交通运输系统工程与信息

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (5): 237-245.DOI: 10.16097/j.cnki.1009-6744.2024.05.022

• 系统工程理论与方法 • 上一篇    下一篇

山地城市道路交通碳排放影响因素非线性关系模型

陈坚*，陈嘉果，陈琦，刘柯良，代雪杨   

  1. 重庆交通大学，交通运输学院，重庆 400074
  • 收稿日期:2024-05-23 修回日期:2024-07-04 接受日期:2024-07-08 出版日期:2024-10-25 发布日期:2024-10-22
  • 作者简介:陈坚(1985- )，男，江西赣州人，教授，博士。
  • 基金资助:
    重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0195)；重庆市哲学社会科学创新工程重点项目(2024CXZD025)。

Nonlinear Relationship Model of Factors Influencing Carbon Emissions in Mountainous Urban Road Transportation

CHEN Jian*, CHEN Jiaguo, CHEN Qi, LIU Keliang, DAI Xueyang   

  1. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
  • Received:2024-05-23 Revised:2024-07-04 Accepted:2024-07-08 Online:2024-10-25 Published:2024-10-22
  • Supported by:
    Chongqing Special Key Project of Technological Innovation and Application Development (CSTB2022TIAD-KPX0195)；Key Project of the Philosophy and Social Sciences Innovation Program of Chongqing (2024CXZD025)。

PDF

摘要： 为定量测度山地城市道路交通碳排放量及其影响因素的作用大小，本文运用车载移动监测设备采集小汽车不同时空下的碳排放量，结合山地城市道路交通特征，选取汽车发动机特性、驾驶特性和道路交通环境这3个维度的8个变量作为解释变量。基于随机森林回归模型，构建山地城市道路交通碳排放影响因素解析模型，运用部分依赖函数量化各影响因素对碳排放的非线性关系和交互影响，并进行多模型对比分析。研究结果表明：随机森林回归模型拟合优度为74.3%，预测精度较高，优于多元线性回归、支持向量机和长短期记忆网络(LSTM)模型；从影响贡献上看，速度(25.97%)和排气筒温度(23.73%)是影响碳排放因子最重要的因素；速度和高程变化对碳排放因子的非线性作用显著，其中速度的阈值在58 km·h-1，高程变化的阈值在0.10 m，应优化山地城市立交、陡坡等复杂交通构造物，减少车辆速度波动区间；低速行驶与其余因素存在正向的交互作用。研究结果可结合道路交通量及路况信息动态测算道路交通碳排放，为交通管理策略设计提供决策依据。

关键词: 城市交通, 碳排放, PEMS, 随机森林, 非线性关系

Abstract: To quantitatively measure the carbon emissions of road transportation in mountainous cities and its influencing factors, this paper uses portable emission measurement system to collect the carbon emissions of cars in different time and space, and selects eight variables in three dimensions, including vehicle engine characteristics, driving characteristics and road traffic environment, which are explanatory variables in combination with the road traffic characteristics of mountain cities. Utilizing the random forest regression model, this paper developed an analytical model to understand the factors influencing carbon emissions from road transportation in mountainous cities. The partial dependence function was used to quantitatively analyze the non-linear and interactive effects of these factors on carbon emissions. Additionally, a comparative analysis was conducted across multiple models. The results indicate: (1) The random forest regression model has better performance and prediction accuracy than the multiple linear regression, support vector machine and Long Short-Term Memory Network (LSTM) models, with a goodness-of-fit of 74.3% . (2) From the perspective of impact contribution, speed (25.97% ) and exhaust cylinder temperature (23.73% ) are the most important factors affecting carbon emission factors. (3) The nonlinear effect of velocity and elevation change on carbon emission factor is significant, and the threshold of speed is 58 km⋅h-1 and the threshold of elevation change is 0.10 m. Complex traffic structures such as overpasses and steep slopes in mountainous cities should be optimized to reduce the range of vehicle speed fluctuations. (4) There is a positive interaction between low-speed driving and other factors. The research results can be combined with real-time calculation of road traffic carbon emissions based on road traffic volume and road condition information, providing decision-making basis for traffic management strategy design.

Key words: urban traffic, carbon emissions, PEMS, random forest, nonlinear relationships

中图分类号: