research-article

Impacts on Carbon Dioxide Emissions from the Replacement of Conventional Buses by Electric Buses

Authors:

Dapeng ZhangAuthors Info & Claims

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

Pages 175 - 180

https://doi.org/10.1145/3414274.3414501

Published: 26 August 2020 Publication History

Abstract

Public transport buses traversing in urban areas emit extensive carbon dioxide (CO2). It is critical to understand and estimate the characteristics of carbon emissions for transit buses to achieve a low-carbon transportation system. This paper compares the CO2 emissions between electric buses and conventional buses. We use the mobile sensor data of electric buses collected from Shenzhen to calculate CO2 emissions. To evaluate the CO2 emissions impacts of electric buses, we design four scenarios of different replacement rates of a conventional buses fleet by electric buses as a case study. The results demonstrate that the CO2 emissions of electric buses fleet can be reduced 34896.512 tons in comparison with conventional fuel buses by 2023. And the reduction rate of CO2 emissions will be 20.601%. Moreover, the reduction rate of CO2 emissions will be 0 if the CO2 emissions intensity is 742 gCO2 /kwh.

References

[1]

Reckien D, Ewald M, Edenhofer O, et al. What parameters influence the spatial variations in CO2 emissions from road traffic in Berlin? Implications for urban planning to reduce anthropogenic CO2 emissions[J]. Urban Studies, 2007, 44(2): 339--355.

[2]

U. S. Environmental Protection Agency. Accessed Mar. 2019. [Online]. Available: https://www.epa.gov/ghgemissions/sources-greenhouse-gasemissions

[3]

Boriboonsomsin K, Barth M. Impacts of road grade on fuel consumption and carbon dioxide emissions evidenced by use of advanced navigation systems[J]. Transportation Research Record, 2009, 2139(1): 21--30.

[4]

Axsen J, Orlebar C, Skippon S. Social influence and consumer preference formation for pro-environmental technology: The case of a UK workplace electric-vehicle study[J]. Ecological Economics, 2013, 95: 96--107.

[5]

Bradley T H, Frank A A. Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles[J]. Renewable and Sustainable Energy Reviews, 2009, 13(1): 115--128.

[6]

Shan X, Hao P, Chen X, et al. Vehicle energy/emissions estimation based on vehicle trajectory reconstruction using sparse mobile sensor data[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(2): 716--726.

[7]

He Z, Zhang W, Jia N. Estimating Carbon Dioxide Emissions of Freeway Traffic: A Spatiotemporal Cell-Based Model[J]. IEEE Transactions on Intelligent Transportation Systems, 2019.

[8]

Wang S, Li Z, Tan J, et al. A method for estimating carbon dioxide emissions based on low frequency GPS trajectories[C]//2017 Chinese Automation Congress (CAC). IEEE, 2017: 1960--1964.

[9]

Sun Z, Hao P, Ban X J, et al. Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data[J]. Transportation Research Part D: Transport and Environment, 2015, 34: 27--40.

[10]

Kan Z, Tang L, Kwan M P, et al. Estimating vehicle fuel consumption and emissions using GPS big data[J]. International journal of environmental research and public health, 2018, 15(4): 566.

[11]

Rakha H, Ahn K, Trani A. Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions[J]. Canadian Journal of Civil Engineering, 2003, 30(6): 1010--1021.

[12]

Ntziachristos L, Gkatzoflias D, Kouridis C, et al. COPERT: a European road transport emission inventory model[M]//Information technologies in environmental engineering. Springer, Berlin, Heidelberg, 2009: 491--504.

[13]

Scora G, Barth M. Comprehensive modal emissions model (cmem), version 3.01[J]. User guide. Centre for environmental research and technology. University of California, Riverside, 2006, 1070.

[14]

Papson A, Hartley S, Kuo K L. Analysis of emissions at congested and uncongested intersections with motor vehicle emission simulation 2010[J]. Transportation research record, 2012, 2270(1): 124--131.

[15]

Zhao Y, Sadek A W. Computationally-efficient approaches to integrating the MOVES emissions model with traffic simulators[J]. Procedia Computer Science, 2013, 19: 882--887.

[16]

Lejri D, Can A, Schiper N, et al. Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale[J]. Transportation research part D: Transport and Environment, 2018, 63: 588--603.

[17]

Sturm P J, Rodler J, Lechner B, et al. Validation of emission factors for road vehicles based on street tunnel measurements[J]. International journal of vehicle design, 2001, 27(1-4): 65--75.

[18]

Hausberger S, Rodler J, Sturm P, et al. Emission factors for heavy-duty vehicles and validation by tunnel measurements[J]. Atmospheric Environment, 2003, 37(37): 5237--5245.

[19]

Wang X, Peng L, Chi T, et al. A hidden Markov model for urban-scale traffic estimation using floating car data[J]. PloS one, 2015, 10(12).

[20]

Zhou X, Tanvir S, Lei H, et al. Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies[J]. Transportation Research Part D: Transport and Environment, 2015, 37: 123--136.

[21]

Hao P, Boriboonsomsin K, Wu G, et al. Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(3): 701--711.

Digital Library

[22]

Teixeira A C R, Sodré J R. Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions[J]. Transportation Research Part D: Transport and Environment, 2018, 59: 375--384.

[23]

Teixeira A C R, Sodré J R. Simulation of the impacts on carbon dioxide emissions from replacement of a conventional Brazilian taxi fleet by electric vehicles[J]. Energy, 2016, 115: 1617--1622.

[24]

Yuan X, Li L, Gou H, et al. Energy and environmental impact of battery electric vehicle range in China[J]. Applied Energy, 2015, 157: 75--84.

[25]

Ahn K, Rakha H, Trani A, et al. Estimating Vehicle Fuel Consumption and Emissions based on Instantaneous Speed and Acceleration Levels[J]. Journal of Transportation Engineering, 2002, 128(2):182--190.

[26]

Wang H, Shi H. Analysis of vehicle carbon emission in Chinese Context. Blue book of new energy vehicle: Annual report on new energy vehicle industry in China (2018) [M]. 2018.

[27]

China National Bureau of statistics. Steady growth of power production, adjustment and optimization of power supply structure [EB/OL]. http://www.stats.gov.cn/tjsj/zxfb/201803/t20180319_1588744.html, 2018--03-19.

[28]

Li X, Cui J, An S, et al. Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation[J]. Transportation Research Part B: Methodological, 2014, 70: 319--339.

Cited By

Sharma NDe PSharma NDe P(2024)Klimawandel und KI in den Finanz-, Energie-, Haushalts- und VerkehrssektorenAuf dem Weg zu Netto-Null-Zielen10.1007/978-981-97-0335-7_1(1-24)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-981-97-0335-7_1
Sharma NDe PSharma NDe P(2022)Climate Change and AI in the Financial, Energy, Domestic, and Transport SectorsTowards Net-Zero Targets10.1007/978-981-19-5244-9_1(1-21)Online publication date: 17-Sep-2022
https://doi.org/10.1007/978-981-19-5244-9_1

Index Terms

Impacts on Carbon Dioxide Emissions from the Replacement of Conventional Buses by Electric Buses
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Other conferences

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

July 2020

261 pages

ISBN:9781450376044

DOI:10.1145/3414274

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Natl University of Singapore: National University of Singapore
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2020

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Science and Technology Innovation Committee of Shenzhen
the National Key R&D Program of China (

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DSIT 2020

DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology

July 24 - 26, 2020

Xiamen, China

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DSIT 2020 Paper Acceptance Rate 40 of 97 submissions, 41%;

Overall Acceptance Rate 114 of 277 submissions, 41%

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Cited By

Sharma NDe PSharma NDe P(2024)Klimawandel und KI in den Finanz-, Energie-, Haushalts- und VerkehrssektorenAuf dem Weg zu Netto-Null-Zielen10.1007/978-981-97-0335-7_1(1-24)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-981-97-0335-7_1
Sharma NDe PSharma NDe P(2022)Climate Change and AI in the Financial, Energy, Domestic, and Transport SectorsTowards Net-Zero Targets10.1007/978-981-19-5244-9_1(1-21)Online publication date: 17-Sep-2022
https://doi.org/10.1007/978-981-19-5244-9_1

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