大数据驱动下的山地城市道路条件对车辆碳排放影响研究

doi:10.16097/j.cnki.1009-6744.2023.05.019

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (5): 172-183.DOI: 10.16097/j.cnki.1009-6744.2023.05.019

大数据驱动下的山地城市道路条件对车辆碳排放影响研究

周涛^1,2,3，李毅军^*1,3，孙琴梅^1,3，任瀚堃^1,2，刘怡^1,2，张振豪^1,3

1. 重庆市交通规划研究院，重庆 401147；2. 重庆市城市交通大数据工程技术研究中心，重庆 400020； 3. 山地城市交通系统与安全重庆市重点实验室，重庆 400074

收稿日期:2023-04-18 修回日期:2023-07-19 接受日期:2023-07-24 出版日期:2023-10-25 发布日期:2023-10-21
作者简介:周涛(1968- )，男，四川内江人，正高级工程师
基金资助:
重庆英才计划(CQYC20210207147)

Impact of Mountain Urban Roads on Vehicle Carbon Emissions Driven by Big Data

ZHOU Tao^1,2,3, LI Yi-jun^*1,3, SUN Qin-mei^1,3, REN Han-kun^1,2, LIU Yi^1,2, ZHANG Zhen-hao^1,3

1. Chongqing Transportation Planning and Research Institute, Chongqing 401147, China; 2. Chongqing Urban Transportation Big Data Engineering Technology Research Center, Chongqing 400020, China; 3. Chongqing Key Laboratory of Traffic System＆Safety in Mountainous Cities, Chongqing 400074, China

Received:2023-04-18 Revised:2023-07-19 Accepted:2023-07-24 Online:2023-10-25 Published:2023-10-21
Supported by:
Chongqing Talent Program (CQYC20210207147)

摘要/Abstract

摘要： 厘清城市道路与车辆碳排放的关系对于城市交通碳排放测算、城市交通建设及城市交通规划设计具有重要意义。本文基于车载自动诊断(On Board Diagnostics, OBD)大数据，以重庆市为例分析道路类型和道路坡度对车辆碳排放量的影响，探索重庆本土化的道路碳排放因子。首先，介绍OBD数据和处理方法，结合汽油燃料碳排放因子将车辆油耗换算为车辆碳排放量；然后，通过设定道路缓冲区、轨迹匹配及轨迹切割等方法建立车路匹配模型，根据车辆运行特征将车辆划分为非营运、营运及货运这 3 类；最后，采用莱文贝格－马夸尔特(Levenberg-Marquardt, LM)方法迭代拟合车辆平均速度和碳排放因子的关系。研究表明：提高城市道路平均速度至 25 km·h^-1 以上，对减少车辆碳排放量有显著效果；道路碳排放因子排序为次干路大于主干路大于快速路，与立交化严重和开口少等本土化特征相关；车辆碳排放因子对陡坡道路最敏感，影响排序为道路坡度大于车辆类型大于道路类型。

关键词: 城市交通, 车辆碳排放, OBD, 道路条件, 轨迹匹配方法, 轨迹切割方法

Abstract: Understanding the relationship between urban roads and vehicle carbon emissions is of great significance for the calculation of urban traffic carbon emissions, urban traffic construction, and urban traffic planning, design. Based on the OBD big data, this paper takes Chongqing as an example to analyze the influence of road type and road slope on vehicle carbon emissions, and explore the localized road carbon emission factors in Chongqing. First, introduce the OBD data and processing method, combine the gasoline fuel carbon emission factor to convert vehicle fuel consumption into vehicle carbon emissions. The characteristics divide the vehicles into three categories: non-operating, operating, and freight. Finally, the LM method is used to iteratively fit the relationship between the average vehicle speed and the carbon emission factor. The research shows that increasing the average speed of urban roads to over 25 km· h^-1 has a significant effect on reducing vehicle carbon emissions; the order of road carbon emission factors is that secondary roads are greater than arterial roads than expressways, which is related to serious interchanges and openings. The vehicle carbon emission factor is most sensitive to steep slope roads, and the order of influence is that the road slope is greater than the vehicle type than the road type.

Key words: urban traffic, vehicle carbon emissions, OBD, road conditions, trajectory matching method, trajectory cutting method

中图分类号:

U491.9

周涛, 李毅军, 孙琴梅, 任瀚堃, 刘怡, 张振豪. 大数据驱动下的山地城市道路条件对车辆碳排放影响研究[J]. 交通运输系统工程与信息, 2023, 23(5): 172-183.

ZHOU Tao, LI Yi-jun, SUN Qin-mei, REN Han-kun, LIU Yi, ZHANG Zhen-hao. Impact of Mountain Urban Roads on Vehicle Carbon Emissions Driven by Big Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 172-183.

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http://www.tseit.org.cn/CN/Y2023/V23/I5/172

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大数据驱动下的山地城市道路条件对车辆碳排放影响研究

Impact of Mountain Urban Roads on Vehicle Carbon Emissions Driven by Big Data

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