Analyzing Spatiotemporal Characteristics of Ridesourcing Emissions
Based on Trajectory Data

doi:10.16097/j.cnki.1009-6744.2022.01.025

Abstract

Abstract: Ridesourcing has gradually become one of the most important transportation modes in cities. Because the travel characteristics of ridesourcing are significantly different from other transportation modes, the environmental impacts of ridesourcing are worthy of detailed study. To reveal the emission characteristics of ridesourcing, this paper collected the parameters of average speed and mileage of ridesourcing vehicles in each trajectory segment using the Global Positioning System (GPS) trajectory data of ridesourcing services in Chengdu. The vehicle emission model COPERT is applied to quantify the CO, HC, NOx, and CO2 emissions of ridesourcing in the study area. The spatial and temporal distribution characteristics are also analyzed for the emissions. The results show that the CO, NOx, HC and CO2 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. Areas with high emissions of ridesourcing are mainly distributed near the Second Ring Elevated Road, the Second Ring Road, and Shudu Avenue, with even more emissions at the intersections of some sections. The regional average speed significantly 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 central city for high emission periods and areas of online hailing. The study provides a scientific method for the environmental impact assessment of ridesourcing, and serves as a decision basis for the formulation of policies related to ridesourcing management in the city

Key words: urban traffic, emission analysis, trajectory data mining, ridesourcing, spatiotemporal characteristics

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，为城市网约车管理的相关政策制定提供决策依据。

关键词: 城市交通, 排放分析, 轨迹数据挖掘, 网约车, 时空特征

CLC Number:

U491

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.

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.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2022.01.025

http://www.tseit.org.cn/EN/Y2022/V22/I1/234

References

[1] ALEMI F, CIRCELLA G, HANDY S, et al. What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California[J]. Travel Behaviour and Society, 2018, 13: 88-104.

[2] DIAS F F, LAVIERI P S, GARIKAPATI V M, et al. A behavioral choice model of the use of car-sharing and ride-sourcing services[J]. Transportation, 2017, 44(6): 1307-1323.

[3] TIRACHINI A, GOMEZ-LOBO A. Does ride-hailing increase or decrease vehicle kilometers traveled (VKT)? A simulation approach for Santiago de Chile[J]. International Journal of Sustainable Transportation, 2020, 14(3): 187-204.

[4] ZHENG H, CHEN X, CHEN X M. How does on-demand ridesplitting influence vehicle use and purchase willingness? A case study in Hangzhou, China[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 11 (3): 143-157.

[5] ALONSO-MORA J, SAMARANAYAKE S, WALLAR A, et al. On-demand high-capacity ride-sharing via dynamic trip- vehicle assignment[J]. Proceedings of the National Academy of Sciences, 2018, 114(3): 462-467.

[6] KUCHARSKI R, CATS O. Exact matching of attractive shared rides (ExMAS) for system-wide strategic evaluations[J]. Transportation Research Part B: Methodological, 2020, 139: 285-310.

[7] LI W, PU Z, LI Y, et al. Characterization of ridesplitting based on observed data: A case study of Chengdu, China [J]. Transportation Research Part C: Emerging Technologies, 2019, 100: 330-353.

[8] 龙雪琴, 周萌, 赵欢, 等. 基于网络核密度的网约车上下客热点识别[J]. 交通运输系统工程与信息, 2021, 21 (3): 86- 93. [LONG X Q, ZHOU M, ZHAO H, et al. Passengers' hot spots identification of online car- hailing based on network kernel density[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 86-93.]

[9] DONG Y, WANG S, LI L, et al. An empirical study on travel patterns of internet based ride-sharing[J]. Transportation Research Part C: Emerging Technologies, 2018, 86: 1-22.

[10] SUI Y, ZHANG H, SONG X, et al. GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions[J]. Journal of Cleaner Production, 2019, 227: 495-505.

[11] LIU H B, CHEN X H, WANG Y Q, et al. Vehicle emission and near-road air quality modeling for Shanghai, China: Based on global positioning system data from taxis and revised MOVES emission inventory [J]. Transportation Research Record: Journal of the Transportation Research Board, 2013, 2340(1): 38-48.

[12] LUO X, DONG L, DOU Y, et al. Analysis on spatialtemporal features of taxis' emissions from big data informed travel patterns: A case of Shanghai, China[J]. Journal of Cleaner Production, 2016, 142: 926-935.

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

[14] LI T, JING P, LI L C, et al. Revealing the varying impact of urban built environment on online car-hailing travel in spatio-temporal dimension: An exploratory analysis in Chengdu, China[J]. Sustainability, 2019, 11(5): 1336.

[15] 王燕军, 王鸣宇, 吉喆, 等. 国外机动车排放模型综述研究 [J]. 环境与可持续发展, 2020(5): 161- 166. [WANG Y J, WANG M Y, JI J et al. Review of vehicle emission models in foreign countries[J]. Environment and Sustainable Development, 2020(5): 161-166 .]

[16] KOURIDIS C, NTZIACHRISTOS L, SAMARAS Z. COPERT III, Computer programme to calculate emissions from road transport[R]. Denmark: European Environment Agency, 2000.

[17] HAO C, XIE S. Estimation of vehicular emission inventories in China from 1980 to 2005[J]. Atmospheric Environment, 2007, 41(39): 8963-8979.

[18] YAN L X, LUO X, ZHU R, et al. Quantifying and analyzing traffic emission reductions from ridesharing: A case study of Shanghai[J]. Transportation Research Part D, 2020, 89: 102629.

[19] LI W X, PU Z Y, LI Y Y, et al. How does ridesplitting reduce emissions from ridesourcing? A spatiotemporal analysis in Chengdu, China[J]. Transportation Research Part D, 2021, 95: 102885.

[20] NTZIACHRISTOS L, SAMARAS Z. Methodology for the calculation of exhaust emissions[R]. Denmark: European Environment Agency, 2018.

[21] LI M, YU L, ZHAI Z Q, et al. Development of emission factors for an urban road network based on speed distributions[J]. Journal of Transportation Engineering, 2016, 142(9): 04016036

[22] WANG A J, GE Y S, TAN J W, et al. On-road pollutant emission and fuel consumption characteristics of buses in Beijing[J]. Journal of Environmental Sciences, 2011, 23(3): 419-426.

[23] LIU D G, LOU D M, LIU J, et al. Evaluating nitrogen oxides and ultrafine particulate matter emission features of urban bus based on real-world driving conditions in the Yangtze River Delta Area, China[J]. Sustainability, 2018, 10(6): 2051.

[24] LIU H B, CHEN X H, WANG Y Q, et al. Vehicle emission and near-road air quality modeling for Shanghai, China: Based on global positioning system data from taxis and revised MOVES emission inventory [J]. Transportation Research Record: Journal of the Transportation Research Board, 2013, 2340(1): 38-48.

Analyzing Spatiotemporal Characteristics of Ridesourcing Emissions Based on Trajectory Data

基于轨迹数据的网约车排放时空特征分析

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	YANG Ya-zao, TANG Hao-dong, PENG yong. Residents' Travel Mode Choice Behavior in Post-COVID-19 era Considering Preference Differences [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 15-24.
[2]	CHEN Qi-xiang , LV Bin , CHEN Xi-qun , HAO Bin-bin, HE Jia-xi. Impacts of Built Environment on Competition and Cooperation Relationship Between Taxi and Subway Considering Spatial Heterogeneity [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 25-35.
[3]	CHEN Guang-hui , JI Hao , JIN Fu-qiang, GUO Qing-e , JIA Bin , SU Bing. Operation Efficiency Evaluation on Urban Taxi Considering Energy Types [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 36-44.
[4]	CHEN Yao , BAI Yun , ZHANG An-ying , MAO Bao-hua , CHEN Shao-kuan. Dynamic Fleet Management of Shared Autonomous Vehicles with Rolling Horizon Optimization [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 45-52.
[5]	XU Xin-yue , XIE Lan-shi-yu. Route Choice Behavior of Urban Rail Transit Passengers Based on Guidance Information [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 63-73.
[6]	GE Xian-long, LI Ting , WANG Bo , YIN Zuo-fa . Electric Vehicle Charging Induction with Minimization of Negative Effects [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 74-83.
[7]	YUE Hao , LIU Jian-ye, LI Chong-nan. Customized Bus Route Optimization with Price Window [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 93-103.
[8]	SONG Li-jing , BAI Tong-zhou , HE Yu-long , CHEN Yan-yan, LIU Xue-jie , MA Teng-teng. Feeder Bus Routes and Frequency Optimization Based on Mixed Integer Nonlinear Programming [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 104-111.
[9]	SHI Jun-gang , QIN Tan , LI Xiang , YANG Li-xing, YANG Xiao-guang. Train Capacity Allocation Strategy and Optimization Model for an Oversaturated Metro Line [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 112-119.
[10]	HU Li-wei , ZHAO Xue-ting. Migration Law of Urban Traffic Congestion Risk Distribution Considering Uncertainty of Boundary Conditions [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 120-131.
[11]	LI Bing , YANG Hong-yu , ZHENG Zuo-xiong , FENG Yue , WANG Zheng-hui. Evaluation Model of Traffic Organization for Left-turn Prohibition Based on Macroscopic Fundamental Diagram [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 179-189.
[12]	LI Chang-min , WEI Wen-bin, ZHANG Lu. Rental and Allocation of Shared Mixed Parking Spaces Considering Matching Priority [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 190-197.
[13]	LV Wei, HUANG Guang-chen , WANG Jing-hui . Simulation of Highway Traffic Bottleneck Via Cellular Automata [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 293-302.
[14]	LIU Xiao-bing , LI Feng-xiao , TIAN Xin-mei, YAN Xue-dong. Identifying Metropolitan Center Structure Based on Commuting Patterns [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 17-28.
[15]	XU Qi, CHEN Yue , HUANG Jing-ru , GAO Shun-xiang , ZHANG Zhi-jian. Job Accessibility Analysis Considering Travel Cost [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 37-44.