DOI QR코드

DOI QR Code

Changes in air pollutant emissions from road vehicles due to autonomous driving technology: A conceptual modeling approach

  • Hwang, Ha (Division of Disaster & Safety Research, Korea Institute of Public Administration) ;
  • Song, Chang-Keun (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
  • 투고 : 2019.03.21
  • 심사 : 2019.05.21
  • 발행 : 2020.06.30

초록

The autonomous vehicles (AVs) could make a positive or negative impact on reducing mobile emissions. This study investigated the changes of mobile emissions that could be caused by large-scale adoption of AVs. The factors of road capacity increase and speed limit increase impacts were simulated using a conceptual modeling approach that combines a hypothetical speed-emission function and a traffic demand model using a virtual transportation network. The simulation results show that road capacity increase impact is significant in decreasing mobile emissions until the market share of AVs is less than 80%. If the road capacity increases by 100%, the mobile emissions will decrease by about 30%. On the other hand, driving speed limit increase impact is significant in increasing mobile emissions, and the environmentally desirable speed limit was found at around 95 km/h. If the speed limit increases to 140 km/h, the mobile emissions will increase by about 25%. This is because some vehicles begin to bypass the congested routes at high speeds as speed limit increases. Based on the simulation results, it is clear that the vehicle platooning technology implemented at reasonable speed limit is one of the AV technologies that are encouraging from the environmental point of view.

키워드

참고문헌

  1. Miller SA, Heard BR. The environmental impact of autonomous vehicles depends on adoption patterns. Environ. Sci. Technol. 2016;50:6119-6121. https://doi.org/10.1021/acs.est.6b02490
  2. Fagnant DJ, Kockelman K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015;77:167-181. https://doi.org/10.1016/j.tra.2015.04.003
  3. Bansal P, Kockelman KM. Forecasting Americans' long-term adoption of connected and autonomous vehicle technologies. Transp. Res. Part A Policy Pract. 2017;95:49-63. https://doi.org/10.1016/j.tra.2016.10.013
  4. Alexander D, Gartner J. Self-driving vehicles, advanced driver assistance systems, and autonomous driving features: Global market analysis and forecasts. Navigant Consulting, Inc.; 2014.
  5. Bierstedt J, Gooze A, Gray C, Peterman J, Raykin L, Walters J. Effects of next-generation vehicles on travel demand and highway capacity. FP Think Working Group; 2014. p. 10-11.
  6. Laslau C, Holman M, Saenko M, See K, Zhang Z. Set autopilot for profits: Capitalizing on the $87 billion self-driving car opportunity. Lux Research; 2014.
  7. Litman T. Autonomous vehicle implementation predictions. Victoria, Canada: Victoria Transport Policy Institute; 2017.
  8. Anenberg SC, Miller J, Minjares R, et al. Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets. Nature 2017;545:467-471. https://doi.org/10.1038/nature22086
  9. Huang Y, Ng ECY, Zhou JL, Surawski NC, Chan EFC, Hong G. Eco-driving technology for sustainable road transport: A review. Renew. Sust. Energ. Rev. 2018;93:596-609. https://doi.org/10.1016/j.rser.2018.05.030
  10. Huang Y, Organ B, Zhou JL, et al. Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong. Atmos. Environ. 2018;182:58-74. https://doi.org/10.1016/j.atmosenv.2018.03.035
  11. EPA. Environmental Protection Agency releases MOVES 2010 Mobile Source Emission Model: Questions and Answers. G. EPA-420-F-09-073, Washington D.C.; 2009.
  12. Los B, Timmer MP, de Vries GJ. How global are global value chains? A new approach to measure international fragmentation. J. Reg. Sci. 2015;55:66-92. https://doi.org/10.1111/jors.12121
  13. Fernandes P, Nunes U. Platooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow. IEEE Trans. Intell. Transp. Syst. 2012;13:91-106. https://doi.org/10.1109/TITS.2011.2179936
  14. Fernandes P, Nunes U. Platooning of autonomous vehicles with intervehicle communications in SUMO traffic simulator. In: 13th International IEEE Conference on Intelligent Transportation Systems; 19-22 September 2010; Funchal, Portugal: IEEE.
  15. Shladover SE, Su D, Lu X-Y. Impacts of cooperative adaptive cruise control on freeway traffic flow. Transp. Res. Rec. 2012;2324:63-70. https://doi.org/10.3141/2324-08
  16. Brown A, Gonder J, Repac B. An analysis of possible energy impacts of automated vehicles. In: Road vehicle automation. New York: Springer; 2014. p. 137-153.
  17. Stephens TS, Gonder J, Chen Y, Lin Z, Liu C, Gohlke D. Estimated bounds and important factors for fuel use and consumer costs of connected and autonomous vehicles. No. NREL/TP-5400-67216. Golden CO, United States: National Renewable Energy Lab. (NREL); 2016.
  18. Horowitz R, Varaiya P. Control design of an automated highway system. Proc. IEEE 2000;88:913-925. https://doi.org/10.1109/5.871301
  19. Milanes V, Villagra J, Godoy J, Simo J, Perez J, Onieva E. An intelligent V2I-based traffic management system. IEEE Trans. Intell. Transp. Syst. 2012;13:49-58. https://doi.org/10.1109/TITS.2011.2178839
  20. Van der Voort M, Dougherty MS, van Maarseveen M. A prototype fuel-efficiency support tool. Transp. Res. Part C Emerg. Technol. 2001;9:279-296. https://doi.org/10.1016/S0968-090X(00)00038-3
  21. Wu C, Zhao G, Ou B. A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles. Transp. Res. Part D Transp. Environ. 2011;16:515-524. https://doi.org/10.1016/j.trd.2011.06.002
  22. Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp. Res. Part C Emerg. Technol. 2016;71:143-163. https://doi.org/10.1016/j.trc.2016.07.007
  23. Wadud Z, MacKenzie D, Leiby P. Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transpor. Res. Part A Policy Pract. 2016;86:1-18. https://doi.org/10.1016/j.tra.2015.12.001
  24. Tientrakool P, Ho Y-C, Maxemchuk NF. Highway capacity benefits from using vehicle-to-vehicle communication and sensors for collision avoidance. In: 2011 IEEE Vehicular Technology Conference (VTC Fall); 5-8 September 2011; San Francisco, CA, USA: IEEE.
  25. Atiyeh C. Predicting traffic patterns, one Honda at a time. MSN Auto; June 2012.
  26. Scholz T, Schmallowsky A, Wauer T. Auswirkungen eines allgemeinen tempolimits auf autobahnen im land brandenburg [Internet]. Schlothauer & Wauer; c2007. Available from: http://www.mil.brandenburg.de/media_fast/4055/studie_tempolimit.pdf.
  27. Yu S, Shi Z. Dynamics of connected cruise control systems considering velocity changes with memory feedback. Measurement 2015;64:34-48. https://doi.org/10.1016/j.measurement.2014.12.036
  28. Bose A, Ioannou PA. Analysis of traffic flow with mixed manual and semiautomated vehicles. IEEE Trans. Intell. Transp. Syst. 2003;4:173-188. https://doi.org/10.1109/TITS.2003.821340
  29. Tang TQ, Li JG, Huang HJ, Yang XB. A car-following model with real-time road conditions and numerical tests. Measurement 2014;48:63-76. https://doi.org/10.1016/j.measurement.2013.10.035
  30. Yu S, Shi Z. An extended car-following model considering vehicular gap fluctuation. Measurement 2015;70:137-147. https://doi.org/10.1016/j.measurement.2015.03.031
  31. Yu S, Shi Z. An improved car-following model considering relative velocity fluctuation. Commun. Nonlinear Sci. Numer. Simul. 2016;36:319-326. https://doi.org/10.1016/j.cnsns.2015.11.011
  32. Abou-Senna H, Radwan E. VISSIM/MOVES integration to investigate the effect of major key parameters on $CO_2$ emissions. Transp. Res. Part D Transp. Environ. 2013;21:39-46. https://doi.org/10.1016/j.trd.2013.02.003
  33. Abou-Senna H, Radwan E, Westerlund K, Cooper CD. Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway. J. Air Waste Manage. Assoc. 2013;63:819-831. https://doi.org/10.1080/10962247.2013.795918
  34. Shah R, Nezamuddin N, Levin MW. Supply-side network effects on mobile-source emissions. Transp. Policy 2018 (in press).
  35. Stevanovic A, Stevanovic J, Zhang K, Batterman S. Optimizing traffic control to reduce fuel consumption and vehicular emissions: Integrated approach with VISSIM, CMEM, and VISGAOST. Transp. Res. Rec. 2009;2128:105-113. https://doi.org/10.3141/2128-11
  36. Boyce D. Forecasting travel on congested urban transportation networks: Review and prospects for network equilibrium models. Netw. Spat. Econ. 2007;7:99-128. https://doi.org/10.1007/s11067-006-9009-0
  37. Lee S. Mathematical programming algorithms for equilibrium road traffic assignment [dissertation]. London: Univ. of London; 1995.
  38. LeBlanc LJ, Morlok EK, Pierskalla WP. An efficient approach to solving the road network equilibrium traffic assignment problem. Transp. Res. 1975;9:309-318. https://doi.org/10.1016/0041-1647(75)90030-1
  39. United States, Bureau of Public Roads. Traffic assignment manual for application with a large, high speed computer. Washington D.C.: US Department of Commerce, Bureau of Public Roads, Office of Planning Urban Planning Division; 1964.
  40. Frank M, Wolfe P. An algorithm for quadratic programming. Nav. Res. Logist. 1956;3:95-110. https://doi.org/10.1002/nav.3800030109
  41. National Institute of Environmantal Research. Air pollutant emission factors: Estimated by 2012 emissions. Ministry of Environment, Incheon, Republic of Korea. 2015 (Korean).
  42. Li Y, Pearson B, Murrells T. Updated vehicle emission curves for use in the National Transport Model. Report to the Department for Transport; 2009.
  43. Wang J, Rakha HA. Fuel consumption model for conventional diesel buses. Appl. Energ. 2016;170:394-402. https://doi.org/10.1016/j.apenergy.2016.02.124
  44. Kamali M, Dennis LA, McAree O, Fisher M, Veres S. Formal verification of autonomous vehicle platooning. Sci. Comput. Prog. 2017;148:88-106. https://doi.org/10.1016/j.scico.2017.05.006
  45. Iacobucci R, McLellan B, Tezuka T. Modeling shared autonomous electric vehicles: Potential for transport and power grid integration. Energy 2018;158:148-163. https://doi.org/10.1016/j.energy.2018.06.024
  46. Elliott C. Should you buy a new car? Read this first. In: Forbes; 14 November 2018.
  47. Varun M, Kumar C. Problems in electric vehicles. Int. J. Appl. Res. Mech. Eng. 2012;2:63-73.
  48. Toma S. Six problems with electric cars that nobody talks about. In: Autoevolution; 6 November 2017.
  49. Massey J. Charging electric vehicles: The challenges ahead. In: energypost.eu.; 8 February 2018.
  50. Aengenheyster M, Feng QY, Ploeg F, Dijkstra HA. The point of no return for climate action: Effects of climate uncertainty and risk tolerance. Earth Syst. Dynam. 2018;9:1085-1095. https://doi.org/10.5194/esd-9-1085-2018
  51. IPPC. Intergovernmental panel on climate change. In: IPCC Sixth Assessment Report; 2018.
  52. US Environmental Protection Agency. Light-duty automotive technology, carbon dioxide emissions, and fuel economy trends: 1975 through 2017; January 2018.
  53. Harper C, Mangones S, Hendrickson C, Samaras C. Bounding the potential increases in vehicles miles traveled for the non-driving and elderly populations and people with travel-restrictive medical conditions in an automated vehicle environment. In: Trnsportation Research Board 94th Annual Meeting; 11-15 January 2015; Washington D.C.
  54. MacKenzie D, Wadud Z, Leiby P. A first order estimate of energy impacts of automated vehicles in the United States. In: Transportation Research Board Annual Meeting; Washington D.C.; 2014. p. 12-16.
  55. Childress S, Nichols B, Charlton B, Coe S. Using an activity-based model to explore the potential impacts of automated vehicles. Transp. Res. Rec. 2015;2493:99-106. https://doi.org/10.3141/2493-11
  56. Gucwa M. Mobility and energy impacts of automated cars. In: Proceedings of the Automated Vehicles Symposium; San Francisco; 2014.
  57. Hymel KM, Small KA, Van Dender K. Induced demand and rebound effects in road transport. Transp. Res. Part B Methodol. 2010;44:1220-1241. https://doi.org/10.1016/j.trb.2010.02.007
  58. Cervero R. Induced demand: An urban metropolitan perspective. Univ. of California Transportation Center; UC Berkeley; 2001.
  59. Fagnant DJ, Kockelman KM. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 2014;40:1-13. https://doi.org/10.1016/j.trc.2013.12.001
  60. Wang B. Waymo started its commercial self-driving ride sharing service. In: NextBigFuture; 6 December 2018.

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