| 基于轨迹数据的网约车排放时空特征分析 |
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| 引用本文： | 韩印,李媛媛,李文翔,刘向龙,祁昊,万东奇. 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. DOI: 10.16097/j.cnki.1009-6744.2022.01.025 |
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| 作者姓名： | 韩印 李媛媛 李文翔 刘向龙 祁昊 万东奇 |
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| 作者单位： | 1. 上海理工大学，管理学院，上海 200093；2. 交通运输部科学研究院，北京 100029； 3. 城市公共交通智能化交通运输行业重点实验室，北京 100029 |
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| 基金项目： | 国家自然科学基金；上海市晨光计划；城市公共交通智能化交通运输行业重点实验室开放课题 |
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| 摘 要： | 网约车逐渐成为城市中重要的交通方式之一，由于网约车出行特征与其他交通方式显著不同，其环境影响仍有待深入研究。为揭示网约车的排放特征，基于成都市网约车GPS轨迹数据，采用大数据分析方法得到网约车在各轨迹段的平均速度、行驶里程等参数，然后应用机动车排放模型COPERT实现对研究区域内网约车CO、HC、NOx和CO2排放的量化，并进一步分析其时空分布特征。结果显示：2016年11月18日成都市研究区域内网约车CO、NOx、HC、CO2的排放量分别为151,41.5,8.93,125497.6 kg；网约车排放的高峰时段发生在9:00-10:00、14:00-15:00和17:00-18:00；网约车高排放区域主要分布于二环高架路、二环路、蜀都大道附近，其中部分路段交叉口的排放最为突出；区域平均速度可显著影响该区域网约车平均排放因子。因此，政府相关部门可针对网约车高排放时段和地区，采取交通需求管理及车辆限速控制等治理手段，以减少中心城区的交通排放。研究成果可为网约车环境影响评估提供科学方法，为城市网约车管理...
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| 关 键 词： | 城市交通 排放分析 轨迹数据挖掘 网约车 时空特征 |
| 收稿时间： | 2021-08-10 |
Analyzing Spatiotemporal Characteristics of Ridesourcing Emissions Based on Trajectory Data |
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| HAN Yin,LI Yuan-yuan,LI Wen-xiang,LIU Xiang-long,QI Hao,WAN Dong-qi. Analyzing Spatiotemporal Characteristics of Ridesourcing Emissions Based on Trajectory Data[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 234-242. DOI: 10.16097/j.cnki.1009-6744.2022.01.025 |
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| Authors: | HAN Yin LI Yuan-yuan LI Wen-xiang LIU Xiang-long QI Hao WAN Dong-qi |
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| Affiliation: | 1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;2. China Academy of Transportation Sciences, Beijing 100029, China;3. Key Laboratory of Advanced Public Transportation Science, Beijing 100029, China |
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| Abstract: | Ridesourcing has gradually become one of the most important transportation modes in cities. Because thetravel characteristics of ridesourcing are significantly different from other transportation modes, the environmentalimpacts of ridesourcing are worthy of detailed study. To reveal the emission characteristics of ridesourcing, this papercollected the parameters of average speed and mileage of ridesourcing vehicles in each trajectory segment using theGlobal Positioning System (GPS) trajectory data of ridesourcing services in Chengdu. The vehicle emission modelCOPERT is applied to quantify the CO, HC, NOx, and CO2 emissions of ridesourcing in the study area. The spatial andtemporal distribution characteristics are also analyzed for the emissions. The results show that the CO, NOx, HC andCO2 emissions of ridesourcing are respectively 151, 41.5, 8.93, 125497.6 kg, in the study area on November 18, 2016.The peak hours of the ridesourcing emissions occurred at 9:00-10:00 am, 2:00-3:00 pm, and 5:00-6:00 pm. Areaswith high emissions of ridesourcing are mainly distributed near the Second Ring Elevated Road, the Second RingRoad, and Shudu Avenue, with even more emissions at the intersections of some sections. The regional average speedsignificantly affects the average emission factors of ridesourcing in the region. Therefore, the authorities can take measures such as traffic demand management and vehicles speed limit control to reduce traffic emissions in the centralcity for high emission periods and areas of online hailing. The study provides a scientific method for the environmentalimpact assessment of ridesourcing, and serves as a decision basis for the formulation of policies related to ridesourcingmanagement in the city |
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| Keywords: | urban traffic emission analysis trajectory data mining ridesourcing spatiotemporal characteristics |
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