Keyword(s):  
Surface Damage ◽  
Road Surface ◽  
Assessment System ◽  
Pavement Condition ◽  
Surface Conditions ◽  
Traffic Volumes ◽  
Surface Distress ◽  
Left Section ◽  
The Right ◽  
Initial Research

Prasarana jalan jika terbebani volume lalu lintas yang tinggi dan berulang-ulang akan menyebabkan terjadinya penurunan kualitas jalan sehingga dapat mempengaruhi keamanan, kenyamanan dan kelancaran dalam berlalu lintas. Untuk menjaga agar tidak terjadi penurunan kondisi khususnya pada jalan poros Kecamatan Sabbang Selatan Kabupaten Luwu Utara tepatnya di jalan Padang Sarre, Buntu Terpedo sampai jalan Dandang sepanjang 4 km perlu adanya penanganan. Maka perlu dilakukan penelitian awal terhadap kondisi permukaan jalan dengan melakukan survei secara visual dengan cara menganalisa kerusakan berdasarkan jenis dantingkat kerusakannya. Tujuan penelitian yaitu menilai kondisi perkerasan danpenanganan sesuai kondisi permukaan jalan. Penelitian ini menggunakan system penilaian kondisi perkerasan menurut Bina Marga dengan perhitungan Surface Distress Index (SDI) untuk jalan beraspal. Dari hasil penelitian di dapatkan penilaian untuk jenis kerusakan permukaan jalan pada ruas kanan yaitu retak pinggir 1,183%, lubang 0,031%, amblas 0,054%, retak kulit buaya 3,271%, retak kotak-kotak 3,222%, tambalan 0,033% dan pengelupasan butir 0,013%. Sedangkan untuk ruas kiri yaitu retak pinggir 0,035%, lubang 0,051%, amblas 0,000%, retak kulit buaya 0,130%, retak kotak-kotak 2,351%, tambalan 0,000% dan pengelupasan butir 0,150%. Kondisi perkerasan jalan yang menjadi objek penelitian sepanjang 4 km yaitu 85% baik, 0% sedang, 15% rusak ringan, 0% rusakberat.Road infrastructure if it is burdened by high and repetitive traffic volumes will cause a decrease in road quality so that it can affect safety, comfort and smoothness in traffic. To prevent deterioration in conditions, especially on the axis road of South Sabbang District, North Luwu Regency, precisely on Padang Sarre road, Buntu Terpedo to Dandang road along 4 km, it needs handling. So it is necessary to conduct an initial research on road surface conditions by conducting a visual survey by analyzing the damage based on the type and level of damage. The research objective was to assess pavement conditions and handling according to road surface conditions. This study uses a pavement condition assessment system according to Bina Marga with the calculation of the Surface Distress Index (SDI) for asphalt roads. From the research results obtained an assessment for the type of road surface damage on the right side, namely edge cracks 1.183%, holes 0.031%, collapse 0.054%, crocodile skin cracks 3.271%, checkered cracks 3.222%, 0.033% patches and 0.013% peeling grains. Whereas for the left section, the edges cracked 0.035%, holes 0.051%, collapsed 0.000%, crocodile skin cracks 0.130%, checkered cracks 2.351%, fillings 0.000% and peeling 0.150%. The condition of the pavement which is the object of the research along 4 km is 85% good, 0% moderate, 15% lightly damaged, 0% heavily damaged.

scholarly journals Road Hazard Assessment Using Pothole and Traffic Data in South Korea

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Choong Heon Yang ◽  
Jin Guk Kim ◽  
Sung Pil Shin
Keyword(s):  
Hazard Assessment ◽  
Traffic Safety ◽  
Hazard Index ◽  
Surface Damage ◽  
Road Surface ◽  
Risk Level ◽  
Traffic Data ◽  
Surface Conditions ◽  
The Road ◽  
Damage Data

Road surface conditions have a direct effect on the quality of driving, which in turn affects overall traffic flow. Many studies have been conducted to accurately identify road surface conditions using diverse technologies. However, these previously proposed methods may still be insufficient to estimate actual risks along the roads because the exact road risk levels cannot be determined from only road surface damage data. The actual risk level of the road must be derived by considering both the road surface damage data as well as other factors such as speed. In this study, the road hazard index is proposed using smartphone-obtained pothole and traffic data to represent the level of risk due to road surface conditions. The relevant algorithm and its operating system are developed to produce the estimated index values that are classified into four levels of road risk. This road hazard index can assist road agencies in establishing road maintenance plans and budgets and will allow drivers to minimize the risk of accidents by adjusting their driving speeds in advance of dangerous road conditions. To demonstrate the proposed risk hazard assessment methodology, road hazards were assessed along specific test road sections based on observed pothole and historical travel speed data. It was found that the proposed methodology provides a rational method for improving traffic safety.

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