AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.8 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Review Article | Open Access

A review of vehicle speed control strategies

Changxi Ma1,2Yuanping Li1( )Wei Meng3
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou 730070, China
Gansu Longyuan Information Technology Co., Ltd., Lanzhou 730070, China
Show Author Information

Abstract

Currently, traffic problems in urban road traffic environments remain severe, traffic pollution and congestion have not been effectively improved, and traffic accidents are still frequent. Traditional traffic signal control methods have little effect on these problems. With the continuous improvement of communication technology and network connections, vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic research. However, its generalization has shortcomings. Therefore, this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future research. In this paper, we summarize the existing research in four parts. First, we categorize existing research based on vehicle type. Second, the vehicle speed guidance problem is divided according to the problem research scene. Third, we summarize the existing literature regarding vehicle speed. Finally, we summarize the methods used for speed guidance. Through an analysis of the existing literature, it is concluded that there is a deficiency in the existing research, and suggestions for the future of vehicle speed guidance research are suggested.

References

[1]

Abu-Lebdeh, G., 2002. Integrated adaptive-signal dynamic-speed control of signalized arterials. J Transp Eng, 128, 447–451.

[2]

Ahn, K., Rakha, H., Trani, A., Van Aerde, M., 2002. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J Transp Eng, 128, 182–190.

[3]

Ahn, K., Rakha, H. A., 2013. Network-wide impacts of eco-routing strategies: A large-scale case study. Transp Res Part D Transp Environ, 25, 119–130.

[4]

Ahn, K., Rakha, H. A., Park, S., 2013. Ecodrive application. Transport Res Rec, 2341, 1–11.

[5]

Amirgholy, M., Nourinejad, M., Gao, H. O., 2020. Optimal traffic control at smart intersections: Automated network fundamental diagram. Transp Res Part B Methodol, 137, 2–18.

[6]

Asadi, B., Vahidi, A., 2011. Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Trans Contr Syst Technol, 19, 707–714.

[7]

Asadi, M., Fathy, M., Mahini, H., Rahmani, A. M., 2021. A systematic literature review of vehicle speed assistance in intelligent transportation system. IET Intell Transp Syst, 15, 973–986.

[8]

Ashrafur Rahman, S. M., Masjuki, H. H., Kalam, M. A., Abedin, M. J., Sanjid, A., Sajjad, H., 2013. Impact of idling on fuel consumption and exhaust emissions and available idle-reduction technologies for diesel vehicles–A review. Energy Convers Manag, 74, 171–182.

[9]

Autili, M., Chen, L., Englund, C., Pompilio, C., Tivoli, M., 2021. Cooperative intelligent transport systems: Choreography-based urban traffic coordination. IEEE Trans Intell Transp Syst, 22, 2088–2099.

[10]
Bakibillah, A. S. M., Kamal, M. A. S., Tan, C. P., 2020. Sustainable eco-driving strategy at signalized intersections from driving data. In: 2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 165–170.
[11]
Barbaresso, J., Cordahi, G., Garcia, D., Hill, C., Jendzejec, A., Wright, K., 2014. USDOT’s intelligent transportation systems (ITS) ITS strategic plan, 2015–2019. USA, Intelligent Transportation Systems Joint Program Office.
[12]
Barth, M., Mandava, S., Boriboonsomsin, K., Xia, H., 2011. Dynamic ECO-driving for arterial corridors. In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, 182–188.
[13]
Bashiri, M., Fleming, C. H., 2017. A platoon-based intersection management system for autonomous vehicles. In: 2017 IEEE Intelligent Vehicles Symposium (IV), 667–672.
[14]

Boland, A., Cherry, M. C., Dickson, R., Carden, J., 2020. Doing A systematic review: A student’s guide. Bpsicpr, 15, 119–120.

[15]

Chang, D. J., Morlok, E. K., 2005. Vehicle speed profiles to minimize work and fuel consumption. J Transp Eng, 131, 173–182.

[16]
Chen, H., Abu-Lebdeh, G., 2006. Assessment of capacity and flow improvements of combined dynamic signal control and dynamic speed limits in signalized networks. In: Proceedings of the 5th International Symposium on Highway Capacity and Quality of Service, 669–678.
[17]

Chen, P., Yan, C., Sun, J., Wang, Y., Chen, S., Li, K., 2018. Dynamic eco-driving speed guidance at signalized intersections: Multivehicle driving simulator based experimental study. J Adv Transp, 2018, 6031764.

[18]
Chen, S., Sun, J., Yao, J., 2011. Development and simulation application of a dynamic speed dynamic signal strategy for arterial traffic management. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 1349–1354.
[19]

Chen, W., Liu, Y., Yang, X., Bai, Y., Gao, Y., Li, P., 2015. Platoon-based speed control algorithm for ecodriving at signalized intersection. Transport Res Rec, 2489, 29–38.

[20]

Daganzo, C. F., Pilachowski, J., 2011. Reducing bunching with bus-to-bus cooperation. Transp Res Part B Methodol, 45, 267–277.

[21]

De Mello, R., Chiodi, R. D., 2018. A safe speed guidance model for highways. Int J Inj Contr Saf Promot, 25, 408–415.

[22]
De Nunzio, G., Canudas de Wit, C., Moulin, P., Di Domenico, D., 2013. Eco-driving in urban traffic networks using traffic signal information. In: 52nd IEEE Conference on Decision and Control, 892–898.
[23]

Deng, Y. J., Liu, X. H., Hu, X., Zhang, M., 2020. Reduce bus bunching with a real-time speed control algorithm considering heterogeneous roadway conditions and intersection delays. J Transp Eng Part A Syst, 146, 04020048.

[24]
EEA, 2011. Do lower speed limits on motorways reduce fuel consumption and pollutant emissions? https://www.scribd.com/document/202592644/Do-lower-speed-limits-on-motorways-reduce-fuel-consumption-and-pollutant-emissions-European-Environment-Agency-EEA
[25]

El-Shawarby, I., Ahn, K., Rakha, H., 2005. Comparative field evaluation of vehicle cruise speed and acceleration level impacts on hot stabilized emissions. Transp Res Part D Transp Environ, 10, 13–30.

[26]
EPA, 2016. Inventory of U.S. Greenhouse Gas Emissions and Sinks. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks
[27]

Ericsson, E., 2001. Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transp Res Part D Transp Environ, 6, 325–345.

[28]

Feng, Y., Yu, C., Liu, H. X., 2018. Spatiotemporal intersection control in a connected and automated vehicle environment. Transp Res Part C Emerg Technol, 89, 364–383.

[29]

Furth, P. G., Muller, T. H. J., 2000. Conditional bus priority at signalized intersections: Better service with less traffic disruption. Transport Res Rec, 1731, 23–30.

[30]

Galanis, I., Anagnostopoulos, I., Gurunathan, P., Burkard, D., 2019. Environmental-based speed recommendation for future smart cars. Future Internet, 11, 78.

[31]

Gallen, R., Hautière, N., Cord, A., Glaser, S., 2013. Supporting drivers in keeping safe speed in adverse weather conditions by mitigating the risk level. IEEE Trans Intell Transp Syst, 14, 1558–1571.

[32]

Girianna, M., Benekohal, R. F., 2004. Using genetic algorithms to design signal coordination for oversaturated networks. J Intell Transp Syst, 8, 117–129.

[33]

Guanetti, J., Kim, Y., Borrelli, F., 2018. Control of connected and automated vehicles: State of the art and future challenges. Annu Rev Contr, 45, 18–40.

[34]

Guler, S., Menendez, M., Meier, L., 2014. Using connected vehicle technology to improve the efficiency of intersections. Transp Res Part C Emerg Technol, 46, 121–131.

[35]

Guo, Y., Ma, J., 2021. DRL-TP3: A learning and control framework for signalized intersections with mixed connected automated traffic. Transp Res Part C Emerg Technol, 132, 103416.

[36]

Hao, M., Bie, Y., Zhang, L., Mao, C., 2020. Improving schedule adherence based on dynamic signal control and speed guidance in connected bus system. J Intell Connect Veh, 3, 79–88.

[37]

Head, L., Gettman, D., Wei, Z., 2006. Decision model for priority control of traffic signals. Transportation Research Record, 1978, 169–177.

[38]

Jiang, Z., Yu, D., Luan, S., Zhou, H., Meng, F., 2022. Integrating traffic signal optimization with vehicle microscopic control to reduce energy consumption in a connected and automated vehicles environment. J Clean Prod, 371, 133694.

[39]

Jiménez, F., Cabrera-Montiel, W., 2014. System for road vehicle energy optimization using real time road and traffic information. Energies, 7, 3576–3598.

[40]

Kamal, M. A. S., Mukai, M., Murata, J., Kawabe, T., 2013. Model predictive control of vehicles on urban roads for improved fuel economy. IEEE Trans Contr Syst Technol, 21, 831–841.

[41]
Kamal, M. A. S., Taguchi, S., Yoshimura, T., 2015. Intersection vehicle cooperative eco-driving in the context of partially connected vehicle environment. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 1261–1266.
[42]

Kitchenham, B. Charters, S.M., 2007. Guidelines for performing systematic literature reviews in software engineering. Engineering, 45, 1051.

[43]

Lak, H. J., Gholamhosseinian, A., Seitz, J., 2022. Distributed vehicular communication protocols for autonomous intersection management. Procedia Comput Sci, 201, 150–157.

[44]

Lam, A. Y. S., Łazarz, B., Peruń, G., 2022. Smart energy and intelligent transportation systems. Energies, 15, 2900.

[45]

Leard, B., Linn, J., Munnings, C., 2019. Explaining the evolution of passenger vehicle Miles traveled in the United States. Energy J, 40, 25–54.

[46]
Lee, T., Son, J., 2011. Relationships between driving style and fuel consumption in highway driving. In: 16th Asia Pacific Automotive Engineering Conference, 6.
[47]

Li, Y., Ma, C., 2023. Short-time bus route passenger flow prediction based on a secondary decomposition integration method. J Transp Eng Part A Syst, 149, 04022132.

[48]

Li, Z., Elefteriadou, L., Ranka, S., 2014. Signal control optimization for automated vehicles at isolated signalized intersections. Transp Res Part C Emerg Technol, 49, 1–18.

[49]

Liu, H., Flores, C. E., Spring, J., Shladover, S. E., Lu, X. Y., 2022. Field assessment of intersection performance enhanced by traffic signal optimization and vehicle trajectory planning. IEEE Trans Intell Transp Syst, 23, 11549–11561.

[50]

Ma, J., Li, X., Shladover, S., Rakha, H. A., Lu, X. Y., Jagannathan, R., et al., 2016. Freeway speed harmonization. IEEE Trans Intell Veh, 1, 78–89.

[51]
Mahmud, M., 2014. Evaluation of truck signal priority at N Columbia blvd and martin Luther king jr. blvd intersection. Ph.D. Dissertation. Portland, USA: Portland State University.
[52]
Malakorn, K. J., Park, B., 2010. Assessment of mobility, energy, and environment impacts of IntelliDrive-based cooperative adaptive cruise control and intelligent traffic signal control. In: Proceedings of the 2010 IEEE International Symposium on Sustainable Systems and Technology, 1–6.
[53]

Malandraki, G., Papamichail, I., Papageorgiou, M., Dinopoulou, V., 2015. Simulation and evaluation of a public transport priority methodology. Transp Res Procedia, 6, 402–410.

[54]

Mintsis, E., Vlahogianni, E. I., Mitsakis, E., 2020. Dynamic eco-driving near signalized intersections: Systematic review and future research directions. J Transp Eng Part A Syst, 146, 04020018.

[55]

Müller, E. R., Carlson, R. C., Junior, W. K., 2016. Intersection control for automated vehicles with MILP. IFAC-PapersOnLine, 49, 37–42.

[56]

Muñoz-Organero, M., Magaña, V. C., 2013. Validating the impact on reducing fuel consumption by using an EcoDriving assistant based on traffic sign detection and optimal deceleration patterns. IEEE Trans Intell Transp Syst, 14, 1023–1028.

[57]

Niroumand, R., Tajalli, M., Hajibabai, L., Hajbabaie, A., 2020. Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream. Transp Res Part C Emerg Technol, 116, 102659.

[58]
Odekunle, A., Gao, W., Anayor, C., Wang, X., Chen, Y., 2018. Predictive cruise control of connected and autonomous vehicles: An adaptive dynamic programming approach. In: SoutheastCon, 1–6.
[59]

Olivares-Mendez, M. A., Sanchez-Lopez, J. L., Jimenez, F., Campoy, P., Sajadi-Alamdari, S. A., Voos, H., 2016. Vision-based steering control, speed assistance and localization for inner-city vehicles. Sensors, 16, 362.

[60]

Park, H., Oh, C., 2019. A vehicle speed harmonization strategy for minimizing inter-vehicle crash risks. Accid Anal Prev, 128, 230–239.

[61]
Rafter, C. B., Anvari, B., Box, S., 2017. Traffic responsive intersection control algorithm using GPS data. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 1–6.
[62]
Rakha, H., Kamalanathsharma, R. K., 2011. Eco-driving at signalized intersections using V2I communication. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 341–346.
[63]

Sciarretta, A., De Nunzio, G., Ojeda, L. L., 2015. Optimal ecodriving control: Energy-efficient driving of road vehicles as an optimal control problem. IEEE Contr Syst Mag, 35, 71–90.

[64]
Servin, O., Boriboonsomsin, K., Barth, M., 2006. An energy and emissions impact evaluation of intelligent speed adaptation. In: 2006 IEEE Intelligent Transportation Systems Conference, 1257–1262.
[65]
Sivak, M., Schoettle, B., 2012. Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy. Transp Policy, 22, 96–99.
[66]
Sun, J., Liu, H. X., 2015. Stochastic eco-routing in a signalized traffic network. Transp Res Part C Emerg Technol, 59, 32–47.
[67]

Sun, P., Nam, D., Jayakrishnan, R., Jin, W., 2022. An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections. Transp Res Part C Emerg Technol, 144, 103876.

[68]
Sun J., Liu H. X., 2015. Stochastic eco-routing in a signalized traffic network. Transp Res Procedia, 7, 110–128.
[69]
USDoE, 2018. Driving more efficiently. https://www.energy.gov/energysaver/driving-more-efficiently
[70]

Wang, A., Ge, Y., Tan, J., Fu, M., Shah, A. N., Ding, Y., et al., 2011. On-road pollutant emission and fuel consumption characteristics of buses in Beijing. J Environ Sci, 23, 419–426.

[71]

Wang, H., Fu, L., Zhou, Y., Li, H., 2008. Modelling of the fuel consumption for passenger cars regarding driving characteristics. Transp Res Part D Transp Environ, 13, 479–482.

[72]

Wang, J., Rakha, H. A., 2016. Fuel consumption model for conventional diesel buses. Appl Energy, 170, 394–402.

[73]

Wang, M., Daamen, W., Hoogendoorn, S. P., van Arem, B., 2014. Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control. Transp Res Part C Emerg Technol, 40, 290–311.

[74]

Wang, Q., Gong, Y., Yang, X., 2022. Connected automated vehicle trajectory optimization along signalized arterial: A decentralized approach under mixed traffic environment. Transp Res Part C Emerg Technol, 145, 103918.

[75]

Wang, W., Zhang, Y., Gao, J., Jiang, Y., Yang, Y., Zheng, Z., et al., 2023. GOPS: A general optimal control problem solver for autonomous driving and industrial control applications. Commun Transport Res, 3, 100096.

[76]

Wu, J., Qu, X., 2022. Intersection control with connected and automated vehicles: A review. J Intell Connect Veh, 5, 260–269.

[77]

Xia, H., Boriboonsomsin, K., Barth, M., 2013. Dynamic eco-driving for signalized arterial corridors and its indirect network-wide energy/emissions benefits. J Intell Transp Syst, 17, 31–41.

[78]

Xue, Y., Zhong, M., Xue, L., Tu, H., Tan, C., Kong, Q., et al., 2022. A real-time control strategy for bus operation to alleviate bus bunching. Sustainability, 14, 7870.

[79]

Yang, H., Almutairi, F., Rakha, H., 2021. Eco-driving at signalized intersections: A multiple signal optimization approach. IEEE Trans Intell Transp Syst, 22, 2943–2955.

[80]

Yang, K., Guler, S. I., Menendez, M., 2016. Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles. Transp Res Part C Emerg Technol, 72, 109–129.

[81]
Yang, Y., Chen, S., Sun, J., 2010. Modeling and evaluation of speed guidance strategy in VII system. In: 13th International IEEE Conference on Intelligent Transportation Systems, 1045–1050.
[82]

Yao, H., Li, X., 2020. Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection. Transp Res Part C Emerg Technol, 121, 102846.

[83]
Ye, B. L., Wu, W., Zhou, X., Mao, W., Huang, Y. S., 2014. A green wave band based method for urban arterial signal control. In: Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, 126–131.
[84]

Yu, C., Feng, Y., Liu, H. X., Ma, W., Yang, X., 2018. Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transp Res Part B Methodol, 112, 89–112.

[85]

Zhang, H., Cui, H., Shi, B., 2019. A data-driven analysis for operational vehicle performance of public transport network. IEEE Access, 7, 96404–96413.

[86]

Zhang, R., Yao, E., 2015. Eco-driving at signalised intersections for electric vehicles. IET Intell Transp Syst, 9, 488–497.

[87]
Zheng, S., Xu, J., 2011. Research on red wave and green wave coordinated control model in arterial road for different traffic demands. In: 2011 International Conference on Multimedia Technology, 1661–1664.
[88]

Zhou, F., Li, X., Ma, J., 2017. Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: Theoretical analysis with generalized time geography. Transp Res Part B Methodol, 95, 394–420.

Journal of Intelligent and Connected Vehicles
Pages 190-201
Cite this article:
Ma C, Li Y, Meng W. A review of vehicle speed control strategies. Journal of Intelligent and Connected Vehicles, 2023, 6(4): 190-201. https://doi.org/10.26599/JICV.2023.9210010

453

Views

42

Downloads

2

Crossref

2

Scopus

Altmetrics

Received: 30 April 2023
Revised: 11 May 2023
Accepted: 29 May 2023
Published: 30 December 2023
© The author(s) 2023.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return