ABSTRACT
In modern society, vehicles have become an indispensable means of transportation to ensure people's travel and the circulation of social production materials and living materials. However, while bringing us convenience in life, with the increasing number of vehicles, the corresponding energy consumption and exhaust emission problems have also caused a lot of social wealth loss. Therefore, how to effectively improve the energy efficiency of vehicles to achieve the goal of energy-saving and emission reduction is one of the focuses of current academic and industrial circles. Different from the industrial sector, which mainly achieves energy saving and emission reduction by improving the mechanical performance of vehicles [such as increasing the thermal efficiency of internal combustion engines (ICEs)] or introducing new energy vehicles (such as electric vehicles), we have more choices in the academic world. Among them, through effective traffic signal control, the energy consumption of the vehicle can be improved by achieving a uniform speed of the vehicle as much as possible. We believe that the advantage of this method is that it can improve the energy efficiency of the vehicle within the system without updating the vehicle. In this article, we will prove this assertion and compare some state-of-the-art approaches through the form of an empirical study.
- M. N. Azadani and A. Boukerche. Performance evaluation of driving behavior identification models through can-bus data. In Proc. IEEE WCNC, pages 1--6, 2020.Google Scholar
Digital Library
- M. Bani Younes and A. Boukerche. Intelligent traffic light controlling algorithms using vehicular networks. IEEE Trans. Veh. Technol., 65(8):5887--5899, 2016.Google Scholar
Cross Ref
- T. Begin, A. Busson, I. Guérin Lassous, and A. Boukerche. Video on demand in ieee 802.11p-based vehicular networks: Analysis and dimensioning. In Proc. ACM MSWiM, pages 303--310, 2018. Google Scholar
Digital Library
- A. Boukerche, D. Zhong, and P. Sun. An efficient deep reinforcement learning-based fuel-economic traffic signal control scheme. IEEE Trans. Sustain. Comput., 2020. manuscript number: TSUSC-2020-05-0058.Google Scholar
- M. Di Felice, L. Bedogni, and L. Bononi. Dysco: A dynamic spectrum and contention control framework for enhanced broadcast communication in vehicular networks. In Proc. ACM MobiWac, pages 97--106, 2012. Google Scholar
Digital Library
- H. Ge, Y. Song, C. Wu, J. Ren, and G. Tan. Cooperative deep q-learning with q-value transfer for multi-intersection signal control. IEEE Access, 7:40797--40809, 2019.Google Scholar
Cross Ref
- W. Genders and S. Razavi. Using a deep reinforcement learning agent for traffic signal control, 2016.Google Scholar
- M. Gerla. Avanet services, autonomous vehicles and the mobile cloud. In Proc. ACM MSWiM, pages 1--1, 2015. Google Scholar
Digital Library
- S. Guberinic, G. Senborn, and B. Lazic. Optimal Traffic Control: Urban Intersections. CRC Press, 2007.Google Scholar
Cross Ref
- N. Hounsell, J. Landles, R. Bretherton, and K. Gardner. Intelligent systems for priority at traffic signals in london: the income project. In Proc. RTIC, pages 90--94, 1998.Google Scholar
Cross Ref
- M. Keller. Handbook of emission factors for road transport (hbefa) 3.1. Technical report, INFRAS, 2010.Google Scholar
- X. Liang, X. Du, G. Wang, and Z. Han. A deep reinforcement learning network for traffic light cycle control. IEEE Trans. Veh. Technol., 68(2):1243--1253, 2019.Google Scholar
Cross Ref
- X. Liang, X. Du, G. Wang, and Z. Han. A deep reinforcement learning network for traffic light cycle control. IEEE Trans. Veh. Technol., 68(2):1243--1253, 2019.Google Scholar
Cross Ref
- Y. Lu, X. Xu, C. Ding, and G. Lu. A speed control method at successive signalized intersections under connected vehicles environment. IEEE Intell. Transp. Syst. Mag., 11(3):117--128, 2019.Google Scholar
Cross Ref
- G. F. Newell. Theory of highway traffic signals. Research Report, Institute of Transportation Studies, UCB-ITS-RR-89--7, 1989.Google Scholar
- K. Pandit, D. Ghosal, H. M. Zhang, and C.-N. Chuah. Adaptive traffic signal control with vehicular ad hoc networks. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 62(4):1459--1471, May 2013.Google Scholar
Cross Ref
- C. Priemer and B. Friedrich. A decentralized adaptive traffic signal control using v2i communication data. In Proc. ITSC, pages 1--6, 2009.Google Scholar
Cross Ref
- C. Rezende, R. W. Pazzi, and A. Boukerche. A reactive solution with a redundancy-based error correction mechanism for video dissemination over vehicular ad hoc networks. In Proc. ACM MSWiM, pages 343--352, 2012. Google Scholar
Digital Library
- SAE International. Engine researchers: 50% gasoline-engine efficiency in sight. [Online]. Available: https://www.sae.org/news/2019/04/high-efficiency-ic-engines-symposium-2019-delphi-gdci-engine, April 2019. Accessed on: Oct., 2020.Google Scholar
- A. M. d. Souza, A. Boukerche, G. Maia, R. I. Meneguette, A. A. Loureiro, and L. A. Villas. Decreasing greenhouse emissions through an intelligent traffic information system based on inter-vehicle communication. In Proc. ACM MobiWac, pages 91--98, 2014. Google Scholar
Digital Library
- P. Sun, N. Aljeri, and A. Boukerche. A fast vehicular traffic flow prediction scheme based on fourier and wavelet analysis. In Proc. Globecom, pages 1--6, 2018.Google Scholar
Digital Library
- P. Sun, N. Aljeri, and A. Boukerche. Machine learning-based models for real-time traffic flow prediction in vehicular networks. IEEE Netw., 34(3):178--185, 2020.Google Scholar
Cross Ref
- P. Sun and A. Boukerche. Challenges of designing computer vision-based pedestrian detector for supporting autonomous driving. In Proc. IEEE MASS, pages 28--36, 2019.Google Scholar
Cross Ref
- P. Sun and A. Boukerche. Ai-assisted data dissemination methods for supporting intelligent transportation systems. Internet Technol. Lett., page e169, 2020. Early access, DOI: https://doi.org/10.1002/itl2.169.Google Scholar
- P. Sun and A. Boukerche. A novel internet-of-vehicles assisted collaborative low-visible pedestrian detection approach. In Proc. IEEE Globecom, 2020. accepted.Google Scholar
- P. Sun and N. Samaan. A novel vanet-assisted traffic control for supporting vehicular cloud computing. IEEE Trans. Intell. Transp. Syst., pages 1--11, 2020.Google Scholar
Cross Ref
- R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA, 2018. Google Scholar
Digital Library
- T.-Q. Tang, Z.-Y. Yi, J. Zhang, T. Wang, and J.-Q. Leng. A speed guidance strategy for multiple signalized intersections based on car-following model. Physica A Stat. Mech. Appl., 496:399--409, 2018.Google Scholar
Cross Ref
- The European Environment Agency. Electric vehicles as a proportion of the total fleet. [Online]. Available: https://www.eea.europa.eu/data-and-maps/indicators/proportion-of-vehicle-fleet-meeting-4/assessment-4, Dec. 2019. Accessed on: Oct., 2020.Google Scholar
- Toronto Transportation Services. King st. transit pilot - detailed traffic & pedestrian volumes. [Online]. Available: https://ckan0.cf.opendata.inter.prod-toronto.ca/dataset/5a4e2ef7--8eab-45da-9080--9956a8605229/resource/a5efb524-f062--48e9--84a0--589b12f0754b/download/detailed-traffic-pedestrian-volumes-2018.gz, 2018. Accessed on: May, 2020.Google Scholar
- U.S. Department of Energy. Where the energy goes: Gasoline vehicles. [Online]. Available: https://www.fueleconomy.gov/feg/atv.shtml#data-sources. Accessed on: Oct., 2020.Google Scholar
- U.S. Department of Transportation. Congestion pricing - a primer: Overview. [Online]. Available: https://ops.fhwa.dot.gov/publications/fhwahop08039/cp_prim1_02.htm, Feb. 2017. Accessed on: May, 2019.Google Scholar
- E. Van der Pol and F. A. Oliehoek. Coordinated deep reinforcement learners for traffic light control. In NIPS MALIC Workshop, 2016.Google Scholar
- H. Wei, G. Zheng, H. Yao, and Z. Li. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proc. KDD, pages 2496--2505. ACM, 2018. Google Scholar
Digital Library
- H. Wei, G. Zheng, H. Yao, and Z. Li. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proc. ACM SIGKDD, pages 2496--2505, 2018. Google Scholar
Digital Library
- M. B. Younes and A. Boukerche. A performance evaluation of an efficient traffic congestion detection protocol (ecode) for intelligent transportation systems. Ad Hoc Netw., 24:317--336, 2015. Google Scholar
Digital Library
- D. Zhong and A. Boukerche. Traffic signal control using deep reinforcement learning with multiple resources of rewards. In Proc. ACM PE-WASUN, pages 23--28, 2019. Google Scholar
Digital Library
Index Terms
Empirical Study and Analysis of the Impact of Traffic Flow Control at Road Intersections on Vehicle Energy Consumption
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