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High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane

Received: 13 December 2021    Accepted: 23 December 2021    Published: 9 February 2022
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Abstract

This work seeks to assess the impact of adding a lane for slower trucks on a divided multilane highway on CO2 emissions. A portion of the U.S. 101 highway in San Luis Obispo County in California consists of the Cuesta Grade which is a 2.75-mile segment with a 7% grade. A microsimulation software, VISSIM, was used in conjunction with the Environmental Protection Agency’s emissions model, MOVES, to estimate CO2 emissions on the corridor before and after the construction of the third lane. It was found that CO2 emissions decreased between 1998 (before) and 2012 (after the 2003 lane addition), but the effect of the truck lane was shown to be different for the northbound (uphill) and southbound (downhill) directions. The truck lane in the northbound direction exhibited a 9.5% decrease in volume with 10.7% decrease in emissions, and the southbound direction experienced a 20.3% increase in volume but 7.4% decrease in emissions. For the northbound (uphill) direction, emissions seemed to correlate more closely with traffic volumes while a sensitivity analysis revealed travel speeds had a more profound effect on southbound (downhill) emission rates. In the conclusion section, ideas to further validate the emissions estimate are discussed. Emissions seemed to correlate more closely with traffic volumes (uphill) while travel speeds had a more profound effect on southbound (downhill) emission rates. One factor to be accounted for is the change in volume which seems to play a much larger role in emissions than roadway features or topography.

Published in American Journal of Traffic and Transportation Engineering (Volume 7, Issue 1)
DOI 10.11648/j.ajtte.20220701.13
Page(s) 19-27
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Emissions, Traffic Simulation, VISSIM, MOVES

References
[1] Environmental Protection Agency (EPA). (2014). Sources of Greenhouse Gas Emissions. Retrieved from https://www3.epa.gov/climatechange/ghgemissions/sources/transportation.html
[2] Abou-Senna, H., Radwan, E. (2013). VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions, Journal of Transportation Research Part D: Transport and Environment, 21, 39-46. doi: 10.1016/j.trd.2013.02.003.
[3] Abou-Senna, H., Radwan, E., Westerlund, K., & Cooper, C. D. (2013). Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway, Journal of the Air & Waste Management Association, 63 (7), 819-831. doi: 10.1080/10962247.2013.795918.
[4] Ahn, K., Rakha, H., Trani, A., & Aerde, M. V. (2002). Estimating Vehicle Fuel Consumption and Emissions based on Instantaneous Speed and Acceleration Levels, Journal of Transportation Engineering, 128 (2), 182-190.
[5] Chamberlin, R., Swanson, B., Talbor, E., Dumont, J., & Pesci, S. (2010). Analysis of MOVES and CMEM for Evaluating the Emissions Impacts of an Intersection Control Change. Paper presented at Transportation Research Board 2011. Washington, D.C.
[6] Barth, M., Scora, G., & Younglove, T. (2014). Model Emissions Model for Heavy-Duty Diesel Vehicles, Transportation Research Record: Journal of the Transportation Research Board, 1880 (1), 10-20. doi: http://dx.doi.org/10.3141/1880-02
[7] Nam, E. K., Gierczak, C. A., & Butler, J. W. (2002). A Comparison Of Real-World and Modeled Emissions Under Conditions of Variable Driver Aggressiveness. Paper presented at Transportation Research Board 2003. Washington, D.C.
[8] Chamberlin, R., Swanson, B., Talbot, E., Sharma, S., & Crouch, P. (2011). Toward Best Practices for Conducting a MOVES Project-Level Analysis. Paper presented at Transportation Research Board 2012. Washington, D.C.
[9] Chu, H. & Meyer, M. D. (2009). Methodology for assessing emission reduction of truck-only toll lanes, Energy Policy, 37, 3287-3294.
[10] Boriboonsomsin, K. & Barth, M. (2009). Impacts of Road Grade on Fuel Consumption and Carbon Dioxide Emissions Evidenced by Use of Advanced Navigation Systems, Transportation Research Record: Journal of the Transportation Research Board, 2139, 21-30. doi: 10.3141/2139-03.
[11] Liu, H., Rodgers, M., & Guensler, R. Impact of road grade on vehicle speed-acceleration distribution, emissions and dispersion modeling on freeways. Transportation Research Part D: Transport and Environment, Volume 69, April 2019, Pages 107-122.
[12] Llopis-Castelló, D., Pérez-Zuriaga, A., Camacho-Torregrosa, F., & García, A. Impact of horizontal geometric design of two-lane rural roads on vehicle CO2 emissions. Transportation Research Part D: Transport and Environment, Volume 59, 2018, Pages 46-57.
[13] Llopis-Castelló, D., Camacho-Torregrosa, F., & García, A. Analysis of the influence of geometric design consistency on vehicle CO2 emissions, Transportation Research Part D: Transport and Environment, Volume 69, 2019, Pages 40-50, ISSN 1361-9209, https://doi.org/10.1016/j.trd.2019.01.029.
[14] Papson, A., Hartley, S., & Kuo, K. (2011). Analysis of Emissions at Congested and Uncongested Intersections Using MOVES2010. Paper presented at Transportation Research Board 2012. Washington, D.C.
[15] Perugu, H., Wei, H., & Yao, Z. Developing high-resolution urban scale heavy-duty truck emission inventory using the data-driven truck activity model output. Atmospheric Environment, Volume 155, April 2017, Pages 210-230.
[16] Punzo, V. & Simonelli, F. (2005). Analysis and comparison of microscopic traffic flow models with real traffic microscopic data, Transportation Research Record, 1934, 53-63.
[17] Mahesh, S., Ramadurai, G., & Nagendra, S. On-board measurement of emissions from freight trucks in urban arterials: Effect of operating conditions, emission standards, and truck size. Atmospheric Environment, Volume 212, 1 September 2019, Pages 75-82.
[18] San Luis Obispo County of Governments (SLOCOG). (2003, October). Cuesta Grade Project Completed. Retrieved from: http://www.slocog.org/sites/default/files/newsletter/Oct03%20newsletter.pdf
[19] Caltrans. (2016). Traffic Census Program. Retrieved from http://www.dot.ca.gov/trafficops/census/
[20] Wisconsin Department of Transportation. (2014). Model Calibration. Retrieved from: http://www.wisdot.info/microsimulation/index.php?title=Model_Calibration
[21] Bliemel, F. (1973). Theil’s Forecast Accuracy Coefficient: A Clarification, Journal of Marketing Research, X, 444-446.
[22] Tang, E. C. (2017). Quantifying the Impact of Truck only lanes on Vehicular Emissions on a Limited-Access Highway. MS Thesis, California Polytechnic State University, San Luis Obispo, CA.
[23] Maness, H. L., Thurlow, M. E., McDonald, B. C., & Harley, R. A. (2015). Estimates of CO2 traffic emissions from mobile concentration measurements, Journal of Geophysical Research: Atmospheres, 120. doi: 10.1002/2014JD022876.
Cite This Article
  • APA Style

    Edward Tang, Hatem Abou-Senna, Anurag Pande, Robert Bertini. (2022). High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane. American Journal of Traffic and Transportation Engineering, 7(1), 19-27. https://doi.org/10.11648/j.ajtte.20220701.13

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    ACS Style

    Edward Tang; Hatem Abou-Senna; Anurag Pande; Robert Bertini. High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane. Am. J. Traffic Transp. Eng. 2022, 7(1), 19-27. doi: 10.11648/j.ajtte.20220701.13

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    AMA Style

    Edward Tang, Hatem Abou-Senna, Anurag Pande, Robert Bertini. High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane. Am J Traffic Transp Eng. 2022;7(1):19-27. doi: 10.11648/j.ajtte.20220701.13

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  • @article{10.11648/j.ajtte.20220701.13,
      author = {Edward Tang and Hatem Abou-Senna and Anurag Pande and Robert Bertini},
      title = {High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane},
      journal = {American Journal of Traffic and Transportation Engineering},
      volume = {7},
      number = {1},
      pages = {19-27},
      doi = {10.11648/j.ajtte.20220701.13},
      url = {https://doi.org/10.11648/j.ajtte.20220701.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20220701.13},
      abstract = {This work seeks to assess the impact of adding a lane for slower trucks on a divided multilane highway on CO2 emissions. A portion of the U.S. 101 highway in San Luis Obispo County in California consists of the Cuesta Grade which is a 2.75-mile segment with a 7% grade. A microsimulation software, VISSIM, was used in conjunction with the Environmental Protection Agency’s emissions model, MOVES, to estimate CO2 emissions on the corridor before and after the construction of the third lane. It was found that CO2 emissions decreased between 1998 (before) and 2012 (after the 2003 lane addition), but the effect of the truck lane was shown to be different for the northbound (uphill) and southbound (downhill) directions. The truck lane in the northbound direction exhibited a 9.5% decrease in volume with 10.7% decrease in emissions, and the southbound direction experienced a 20.3% increase in volume but 7.4% decrease in emissions. For the northbound (uphill) direction, emissions seemed to correlate more closely with traffic volumes while a sensitivity analysis revealed travel speeds had a more profound effect on southbound (downhill) emission rates. In the conclusion section, ideas to further validate the emissions estimate are discussed. Emissions seemed to correlate more closely with traffic volumes (uphill) while travel speeds had a more profound effect on southbound (downhill) emission rates. One factor to be accounted for is the change in volume which seems to play a much larger role in emissions than roadway features or topography.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - High-Resolution Modelling of Carbon Dioxide Emissions Before and After the Implementation of a Designated Truck Lane
    AU  - Edward Tang
    AU  - Hatem Abou-Senna
    AU  - Anurag Pande
    AU  - Robert Bertini
    Y1  - 2022/02/09
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    DO  - 10.11648/j.ajtte.20220701.13
    T2  - American Journal of Traffic and Transportation Engineering
    JF  - American Journal of Traffic and Transportation Engineering
    JO  - American Journal of Traffic and Transportation Engineering
    SP  - 19
    EP  - 27
    PB  - Science Publishing Group
    SN  - 2578-8604
    UR  - https://doi.org/10.11648/j.ajtte.20220701.13
    AB  - This work seeks to assess the impact of adding a lane for slower trucks on a divided multilane highway on CO2 emissions. A portion of the U.S. 101 highway in San Luis Obispo County in California consists of the Cuesta Grade which is a 2.75-mile segment with a 7% grade. A microsimulation software, VISSIM, was used in conjunction with the Environmental Protection Agency’s emissions model, MOVES, to estimate CO2 emissions on the corridor before and after the construction of the third lane. It was found that CO2 emissions decreased between 1998 (before) and 2012 (after the 2003 lane addition), but the effect of the truck lane was shown to be different for the northbound (uphill) and southbound (downhill) directions. The truck lane in the northbound direction exhibited a 9.5% decrease in volume with 10.7% decrease in emissions, and the southbound direction experienced a 20.3% increase in volume but 7.4% decrease in emissions. For the northbound (uphill) direction, emissions seemed to correlate more closely with traffic volumes while a sensitivity analysis revealed travel speeds had a more profound effect on southbound (downhill) emission rates. In the conclusion section, ideas to further validate the emissions estimate are discussed. Emissions seemed to correlate more closely with traffic volumes (uphill) while travel speeds had a more profound effect on southbound (downhill) emission rates. One factor to be accounted for is the change in volume which seems to play a much larger role in emissions than roadway features or topography.
    VL  - 7
    IS  - 1
    ER  - 

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Author Information
  • Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, USA

  • Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, USA

  • Civil and Environmental Engineering, California Polytechnic State University, San Luis Obispo, USA

  • Civil and Construction Engineering, Oregon State University, Corvallis, USA

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