Improving Urban Mobility: Analyzing Impacts of Connected and Automated Vehicles on Traffic and Emissions

Authors: Saad Roustom, Hajo Ribberink

Abstract:

In most cities in the world, traffic has increased strongly over the last decades, causing high levels of congestion and deteriorating inner-city air quality. This study analyzes the impact of connected and automated vehicles (CAVs) on traffic performance and greenhouse gas (GHG) emissions under different CAV penetration rates in mixed fleet environments of CAVs and driver-operated vehicles (DOVs) and under three different traffic demand levels. Utilizing meso-scale traffic simulations of the City of Ottawa, Canada, the research evaluates the traffic performance of three distinct CAV driving behaviors—Cautious, Normal, and Aggressive—at penetration rates of 25%, 50%, 75%, and 100%, across three different traffic demand levels. The study employs advanced correlation models to estimate GHG emissions. The results reveal that Aggressive and Normal CAVs generally reduce traffic congestion and GHG emissions, with their benefits being more pronounced at higher penetration rates (50% to 100%) and elevated traffic demand levels. On the other hand, Cautious CAVs exhibit an increase in both traffic congestion and GHG emissions. However, results also show deteriorated traffic flow conditions when introducing 25% penetration rates of any type of CAVs. Aggressive CAVs outperform all other driving at improving traffic flow conditions and reducing GHG emissions. The findings of this study highlight the crucial role CAVs can play in enhancing urban traffic performance and mitigating the adverse impact of transportation on the environment. This research advocates for the adoption of effective CAV-related policies by regulatory bodies to optimize traffic flow and reduce GHG emissions. By providing insights into the impact of CAVs, this study aims to inform strategic decision-making and stimulate the development of sustainable urban mobility solutions.

Keywords: Connected and automated vehicles, congestion, GHG emissions, mixed fleet environment, traffic performance, traffic simulations.

References:

[1] Atkins Ltd., "Research on the Impacts of Connected and Autonomous Vehicles (CAVs) on Traffic Flow Summary Report Department for Transport," (Online). Available: https://trid.trb.org/view/1448450, 2016.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
[2] Q. Lu, T. Tettamanti, D. Hörcher, and I. Varga, "The impact of autonomous vehicles on urban traffic network capacity: an experimental analysis by microscopic traffic simulation," Transportation Letters, vol. 12, no. 8, pp. 540-549, 2020. (Online). Available: https://doi.org/10.1080/19427867.2019.1662561
[3] F. Bohm and K. Häger, "Introduction of Autonomous Vehicles in the Swedish Traffic System : Effects and Changes Due to the New Self-Driving Car Technology," 2015. (Online). Available: https://www.semanticscholar.org/paper/Introduction-of-Autonomous-Vehicles-in-the-Swedish-Bohm-H%C3%A4ger/ea2be6805b2adaba043df516e132f1289ce103cb#citing-papers
[4] H. Y. Wang and L. Wang, "Autonomous vehicles’ performance on single lane road: A simulation under VISSIM environment," in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp. 1-5. DOI: 10.1109/CISP-BMEI.2017.8302162.
[5] Z. B. Biramo and A. A. Mekonnen, "Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track," Environmental Systems Research, vol. 11, no. 1, p. 28, 2022. (Online). Available: https://doi.org/10.1186/s40068-022-00276-2
[6] R. F. Tomás, P. Fernandes, E. Macedo, J. M. Bandeira, and M. C. Coelho, "Assessing the emission impacts of autonomous vehicles on metropolitan freeways," Transportation Research Procedia, vol. 47, pp. 617-624, 2020. (Online). Available: https://doi.org/https://doi.org/10.1016/j.trpro.2020.03.139
[7] C. Stogios, D. Kasraian, M. J. Roorda, and M. Hatzopoulou, "Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions," Transportation Research Part D: Transport and Environment, vol. 76, pp. 176-192, 2019. (Online). Available: https://doi.org/10.1016/j.trd.2019.09.020
[8] J. Conlon and J. Lin, "Greenhouse Gas Emission Impact of Autonomous Vehicle Introduction in an Urban Network," Transportation Research Record, vol. 2673, no. 5, pp. 142-152, 2019. (Online). Available: https://doi.org/10.1177/0361198119839970
[9] Roustom, S. and Ribberink, H., "Optimizing CAV Driving Behaviour to Reduce Traffic Congestion and GHG Emissions," in Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), SciTePress, pp. 198-205, 2023. DOI: 10.5220/0011792500003479.
[10] A. Brown, J. Gonder, and B. Repac, "An Analysis of Possible Energy Impacts of Automated Vehicles," in Road Vehicle Automation, G. Meyer and S. Beiker, Eds. Springer International Publishing, 2014, pp. 137-153. (Online). Available: https://doi.org/10.1007/978-3-319-05990-7_13
[11] Z. Wadud, D. MacKenzie, and P. Leiby, "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, vol. 86, pp. 1-18, 2016. (Online). Available: https://doi.org/10.1016/j.tra.2015.12.001
[12] MMM Group Limited, "TRANS Model – Evolution of the TRANS Regional Travel Forecasting Model," 2014.
[13] Bentley. (n.d.). Dynameq. Available: https://www.bentley.com/software/dynameq/
[14] S. G. Roustom, "Environmental Impacts of Connected and Automated Vehicles Considering Traffic Flow and Road Characteristics," Thesis (M.App.Sc.), Carleton University, 2022.
[15] Bentley. (n.d.). Emme. Available: https://www.bentley.com/software/emme/