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Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

Author

Listed:
  • Roozbeh Jalali

    (Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canada)

  • Seama Koohi-Fayegh

    (Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Canada)

  • Khalil El-Khatib

    (Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Ontario, Canada)

  • Daniel Hoornweg

    (Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Canada)

  • Heng Li

    (College of Computer Science and Electronic Engineering, Hunan University, China)

Abstract

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily.

Suggested Citation

  • Roozbeh Jalali & Seama Koohi-Fayegh & Khalil El-Khatib & Daniel Hoornweg & Heng Li, 2017. "Investigating the Potential of Ridesharing to Reduce Vehicle Emissions," Urban Planning, Cogitatio Press, vol. 2(2), pages 26-40.
    • Handle: RePEc:cog:urbpla:v2:y:2017:i:2:p:26-40
    DOI: 10.17645/up.v2i2.937
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    References listed on IDEAS

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    1. Hao, Han & Liu, Zongwei & Zhao, Fuquan & Li, Weiqi & Hang, Wen, 2015. "Scenario analysis of energy consumption and greenhouse gas emissions from China's passenger vehicles," Energy, Elsevier, vol. 91(C), pages 151-159.
    2. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    3. Tsao, H.-S. Jacob & Lin, Da-Jie, 1999. "Spatial and Temporal Factors in Estimating the Potential of Ride-sharing for Demand Reduction," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2p57q0c9, Institute of Transportation Studies, UC Berkeley.
    4. Wang, Haikun & Fu, Lixin & Bi, Jun, 2011. "CO2 and pollutant emissions from passenger cars in China," Energy Policy, Elsevier, vol. 39(5), pages 3005-3011, May.
    5. Wu, Ye & Yang, Zhengdong & Lin, Bohong & Liu, Huan & Wang, Renjie & Zhou, Boya & Hao, Jiming, 2012. "Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of China," Energy Policy, Elsevier, vol. 48(C), pages 537-550.
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    Cited by:

    1. Soora Rasouli & Harry Timmermans & Dujuan Yang, 2017. "Three Tales about Limits to Smart Cities Solutions," Urban Planning, Cogitatio Press, vol. 2(2), pages 1-3.

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