Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (10): 2771-2780.doi: 10.13229/j.cnki.jdxbgxb.20230297

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Weight estimation model for trucks integrating multi-head attention mechanism

Ming-chen GU1,2(),Hui-yuan XIONG1,2(),Zeng-jun LIU1,2,Qing-yu LUO3,Hong LIU1,2   

  1. 1.Transport Planning and Research Institute，Ministry of Transport，Beijing 100028，China
    2.Laboratory for Traffic and Transport Planning Digitalization，Beijing 100028
    3.College of Transportation，Jilin University，Changchun 130022，China
  • Received:2023-04-03 Online:2024-10-01 Published:2024-11-22
  • Contact: Hui-yuan XIONG E-mail:gumc@tpri.org.cn;380711471@qq.com

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Abstract:

In order to improve the real-time truck load estimation of convenience and accuracy， and to help to develop real-time monitoring for truck loads in a large-scale low-grade highway networks， this paper proposed a weight estimation model（Mix-MAN） for trucks integrating the multi-head attention mechanism based on the interaction effect of dynamic and static information of trucks. Firstly， multi-head attention was introduced into the model to enhance the network's ability to extract kinematic time series features； secondly， a stacked auto-encoder was used to capture the static features of trucks； finally， a feature fusion structure was designed to extract dynamic features and static features， establish the nonlinear mapping relationship between input features and weight estimation， and then to obtain the final weight estimation result of trucks. The experimental results show that compared with the MAN model without considering static information of truck， the mean absolute value error of Mix-MAN is reduced by 6%， root mean square error is reduced by 5%， mean absolute percentage error is reduced by 0.5%. The model in this paper can provide technical support for the cargo transport supervision of highway and road maintenance.

Key words: engineering of communication and transportation, load estimation, multi-head attention, feature fusion, stacked auto-encoders

CLC Number: 

  • U495

Fig.1

Architecture of multi-head attention block"

Fig.2

Architecture of SAE module"

Fig.3

Architecture of Mix-MAN"

Fig.4

Flowchart of truck weight estimation"

Table 1

Description of datasets"

数据集数据类型主要字段
高速公路收费入口-治超站称重数据车辆称重数据车牌、入口时间、收费车型、车长、轴数、核定质量、车辆总重等
交调轴载站称重数据车辆称重数据车牌、经过时间、轴型、轴数、轴重、车辆总重等
货车轨迹数据驾驶轨迹数据车辆ID、时间、速度（km/h）、车辆转向角等

Fig.5

Data processing flow chart"

Table 2

Description of input features"

类别变量名称变量类型取值范围
车辆运动学时序特征速度连续变量0~120 km/h
加速度连续变量—
车辆静态 特征收费车型离散变量11~16
轴数离散变量2~6
车长连续变量—
入口时间连续变量2021.8.1 00：00 ~2021.9.1 00：00

Fig.6

Model performance under different time window lengths"

Table 3

Performance of different models"

模 型评价指标
MAERMSEMAPE/%
GRU1.511.563.6
Mix-GRU1.521.493.2
LSTM1.481.523.5
Mix-LSTM1.471.503.2
MAN1.421.503.1
Mix-MAN1.341.422.6

Fig.7

Truck estimation results of different models"

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