基于交通量预测的低交通需求地区弹性系数

Elasticity Coefficient in Low Traffic Demand Areas Based on Traffic Volume Prediction

摘要

为提高低交通需求地区交通量预测精度,提升交通建设资源利用率,基于某低交通需求地区2011—2022年的相关统计数据,分析了经济生产和交通发展的相关关系,验证了地区生产总值与各产业产值、汽车拥有量等指标作为弹性系数计算关键指标的可靠性。运用灰色关联方法(grey relational analysis,GRA)分析了产业经济总量与产业结构对弹性系数区域差异变化的影响程度,明确了产业结构是划分弹性系数研究区域的关键指标,并采用k-means方法将研究区域划分为3类地区。采用动态弹性系数计算方法和对数回归分析方法分别构建了不同地区弹性系数与时间的回归预测模型。最后,以该区域4条低交通量高速公路为例进行验证,对比了其2011—2022年的实际统计值和研究预测值。结果表明,弹性系数与交通量的预测精度分别达到了99.06%、91.09%。该研究为低交通需求地区交通量的合理预测和资源优化配置提供更科学的参考依据与方法。

Abstract

To improve the prediction accuracy of traffic volume and the utilization rate of transportation construction resources in low traffic demand areas, this study used statistical data from 2011 to 2022 in a specific low traffic demand area to analyze the correlation between economic production and transportation development. Correlation analysis was used to verify the reliability of key indicators, such as GDP, industrial output, and automobile ownership, for calculating the elasticity coefficient calculation. Additionally, grey relational analysis (GRA) was applied to assess the influence of the industrial economic volume and structure on the regional variability of elasticity coefficients. The study identified industrial structure as the crucial factor in dividing regions for elastic coefficient research and used k-means clustering method to categorize the study area into three types of regions. Furthermore, the regression prediction model of the elasticity coefficient over time was developed by dynamic elasticity coefficient calculation methods and logarithmic regression analysis in different areas. Finally, the study validated the models using four low-traffic-volume expressways in the region, comparing actual statistical data with predicted values from 2012 to 2022. Results show that the prediction accuracy of elasticity coefficient and traffic volume reaches 99.06% and 91.09%, respectively. The research provides a more scientific reference basis and more accurate method for the rational forecasting of traffic volume and the optimal resource allocation in low traffic demand areas.

关键词

低交通需求 / 公路建设 / 交通量预测 / 弹性系数 / 产业结构 / 回归预测

Key words

low traffic demand / highway construction / traffic volume forecasting / elasticity coefficient / industrial structure / regression prediction

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基金资助

内蒙古自治区交通运输厅建设科技项目(NJ-2021-01)

贵州省交通运输厅科技项目(2022-122-017)