Journal of Engineering Research
Journal of Engineering Research. 2025; 4: (1) ; 10.12208/j.jer.20250004 .
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1中海建筑有限公司 贵州贵阳
2华中科技大学土木与水利工程学院 湖北武汉
*通讯作者： 王宇阳,单位：华中科技大学土木与水利工程学院 湖北武汉；
目的 建筑工程货运车辆行程时间预测对于项目管理至关重要。其在城市中行驶受到不同因素的影响，通过捕捉影响因素特征提高预测精度已成为当前亟需解决的问题。方法 首先，使用图卷积网络（Graph convolution network, GCN）捕捉城市网格中行驶轨迹的空间特征；然后，借助转换器网络（Transformer）捕捉城市网格中行驶轨迹的时间特征；其次，将外部因素作为文本信息，通过嵌入模型提取属性特征；最后，构建基于多元特征的工程货运车辆行程时间预测模型。将武汉市内某一建设项目的2320条行驶轨迹作为实验数据，划分行驶区域为500×500、1000×1000和1500×1500m的网格序列，借助本文模型预测工程运输车辆行程时间，并与基线模型进行对比。结果 研究结果表明：三种空间粒度下，本文模型预测结果的均方根误差分别为89.08、111.32和141.6；与基线模型相比，该模型的均方根误差分别平均降低了25.83%、26.43%和27%。结论 研究成果为工程运输行业行程时间预测提供了新的思路，可为项目管理提供技术支撑。
Objective The travel time prediction of construction freight vehicles is very important for project management. Its driving in the city is affected by different factors. It has become an urgent problem to improve the prediction accuracy by capturing the characteristics of influencing factors. Methods Firstly, the spatial features of travel tracks in the urban grid were captured using the Graph convolution network (GCN). Then, the Transformer network was used to capture the time characteristics of the driving trajectory in the urban grid; Secondly, the external factors were taken as text information, and the attribute features are extracted by embedding the model. Finally, the travel time prediction model of construction freight vehicles based on multiple features was constructed. 2320 driving tracks of a construction project in Wuhan were taken as experimental data and divided into grid sequences with driving areas of 500×500, 1000×1000 and 1500×1500m. The travel time of engineering transport vehicles was predicted by this model and compared with the baseline model. [Findings]The results show that the root-mean-square errors (RMSE) of the model prediction results are 89.08, 111.32 and 141.6, respectively. Compared with the baseline model, the RMSE of this model were reduced by an average of 25.83%, 28.43%, and 27%, respectively. Conclusion s The research results provide a new idea for the travel time prediction of engineering transportation industry, and can provide technical support for project management.
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