Connected Vehicle Solution for Winter Road Surface Condition Monitoring

Author(s):  
Michael A. Linton ◽  
Liping Fu
2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu

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.


Author(s):  
Mikko Perttunen ◽  
Oleksiy Mazhelis ◽  
Fengyu Cong ◽  
Mikko Kauppila ◽  
Teemu Leppänen ◽  
...  

Author(s):  
Adham Mohamed ◽  
Mohamed Mostafa M. Fouad ◽  
Esraa Elhariri ◽  
Nashwa El-Bendary ◽  
Hossam M. Zawbaa ◽  
...  

2019 ◽  
Vol 55 (Supplement) ◽  
pp. 2C3-5-2C3-5
Author(s):  
Kazuya ITOH ◽  
Ryoma ITOH

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