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Research paper | Open Access

Analysis of highway performance under mixed connected and regular vehicle environment

Zhao Zhang, Xianfeng (Terry) Yang( )
Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, USA
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Abstract

Purpose

This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment.

Design/methodology/approach

The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions.

Findings

The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.

Originality/value

This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Keywords

Connected vehicleMixed trafficHighway capacityPenetration rateVariable speed limit

References

 

Abdel-Aty, M., Dilmore, J. and Hsia, L. (2006), “Applying variable speed limits and the potential for crash migration”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 1953 No. 1, pp. 21-30.
Crossref Google Scholar
 

Abdel-Aty, M., Cunningham, R.J., Gayah, V.V. and Hsia, L. (2008), “Dynamic variable speed limit strategies for real-time crash risk reduction on freeways”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2078 No. 1, pp. 108-116.
Crossref Google Scholar
 

Abdulsattar, H., Siam, M.R.K. and Wang, H. (2020), “Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent-based approach”, IET Intelligent Transport Systems, Vol. 14 No. 9, pp. 1132-1141.
Crossref Google Scholar
 

Ahn, S., Cassidy, M.J. and Laval, J. (2004), “Verification of a simplified car-following theory”, Transportation Research Part B: Methodological, Vol. 38 No. 5, pp. 431-440.
Crossref Google Scholar
 

Bansal, P. and Kockelman, K.M. (2017), “Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies”, Transportation Research Part A: Policy and Practice, pp. 49-63.
Crossref Google Scholar
 

Carlson, R.C., Papamichail, I., Papageorgiou, M. and Messmer, A. (2010), “Optimal motorway traffic flow control involving variable speed limits and ramp metering”, Transportation Science, Vol. 44 No. 2, pp. 238-253.
Crossref Google Scholar
 

Chen, D., Ahn, S., Chitturi, M. and Noyce, D.A. (2017), “Towards vehicle automation: roadway capacity formulation for traffic mixed with regular and automated vehicles”, Transportation Research Part B: Methodological, Vol. 100, pp. 196-221.
Crossref Google Scholar
 

Chen, Z., He, F., Zhang, L. and Yin, Y. (2016), “Optimal deployment of autonomous vehicle lanes with endogenous market penetration”, Transportation Research Part C: emerging Technologies, Vol. 72, pp. 143-156.
Crossref Google Scholar
 

Ghiasi, A., Hussain, O., Qian, Z.S. and Li, X. (2017), “A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method”, Transportation Research Part B: Methodological, Vol. 106, pp. 266-292.
Crossref Google Scholar
 

Han, Y., Chen, D. and Ahn, S. (2017), “Variable speed limit control at fixed freeway bottlenecks using connected vehicles”, Transportation Research Part B: Methodological, Vol. 98, pp. 113-134.
Crossref Google Scholar
 

Hegyi, A., De Schutter, B. and Hellendoorn, J. (2005), “Optimal coordination of variable speed limits to suppress shock waves”, IEEE Transactions on Intelligent Transportation Systems, Vol. 6 No No. 1, pp. 102-112.
Crossref Google Scholar
 

Hu, J., Bhowmick, P., Arvin, F., Lanzon, A. and Lennox, B. (2020), “Cooperative control of heterogeneous connected vehicle platoons: an adaptive leader-following approach”, IEEE Robotics and Automation Letters, Vol. 5 No. 2, pp. 977-984.
Crossref Google Scholar
 

Hao, W. Liu, L. Yang, X. Li, Y. and Byon, Y.J. (2020), “Reducing CACC platoon disturbances caused by state jitters by combining two stages driving state recognition with multiple platoons' strategies and risk prediction”, IEEE Transactions on Intelligent Transportation Systems.
Google Scholar
 

Huang, K., Yang, X., Lu, Y., Mi, C.C. and Kondlapudi, P. (2018), “Ecological driving system for connected/automated vehicles using a two-stage control hierarchy”, IEEE Transactions on Intelligent Transportation Systems, Vol. 19 No. 7, pp. 2373-2384.
Crossref Google Scholar
 

Jia, D. and Ngoduy, D. (2016), “Platoon based cooperative driving model with consideration of realistic inter-vehicle communication”, Transportation Research Part C: Emerging Technologies, Vol. 68, pp. 245-264.
Crossref Google Scholar
 

Kang, K.P., Chang, G.L. and Zou, N. (2004), “Optimal dynamic speed-limit control for highway work zone operations”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 1877 No. 1, pp. 77-84.
Crossref Google Scholar
 

Khondaker, B. and Kattan, L. (2015), “Variable speed limit: a microscopic analysis in a connected vehicle environment”, Transportation Research Part C: Emerging Technologies, Vol. 58, pp. 146-159.
Crossref Google Scholar
 
Kwon, E., Brannan, D., Shouman, K., Isackson, C. and Arseneau, B. (2007), “Development and field evaluation of variable advisory speed limit system for work zones”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2015Vol. 2015 No. 1, pp. 12-18.
Crossref
 

Liu, H., Wei, H., Zuo, T., Li, Z. and Yang, Y.J. (2017), “Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment”, Transportation Research Part C: emerging Technologies, Vol. 76, pp. 132-149.
Crossref Google Scholar
 

Lyu, P., Lin, Y., Wang, L. and Yang, X. (2017), “Variable speed limit control for delay and crash reductions at freeway work zone area”, Journal of Transportation Engineering, Part A: Systems, Vol. 143 No. 12.
Crossref Google Scholar
 

Lavasani, M., Jin, X. and Du, Y. (2016), “Market penetration model for autonomous vehicles on the basis of earlier technology adoption experience”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2597 No. 1, pp. 67-74.
Crossref Google Scholar
 

Lu, N., Cheng, N., Zhang, N., Shen, X. and Mark, J.W. (2014), “Connected vehicles: solutions and challenges”, IEEE Internet of Things Journal, Vol. 1 No. 4, pp. 289-299.
Crossref Google Scholar
 
National Highway Traffic Safety Administration (NHTSA) (2016), “Vehicle-To-Vehicle communication technology for light vehicles”, Office of Regulatory Analysis and Evaluation National Center for Statistics and Analysis, No. 150.
 

Newell, G.F. (2002), “A simplified car-following theory: a lower order model”, Transportation Research Part B: Methodological, Vol. 36 No. 3, pp. 195-205.
Crossref Google Scholar
 

Papageorgiou, M., Blosseville, J.M. and Hadj-Salem, H. (1990), “Modelling and real-time control of traffic flow on the Southern part of boulevard peripherique in Paris: part Ⅰ: modelling”, Transportation Research Part A: General, Vol. 24 No. 5, pp. 345-359.
Crossref Google Scholar
 

Papageorgiou, M., Kosmatopoulos, E. and Papamichail, I. (2008), “Effects of variable speed limits on motorway traffic flow”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2047 No. 1, pp. 37-48.
Crossref Google Scholar
 

Rahman, M.S. and Abdel-Aty, M. (2018), “Longitudinal safety evaluation of connected vehicles’ platooning on expressways”, Accident Analysis & Prevention, Vol. 117, pp. 381-391.
Crossref Google Scholar
 

Steel, P., McGregor, R.V., Guebert, A.A. and McGuire, T.M. (2005), “Application of variable speed limits along the trans Canada highway in banff national park”, Annual Conference of the Transportation Association of Canada.
Google Scholar
 

Talebpour, A. and Mahmassani, H.S. (2016), “Influence of connected and autonomous vehicles on traffic flow stability and throughput”, Transportation Research Part C: Emerging Technologies, Vol. 71, pp. 143-163.
Crossref Google Scholar
 

Ulfarsson, G.F., Shankar, V.N. and Vu, P. (2005), “The effect of variable message and speed limit signs on mean speeds and speed deviations”, International Journal of Vehicle Information and Communication Systems, Vol. 1 Nos. 1/2, pp. 69-87.
Crossref Google Scholar
 

Wan, N., Vahidi, A. and Luckow, A. (2016), “Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic”, Transportation Research Part C: Emerging Technologies, Vol. 69, pp. 548-563.
Crossref Google Scholar
 

Wang, Y. and Papageorgiou, M. (2005), “Real-time freeway traffic state estimation based on extended kalman filter: a general approach”, Transportation Research Part B: Methodological, Vol. 39 No. 2, pp. 141-167.
Crossref Google Scholar
 

Wang, Q., Yang, X., Huang, Z. and Yuan, Y. (2020), “Multi-vehicle trajectory design during cooperative adaptive cruise control platoon formation”, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674 No. 4, pp. 30-41.
Crossref Google Scholar
 

Yang, H., Wang, Z. and Xie, K. (2017), “Impact of connected vehicles on mitigating secondary crash risk”, International Journal of Transportation Science and Technology, Vol. 6 No. 3, pp. 196-207.
Crossref Google Scholar
 

Yang, X.T. Huang, K. Zhang, Z. Zhang, Z.A. and Lin, F. (2020), “Eco-Driving system for connected automated vehicles: multi-Objective trajectory optimization”, IEEE Transactions on Intelligent Transportation Systems.
Google Scholar
 

Yang, X., Lu, Y. and Lin, Y. (2017), “Optimal variable speed limit control system for freeway work zone operations”, Journal of Computing in Civil Engineering, Vol. 31 No. 1.
Crossref Google Scholar
 
Zhang, Z. Yang, X. and X, L. (2018), “Freeway traffic optimal control with mixed connected vehicles and regular cars”.
Journal of Intelligent and Connected Vehicles
Volume 4 Issue 2,
September 2021
Pages 68-79
DOI: 10.1108/JICV-10-2020-0011
Cite this article:
Zhang Z, Yang X(. Analysis of highway performance under mixed connected and regular vehicle environment. Journal of Intelligent and Connected Vehicles, 2021, 4(2): 68-79. https://doi.org/10.1108/JICV-10-2020-0011

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Received: 29 October 2020
Revised: 20 May 2021
Accepted: 24 June 2021
Published: 31 July 2021
© 2021 Zhao Zhang and Xianfeng (Terry) Yang. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
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