Using Deep Learning to Predict the Amount of Chemicals Applied on the Wheel Track for Winter Road Maintenance

被引:2
|
作者
Hatamzad, Mahshid [1 ]
Polanco Pinerez, Geanette Cleotilde [1 ]
Casselgren, Johan [2 ]
机构
[1] UiT Arctic Univ Norway, Dept Ind Engn, N-8514 Narvik, Nordland, Norway
[2] Lulea Univ Technol, Dept Engn Sci & Math, S-97187 Lulea, Sweden
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 07期
关键词
deep neural network; deep learning; intelligent road transportation; prediction model; salting; winter road maintenance; PAVEMENT; IMPACTS; SALT;
D O I
10.3390/app12073508
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The decade of big data has emerged in recent years, which has led to entering the era of intelligent transportation. One of the main challenges to deploying intelligent transportation is dealing with winter roads in cold climate countries. Different operations can be used to protect the road from ice and snow, such as spreading chemicals (here salt) on the road surface. Using salt for de-icing and anti-icing increases road safety. However, the excess use of salt must be avoided since it is not cost-efficient and has negative impacts on the environment. Therefore, the accurate and timely prediction of salt quantity for winter road maintenance helps decision support systems to achieve effective and efficient winter road maintenance. Thus, this paper performs exploratory data analysis to determine the relationships among variables to find the best prediction model for this problem. Due to the stochastic nature of variables regarding weather and roads, a deep neural network/deep learning is selected to predict the amount of salt on the wheel track, using historical data measured by sensors and road weather stations. The results show that the proposed model performs perfectly to learn and predict the amount of salt on the wheel track, based on different metrics, including the loss function, scatter plot, mean absolute error, and explained variance.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Optimal track geometry maintenance limits using machine learning: A case study
    Kasraei, Ahmad
    Zakeri, Jabbar Ali
    Bakhtiary, Arash
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT, 2021, 235 (07) : 876 - 886
  • [42] Road traffic flow prediction using deep transfer learning
    Wang, Bin
    Yan, Zheng
    Lu, Jie
    Zhang, Guangquan
    Li, Tianrui
    DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT, 2018, 11 : 331 - 338
  • [43] Road and railway detection in SAR images using deep learning
    Sen, Nigar
    Olgun, Orhun
    Ayhan, Oner
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXV, 2019, 11155
  • [44] Deep representation learning for road detection using Siamese network
    Huafeng Liu
    Xiaofeng Han
    Xiangrui Li
    Yazhou Yao
    Pu Huang
    Zhenmin Tang
    Multimedia Tools and Applications, 2019, 78 : 24269 - 24283
  • [45] Deep representation learning for road detection using Siamese network
    Liu, Huafeng
    Han, Xiaofeng
    Li, Xiangrui
    Yao, Yazhou
    Huang, Pu
    Tang, Zhenmin
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (17) : 24269 - 24283
  • [46] Underground Mine Road Detection Using Deep Learning Technique
    Tong, Zhixue
    Zhang, Wenda
    Zhang, Xuefeng
    APPLIED SCIENCES-BASEL, 2023, 13 (07):
  • [47] Road Course Estimation using Deep Learning on Radar Data
    Giese, Tilmann
    Klappstein, Jens
    Dickmann, Juergen
    Woehler, Christian
    2017 18TH INTERNATIONAL RADAR SYMPOSIUM (IRS), 2017,
  • [48] Automobile Maintenance Prediction Using Deep Learning with GIS Data
    Chen, Chong
    Liu, Ying
    Sun, Xianfang
    Di Cairano-Gilfedder, Carla
    Titmus, Scott
    52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS), 2019, 81 : 447 - 452
  • [49] Prescriptive Maintenance of Freight Vehicles using Deep Reinforcement Learning
    Tham, Chen-Khong
    Liu, Weihao
    Chattopadhyay, Rajarshi
    2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING, 2023,
  • [50] Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis
    Viatkin, Dmitry
    Garcia-Zapirain, Begonya
    Mendez Zorrilla, Amaia
    APPLIED SCIENCES-BASEL, 2021, 11 (07):