Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data

Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent o...

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Main Authors: Lukai Zhang, Xiaoya Wang, Yingping Wang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/24/24/7930
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author Lukai Zhang
Xiaoya Wang
Yingping Wang
author_facet Lukai Zhang
Xiaoya Wang
Yingping Wang
author_sort Lukai Zhang
collection DOAJ
description Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks. Initially, using the Highway Asphalt Pavement Design Specification, it analyzes the demand for these calculations in road sections. Considering the current axle load detection coverage, a method supported by highway traffic data is proposed. For integrating multi-source data, a generalized regression neural network model is established, enabling deep learning calculations. The method is validated and applied to Xuzhou’s highway network. Results show consistency between the calculated average axle load spectrum and actual data. Among validation samples, 3-axle vehicles exhibit the smallest deviation, while 6-axle vehicles show the largest. Calculating equivalent axle numbers reveals the distribution and grading of heavily loaded road sections, aiding maintenance decisions.
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institution Kabale University
issn 1424-8220
language English
publishDate 2024-12-01
publisher MDPI AG
record_format Article
series Sensors
spelling doaj-art-048885c8cc7f4e3a8eee4022a38a63162024-12-27T14:52:28ZengMDPI AGSensors1424-82202024-12-012424793010.3390/s24247930Deep Learning Calculation and Application of Axle Loads in Highway Sensor DataLukai Zhang0Xiaoya Wang1Yingping Wang2Transport Planning and Research Institute, Ministry of Transport, Chaoyang District, Beijing 100028, ChinaReading Academy, Nanjing University of Information Science and Technology, Pukou District, Nanjing 210044, ChinaTransport Planning and Research Institute, Ministry of Transport, Chaoyang District, Beijing 100028, ChinaAxle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks. Initially, using the Highway Asphalt Pavement Design Specification, it analyzes the demand for these calculations in road sections. Considering the current axle load detection coverage, a method supported by highway traffic data is proposed. For integrating multi-source data, a generalized regression neural network model is established, enabling deep learning calculations. The method is validated and applied to Xuzhou’s highway network. Results show consistency between the calculated average axle load spectrum and actual data. Among validation samples, 3-axle vehicles exhibit the smallest deviation, while 6-axle vehicles show the largest. Calculating equivalent axle numbers reveals the distribution and grading of heavily loaded road sections, aiding maintenance decisions.https://www.mdpi.com/1424-8220/24/24/7930highway sensorsmaintenance managementaxle load spectrumequivalent axle volumedeep learning
spellingShingle Lukai Zhang
Xiaoya Wang
Yingping Wang
Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
Sensors
highway sensors
maintenance management
axle load spectrum
equivalent axle volume
deep learning
title Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
title_full Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
title_fullStr Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
title_full_unstemmed Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
title_short Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data
title_sort deep learning calculation and application of axle loads in highway sensor data
topic highway sensors
maintenance management
axle load spectrum
equivalent axle volume
deep learning
url https://www.mdpi.com/1424-8220/24/24/7930
work_keys_str_mv AT lukaizhang deeplearningcalculationandapplicationofaxleloadsinhighwaysensordata
AT xiaoyawang deeplearningcalculationandapplicationofaxleloadsinhighwaysensordata
AT yingpingwang deeplearningcalculationandapplicationofaxleloadsinhighwaysensordata