Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management. (1st February 2023)

• Record Type: Journal Article
• Authors: Liu, Jianmiao; Li, Junyi; Chen, Yong; Lian, Song; Zeng, Jiaqi; Geng, Maosi; Zheng, Sijing; Dong, Yinan; He, Yan; Huang, Pei; Zhao, Zhijian; Yan, Xiaoyu; Hu, Qinru; Wang, Lei; Yang, Di; Zhu, Zheng; Sun, Yilin; Shang, Wenlong; Wang, Dianhai; Zhang, Lei; Hu, Simon; Chen, Xiqun (Michael)
• Is Part Of: Applied energy. Volume 331(2023)
• Publication Date: 2023-02-01
• Subjects: Urban carbon emissions -- Passenger transportation -- High-resolution CO2 emissions -- Smart mobility -- Big data analytics -- Sustainable transportation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120407
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Propose a bottom-up CO2 emissions calculation approach based on sparse individual trip chain data; Propose a novel multimodal individual trip chain identification and reconstruction method; Propose two city-scale trip information expansion methods based on population and checkpoint data; Develop a multi-scale high-resolution transportation carbon emission calculation platform; Explore potential implementations of carbon reduction policies in the transportation sector. Abstract: Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36, 435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40, 000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel rewards and carbon credits in transportation) have been discussed.