出租车碳排放时空分布特征及减排潜力评估

doi:10.16097/j.cnki.1009-6744.2025.01.029

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 311-318.DOI: 10.16097/j.cnki.1009-6744.2025.01.029

出租车碳排放时空分布特征及减排潜力评估

王明智^1,2，金敬东²，董春娇^*1，李鹏辉¹，王菁¹，王君悦¹

1. 北京交通大学，综合交通运输大数据应用技术交通运输行业重点实验室，北京100044;2. 交通运输部规划研究院，综合交通规划数字化实验室，北京100028

收稿日期:2024-08-30 修回日期:2024-09-29 接受日期:2024-10-16 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:王明智(1995—)，男，河南淮阳人，博士生。
基金资助:
国家社科基金重大项目(23&ZD138)。

Spatiotemporal Distribution Characteristics and Reduction Potential Assessment of Taxi Carbon Emissions

WANG Mingzhi^1,2, JIN Jingdong², DONG Chunjiao^*1, LI Penghui¹, WANG Jing¹, WANG Junyue¹

1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Laboratory for Traffic & Transport Planning Digitalization, Transport Planning and Research Institute Ministry of Transport, Beijing 100028, China

Received:2024-08-30 Revised:2024-09-29 Accepted:2024-10-16 Online:2025-02-25 Published:2025-02-24
Supported by:
Major Program of National Social Science Foundation (23&ZD138)。

摘要/Abstract

摘要： 为揭示出租车碳排放时空分布特征，本文基于出租车GPS(GlobalPositioningSystem)轨迹数据提取轨迹点间的平均速度和行驶里程等参数，构建COPERT(ComputerProgrammetoCalculate Emissions from Road Transport)微观排放模型量化出租车排放。在此基础上，采用分布拟合分析排放在时间、空间和车辆上的分布特征。最后，基于分析结果提出出租车限行和速度管控两项交通管理措施，并通过数值模拟对措施的减排潜力进行评估。以长治市为例进行实证研究，结果表明，出租车行业存在零和博弈现象，排放更加均匀分布于8:00-13:00和14:00-22:00之间。节点和路段上集聚的排放量服从截断幂律分布，排放量最高的前10%的节点和路段分别汇集了95.59%和74.71%的排放量。评估结果表明，出租车限行政策最高能减少约20.35%的排放量；出租车行驶速度为15m·s-1左右时，排放因子最低，且将速度保持在15m·s-1时，最高能减少21.43%的排放量；选取排放量最高的前10%的路段进行速度管控能减少16.23%的排放，而随机选取相同数量的路段仅能减少2.37%的排放。结果可为城市交通制定精细化的碳排放管控策略和节能减排措施提供支持。

关键词: 城市交通, 时空特征, 幂律分布, 出租车, 零和博弈, CO₂

Abstract: To investigate the spatiotemporal distribution characteristics of taxi carbon emissions, this study extracts parameters such as average speed and travel distance between trajectory points from taxi Global Positioning System (GPS) data and constructs the Computer Programme to Calculate Emissions from Road Transport (COPERT) micro- emission model to quantify taxi emissions. Based on this, distribution fitting is used to analyze the distribution characteristics of emissions over time, space, and among vehicles. Based on the analysis results, two traffic management measures—taxi restriction and speed control—are proposed, and their emission reduction potential is evaluated through numerical simulations. An empirical study in Changzhi City shows that the taxi industry exhibits a zero-sum game phenomenon, with emissions more evenly distributed between 8:00-13:00 and 14:00-22:00. Emissions aggregated at nodes and road segments follow a truncated power-law distribution, with the top 10% of nodes and road segments accounting for 95.59% and 74.71% of emissions, respectively. The evaluation results indicate that the taxi restriction policy can reduce emissions by up to 20.35%, maintaining a taxi speed of 15 m ⋅ s-1 results in the lowest emission factor, potentially reducing emissions by up to 21.43%. Implementing speed control on the top 10% of road segments with the highest emissions can reduce emissions by 16.23%, while randomly selecting the same number of segments for speed control can only reduce emissions by 2.37%. The results can support the development of refined carbon emission control strategies and energy-saving measures for urban transportation.

Key words: urban traffic, spatiotemporal characteristics, power-law distribution, taxis, zero-sum game, carbon dioxide(CO₂)

中图分类号:

U491

王明智, 金敬东, 董春娇, 李鹏辉, 王菁, 王君悦. 出租车碳排放时空分布特征及减排潜力评估[J]. 交通运输系统工程与信息, 2025, 25(1): 311-318.

WANG Mingzhi, JIN Jingdong, DONG Chunjiao, LI Penghui, WANG Jing, WANG Junyue. Spatiotemporal Distribution Characteristics and Reduction Potential Assessment of Taxi Carbon Emissions[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 311-318.

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http://www.tseit.org.cn/CN/Y2025/V25/I1/311

参考文献

[1]陈丹,于慧,汤程,等.城市公共客运交通碳排放及其大气环境影响的实证研究[J].交通运输系统工程与信息, 2023, 23(4): 1-10. [CHEN D, YU H, TANG C, et al. Empirical study on carbon dioxide emissions and atmospheric environment impact of urban public passenger transportation[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(4): 1-10.]

[2]NYHAN M, SOBOLEVSKY S, KANG C G, et al. Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emission's model[J]. Atmospheric Environment, 2016, 140: 352-363.

[3]LUO X, DONG L, DOU Y, et al. Analysis on spatial temporal features of taxis' emissions from big data informed travel patterns: A case of Shanghai, China[J]. Journal of Cleaner Production, 2017, 142: 926-935.

[4]ZHANG J, CHEN F, WANG Z, et al. Spatiotemporal patterns of carbon emissions and taxi travel using GPS data in Beijing[J]. Energies, 2018, 11(3): 500.

[5]LIU J L, HAN K, CHEN X Q, et al. Spatial-times new romantemporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data[J]. Transportation Research Part C: Emerging Technologies, 2019, 106: 145-165.

[6]贾涛,李琦,马楚,等.武汉市出租车轨迹二氧化碳排放的时空模式分析[J]. 武汉大学学报(信息科学版),2019, 44(8): 1115-1123. [JIA T, LI Q, MA C, et al. Computing the CO2 emissions of taxi trajectories and exploring their spatiotemporal patterns in Wuhan city[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1115-1123.]

[7]LI T, WU J, DANG A, et al. Emission pattern mining based on taxi trajectory data in Beijing[J]. Journal of Cleaner Production, 2019, 206: 688-700.

[8]韩印,李媛媛,李文翔,等.基于轨迹数据的网约车排放时空特征分析[J].交通运输系统工程与信息,2022,22(1): 234-242. [HAN Y, LI Y Y, LI W X, et al. Analyzing spatiotemporal characteristics of ridesourcing emissions based on trajectory data[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 234-242.]

[9]TIAN D, ZHANG J, LI B, et al. Spatial analysis of commuting carbon emissions in main urban area of Beijing: A GPS trajectory-based approach[J]. Ecological Indicators, 2024, 159: 111610.

[10] KINSELLA L, STEFANIEC A, FOLEY A, et al. Pathways to decarbonising the transport sector: The impacts of electrifying taxi fleets[J]. Renewable and Sustainable Energy Reviews, 2023, 174: 113160.

[11] ZHANG L, WU S, LENG K, et al. Carbon emission reduction pathways for taxis based on the whole life cycle [J]. Transportation Research Part D: Transport and Environment, 2024, 132: 104236.

[12] ZHANG Z, GAO K, HE H D, et al. Environmental impacts of ridesplitting considering modal substitution and associations with built environment[J]. Transportation Research Part D: Transport and Environment, 2024, 130: 104160.

[13] WU J, JIA P, FENG T, et al. Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications[J]. Transportation Research Part D: Transport and Environment, 2023, 121: 103839.

[14] CLAUSET A, SHALIZI C R, NEWMAN M E J. Power law distributions in empirical data[J]. SIAM Review, 2009, 51(4): 661-703.

[15] BÖHM M, NANNI M, PAPPALARDO L. Gross polluters and vehicle emissions reduction[J]. Sustainability, 2022, 5(8): 699-707.

出租车碳排放时空分布特征及减排潜力评估

Spatiotemporal Distribution Characteristics and Reduction Potential Assessment of Taxi Carbon Emissions

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