Abstract: The Chengdu-Chongqing Economic Circle, as a core area of the national strategic hinterland, holds significant importance for regional economic development and national security, where accurate prediction of highway freight volume is crucial. Based on panel data from 16 prefecture-level cities in the Chengdu-Chongqing region from 2010 to 2023, a prediction model for highway freight volume was constructed and key influencing factors were identified. Initially, 16 explanatory variables were preliminarily selected from dimensions such as socio-economic activities. Through Spearman rank correlation analysis, significance testing, and city fixed-effects models, seven core variables were screened out, including the number of employed personnel, total number of tourists and regional heterogeneity coefficients. Then, a systematic comparison of the performance of eight machine learning models, such as extreme random trees and gradient boosting decision trees, was conducted, using hyperparameter optimization methods to select the most suitable prediction model for highway freight in the Chengdu-Chongqing region. Finally, feature importance analysis was performed based on SHAP values. The research results indicate that the extreme random trees and gradient bosting decision trees models perform optimally, with coefficients of determination (R2) above 0.95, errors below 10% and no underfitting or overfitting issues, demonstrating the superior applicability of the extreme random trees model for predicting highway freight volume in the Chengdu-Chongqing region. The number of employed personnel, total number of tourists and regional heterogeneity coefficients were identified as key influencing factors. Among them, the number of employed personnel shows an approximately linear positive correlation with highway freight volume, the total number of tourists drives freight growth through demand-side effects, while the regional heterogeneity coefficients exhibit complex nonlinear effects due to differences in urban resources and industrial structure.

Key words: traffic and transportation engineering; freight volume; machine learning; influencing factors; feature importance analysis

摘要： 成渝地区双城经济圈作为国家战略腹地核心区，其公路货运量的精准预测对区域经济发展与国家安全具有重要意义。基于2010—2023年成渝地区16个地级市的面板数据，构建了公路货运量预测模型并识别关键影响因素。从社会经济活动等维度初选出16个解释变量，通过Spearman秩相关分析、显著性检验与城市固定效应模型，筛选出就业人员数量、旅游总人数、区域异质性系数等7个核心变量；采用超参数优化方法系统比较了极端随机树、梯度提升决策树等8种机器学习模型的性能，选出适用于成渝地区公路货运的预测模型；基于SHAP值进行了特征重要性分析。研究结果显示：极端随机树和梯度提升决策树模型表现最优，其决定系数(R2)均高于0.95，误差低于10%，且未出现欠拟合或过拟合情况，表明极端随机树模型在成渝地区公路货运量预测中具有最优适用性；就业人员数量、旅游总人数与区域异质性系数为关键影响因素，其中就业人员数量与公路货运量近似呈线性正相关，旅游总人数通过需求侧拉动货运量增长，区域异质性系数则因城市资源与产业结构差异呈现复杂非线性效应。

关键词: 交通运输工程； 货运量； 机器学习； 影响因素； 特征重要性分析