Abstract
In the member countries of the Organization for Economic Co-operation and Development (OECD), projections show that the improved energy efficiency in transportation may lead to a net decline of about 2% in energy use until 2040, thus outpacing the predicted increase of vehicle-miles traveled (VMT). However, in OECD-Europe, transportation still represents the biggest source of carbon emissions, contributing by about 25% to the total CO2 emissions, with cars and vans representing more than two-thirds of this share. The situation is even more alarming in non-OECD countries, where the transportation energy demand is expected to rise by 64% until 2040. The shift that we are witnessing toward the adoption of connected and automated vehicles (CAVs) is going to be perhaps the most disruptive since the early days of automobiles and could revolutionize movement of people and goods. This level of connectivity and autonomy will transform transportation in several dimensions with important societal and economic impacts: improved safety, increased comfort, time saving potential, and more efficient road utilization are among the most widely discussed positive impacts of CAVs. However, the potential energy efficiency benefits of these technologies remain uncertain. From a single-vehicle efficiency perspective, research suggests that lightweight, low-speed, autonomous vehicles have the potential to achieve fuel economy an order of magnitude higher than current cars. Yet, at system-wide level, current estimates suggest that the total energy consumption impacts can range from a 90% decrease to a 200% increase in fuel consumption as compared to a projected 2050 baseline energy consumption. The paradigm that traffic congestion mitigation should reduce CO2 emissions is yet to be proved. Therefore, interest in transportation regulation problems with explicit environmental considerations is growing. This work takes a more in-depth look at increased opportunities for energy-efficient driving with energy-oriented traffic management and CAVs deployment. In particular, the focus will be put on the road traffic control strategies in urban networks using connectivity to enable variable speed limits and traffic light adaptive control, as well as the energy-saving opportunities that arise for individual CAVs by anticipating future road geometry, traffic conditions, and interactions with neighboring vehicles.
Similar content being viewed by others
References
Aarts L, Feddes G (2016) European truck platooning challenge. In: International symposium on heavy vehicle transport technology
Ahmane M, Abbas-Turki A, Perronnet F, Wu J, El Moudni A, Buisson J, Zeo R (2013) Modeling and controlling an isolated urban intersection based on cooperative vehicles. Transp Res Part C Emerg Technol 28:44–62
Al Alam A, Gattami A, Johansson KH (2010) An experimental study on the fuel reduction potential of heavy duty vehicle platooning. In: 13th international IEEE conference on intelligent transportation systems, IEEE, pp 306–311
An H, Jung J-i (2018) Design of a cooperative lane change protocol for a connected and automated vehicle based on an estimation of the communication delay. Sensors 18(10):3499
Andersen H, Shen X, Eng YH, Rus D, Ang MH Jr (2017) Connected cooperative control of autonomous vehicles during unexpected road situations. Mech Eng 139(12):S3–S7
Asadi B, Zhang C, Vahidi A (2010) The role of traffic flow preview for planning fuel optimal vehicle velocity. In: Dynamic systems and control conference, vol 44182, pp 813–819
Awal T, Murshed M, Ali M (2015) An efficient cooperative lane-changing algorithm for sensor-and communication-enabled automated vehicles. In: 2015 IEEE intelligent vehicles symposium (IV), IEEE, pp 1328–1333
Bhoopalam AK, Agatz N, Zuidwijk R (2018) Planning of truck platoons: a literature review and directions for future research. Transp Res B Methodol 107:212–228
Bodenheimer R, Brauer A, Eckhoff D, German R (2014) Enabling GLOSA for adaptive traffic lights. In: 2014 IEEE vehicular networking conference (VNC), IEEE, pp 167–174
Bonnet C, Fritz H (2000) Fuel consumption reduction in a platoon: experimental results with two electronically coupled trucks at close spacing. Technical report, SAE technical paper
Browand F, McArthur J, Radovich C (2004) Fuel saving achieved in the field test of two tandem trucks. UC Berkeley: California Partners for Advanced Transportation Technology. Retrieved from https://escholarship.org/uc/item/29v570mm
Brown A, Gonder J, Repac B (2014) An analysis of possible energy impacts of automated vehicles. In: Meyer G, Beiker S (eds) Road vehicle automation, Lecture notes in mobility. Springer, Cham, pp 137–153
Charalampidis AC, Gillet D (2014) Speed profile optimization for vehicles crossing an intersection under a safety constraint. In: 2014 European control conference (ECC), IEEE, pp 2894–2901
Continental (2019) Continental launches smart city mobility and transportation hub for safer and smarter cities. https://www.continental.com/en-us/press-/press-releases/smart-city-mobility-205048
Csiks A, Luspay T, Varga I (2011) Modeling and optimal control of travel times and traffic emission on freeways. IFAC Proc Vol 44(1):13058–13063. 18th IFAC world congress
De Nunzio G Gutman P-O (2017) An application of shock wave theory to urban traffic control via dynamic speed advisory. In: hEART 2017: 6th symposium of the European Association for Research in Transportation
De Nunzio G, Thibault L (2017) Energy-optimal driving range prediction for electric vehicles. In: 2017 IEEE intelligent vehicles symposium (IV), pp 1608–1613
De Nunzio G, Canudas-de-Wit C, Moulin P (2014) Urban traffic eco-driving: a macroscopic steady-state analysis. In: Control conference (ECC), 2014 European, IEEE, pp 2581–2587
De Nunzio G, Gomes G, Canudas-de-Wit C, Horowitz R, Moulin P (2015) Arterial bandwidth maximization via signal offsets and variable speed limits control. In: Decision and control (CDC), 2015 IEEE 54th annual conference on, IEEE, pp 5142–5148
De Nunzio G, Canudas-de-Wit C, Moulin P, Di Domenico D (2016) Eco-driving in urban traffic networks using traffic signals information. Int J Robust and Nonlinear Control 26(6):1307–1324
De Nunzio G, Gomes G, Canudas-de-Wit C, Horowitz R, Moulin P (2017) Speed advisory and signal offsets control for arterial bandwidth maximization and energy consumption reduction. IEEE Trans Control Syst Technol 25(3):875–887
De Nunzio G, Thibault L, Sciarretta A (2017a) Bi-objective eco-routing in large urban road networks. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), pp 1–7
De Nunzio G, Thibault L, Sciarretta A (2017b) Model-based eco-routing strategy for electric vehicles in large urban networks. In: Comprehensive energy management–eco routing & velocity profiles, Springer, pp 81–99
De Nunzio G, Sciarretta A, Ben Gharbia I, Ojeda LL (2018) A constrained eco-routing strategy for hybrid electric vehicles based on semi-analytical energy management. In: 2018 21st international conference on intelligent transportation systems (ITSC), pp 355–361
Dey KC, Yan L, Wang X, Wang Y, Shen H, Chowdhury M, Yu L, Qiu C, Soundararaj V (2015) A review of communication, driver characteristics, and controls aspects of cooperative adaptive cruise control (CACC). IEEE Trans Intell Transp Syst 17(2):491–509
Di Vaio M, Fiengo G, Petrillo A, Salvi A, Santini S, Tufo M (2019) Cooperative shock waves mitigation in mixed traffic flow environment. IEEE Trans Intell Transp Syst 20(12):4339–4353
Dinopoulou V, Diakaki C, Papageorgiou M (2006) Applications of the urban traffic control strategy TUC. Eur J Oper Res 175(3):1652–1665
Dollar A, Sciarretta A, Vahidi A (2020a) Information and collaboration levels in vehicular strings: a comparative study. In: IFAC proceedings volumes
Dollar A, Sciarretta A, Vahidi A (2020b) Multi-agent control of lane-switching automated vehicles for energy efficiency. In: 2020 American control conference (ACC), IEEE, pp 422–429
Du S, Razavi S (2019) Variable speed limit for freeway work zone with capacity drop using discrete-time sliding mode control. J Comput Civ Eng 33(2):04019001
Ericsson E, Larsson H, Brundell-Freij K (2006) Optimizing route choice for lowest fuel consumption-potential effects of a new driver support tool. Transp Res Part C Emerg Technol 14(6):369–383
Fayazi SA, Vahidi A (2018) Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing. IEEE Trans Intell Veh 3(3):287–299
Fayazi SA, Vahidi A, Luckow A (2017) Optimal scheduling of autonomous vehicle arrivals at intelligent intersections via MILP. In: 2017 American control conference (ACC), IEEE, pp 4920–4925
Fontaras G, Zacharof N-G, Ciuffo B (2017) Fuel consumption and CO2 emissions from passenger cars in Europe–laboratory versus real-world emissions. Prog Energy Combust Sci 60:97–131
Frejo JRD, Papamichail I, Papageorgiou M, De Schutter B (2019) Macroscopic modeling of variable speed limits on freeways. Transp Res Part C Emerg Technol 100:15–33
Ghiasi A, Li X, Ma J (2019) A mixed traffic speed harmonization model with connected autonomous vehicles. Transp Res Part C Emerg Technol 104:210–233
Giammarino V, Baldi S, Frasca P, Monache MLD (2020) Traffic flow on a ring with a single autonomous vehicle: an interconnected stability perspective. IEEE Trans Intell Transp Syst 1–11. https://ieeexplore.ieee.org/document/9072289
Gomes G (2015) Bandwidth maximization using vehicle arrival functions. IEEE Trans Intell Transp Syst 16(4):1977–1988
Goodall NJ, Smith BL, Park B (2013) Traffic signal control with connected vehicles. Transp Res Rec 2381(1):65–72
Guanetti J, Kim Y, Borrelli F (2018) Control of connected and automated vehicles: state of the art and future challenges. Annu Rev Control 45:18–40
Hadiuzzaman M, Qiu TZ (2013) Cell transmission model based variable speed limit control for freeways. Can J Civ Eng 40(1):46–56
Han J, Sciarretta A, Ojeda LL, De Nunzio G, Thibault L (2018) Safe-and eco-driving control for connected and automated electric vehicles using analytical state-constrained optimal solution. IEEE Trans Intell Veh 3(2):163–172
Han J, Vahidi A, Sciarretta A (2019) Fundamentals of energy efficient driving for combustion engine and electric vehicles: an optimal control perspective. Automatica 103:558–572
He Q, Head KL, Ding J (2012) Pamscod: platoon-based arterial multi-modal signal control with online data. Transp Res Part C Emerg Technol 20(1):164–184
Hegyi A, Hoogendoorn SP, Schreuder M, Stoelhorst H, Viti F (2008) Specialist: a dynamic speed limit control algorithm based on shock wave theory. In: 2008 11th international IEEE conference on intelligent transportation systems, pp 827–832
Heß D, Lattarulo R, Pérez J, Schindler J, Hesse T, Köster F (2018) Fast maneuver planning for cooperative automated vehicles. In: 2018 21st international conference on intelligent transportation systems (ITSC), IEEE, pp 1625–1632
Huang S, Sadek AW, Zhao Y (2012) Assessing the mobility and environmental benefits of reservation-based intelligent intersections using an integrated simulator. IEEE Trans Intell Transp Syst 13(3):1201–1214
Huang Y, Ng EC, Zhou JL, Surawski NC, Chan EF, Hong G (2018) Eco-driving technology for sustainable road transport: a review. Renew Sust Energ Rev 93:596–609
Hunt P, Robertson D, Bretherton R, Winton R (1981) SCOOT-a traffic responsive method of coordinating signals. Technical report
IEA (2017a) Energy consumption in transport in selected IEA countries. https://www.iea.org/data-and-statistics/charts/energy-consumption-in-transport-in-selected-iea-countries-2017
IEA (2017b) Largest end-uses of energy by sector in selected IEA countries. https://www.iea.org/data-and-statistics/charts/largest-end-uses-of-energy-by-sector-in-selected-iea-countries-2017
Jin Q, Wu G, Boriboonsomsin K, Barth M (2013) Platoon-based multi-agent intersection management for connected vehicle. In: 16th international IEEE conference on intelligent transportation systems (ITSC 2013), IEEE, pp 1462–1467
Jin Q, Wu G, Boriboonsomsin K, Barth MJ (2016) Power-based optimal longitudinal control for a connected eco-driving system. IEEE Trans Intell Transp Syst 17(10):2900–2910
Kamal MAS, Imura J-i, Hayakawa T, Ohata A, Aihara K (2015) Traffic signal control of a road network using MILP in the MPC framework. Int J Intell Transp Syst Res 13(2):107–118
Kamal MAS, Taguchi S, Yoshimura T (2016) Efficient driving on multilane roads under a connected vehicle environment. IEEE Trans Intell Transp Syst 17(9):2541–2551
Kataria P, Rani A (2019) Real-time traffic light management system with manual control. In: 2019 3rd international conference on recent developments in control, automation & power engineering (RDCAPE), IEEE, pp 419–424
Khamis MA, Gomaa W (2012) Enhanced multiagent multi-objective reinforcement learning for urban traffic light control. In: 2012 11th international conference on machine learning and applications, vol 1, IEEE, pp 586–591
Khondaker B, Kattan L (2015a) Variable speed limit: a microscopic analysis in a connected vehicle environment. Transp Res Part C Emerg Technol 58:146–159
Khondaker B, Kattan L (2015b) Variable speed limit: an overview. Transp Lett 7(5):264–278. Taylor & Francis
Kluge S, Santa C, Dangl S, Wild S, Brokate M, Reif K, Busch F (2013) On the computation of the energy-optimal route dependent on the traffic load in Ingolstadt. Transp Res Part C Emerg Technol 36:97–115
Kubička M, Klusáček J, Sciarretta A, Cela A, Mounier H, Thibault L, Niculescu S-I (2016) Performance of current eco-routing methods. In: Intelligent vehicles symposium (IV), 2016 IEEE, IEEE, pp 472–477
Kuo Y (2010) Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Comput Ind Eng 59(1):157–165
Larson J, Liang K-Y, Johansson KH (2014) A distributed framework for coordinated heavy-duty vehicle platooning. IEEE Trans Intell Transp Syst 16(1):419–429
Lee J, Kim J, Park J, Bae C (2013) Effect of the air-conditioning system on the fuel economy in a gasoline engine vehicle. Proc Inst Mech Eng D 227(1):66–77
Levin M, Chen R, Liao C-F, Zhang T (2019) Improving intersection safety through variable speed limits for connected vehicles (No. CTS 19-12) (Tech. Rep.). Roadway Safety Institute
Li X, Sun J-Q (2017) Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact. Physica A 467:41–58
Liang K-Y, Mårtensson J, Johansson KH (2013) When is it fuel efficient for a heavy duty vehicle to catch up with a platoon? IFAC Proc Vol 46(21):738–743
Liard T, Stern R, Delle Monache M-L (2020) Optimal driving strategies for traffic control with autonomous vehicles. In: The 21rst IFAC world congress
Lin D, Jabari SE (2020) Pay for intersection priority: a free market mechanism for connected vehicles. arXiv preprint arXiv:2001.01813
Lin D, Li L, Jabari SE (2019) Pay to change lanes: a cooperative lane-changing strategy for connected/automated driving. Transp Res Part C Emerg Technol 105:550–564
Lioris J, Pedarsani R, Tascikaraoglu FY, Varaiya P (2016) Doubling throughput in urban roads by platooning. IFAC-PapersOnLine 49(3):49–54
Litman T (2017) Autonomous vehicle implementation predictions. Victoria Transport Policy Institute, Victoria
Liu B, Ghosal D, Chuah C-N, Zhang HM (2011) Reducing greenhouse effects via fuel consumption-aware variable speed limit (FC-VSL). IEEE Trans Veh Technol 61(1):111–122
Liu P, Ozguner U, Zhang Y (2017a) Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations. Transp Res Part C Emerg Technol 77:80–95
Liu S, Hellendoorn H, De Schutter B (2017b) Model predictive control for freeway networks based on multi-class traffic flow and emission models. IEEE Trans Intell Transp Syst 18(2):306–320
Liu F, Zhao F, Liu Z, Hao H (2019a) Can autonomous vehicle reduce greenhouse gas emissions? A country-level evaluation. Energy Policy 132:462–473
Liu H, Lu X-Y, Shladover SE (2019b) Traffic signal control by leveraging cooperative adaptive cruise control (CACC) vehicle platooning capabilities. Transp Res Part C Emerg Technol 104:390–407
Liu H, Shladover SE, Lu X-Y, Kan X (2020) Freeway vehicle fuel efficiency improvement via cooperative adaptive cruise control. J Intell Transp Syst 0:1–13. https://www.tandfonline.com/doi/abs/10.1080/15472450.2020.1720673?journalCode=gits20
Lombard A, Perronnet F, Abbas-Turki A, El Moudni A (2017) On the cooperative automatic lane change: speed synchronization and automatic courtesy. In: Design, automation & test in Europe Conference & Exhibition (DATE), 2017, IEEE, pp 1655–1658
Lowrie P et al (1982) The Sydney co-ordinated adaptive traffic system (SCATS) – principles, methodology, algorithms. In: Proceedings of the international conference on road traffic signaling, London
Lu X-Y, Shladover SE (2011) Automated truck platoon control. Technical report, Institute of Transportation Studies, UC Berkeley
Marcu B, Browand F (1999) Aerodynamic forces experienced by a 3-vehicle platoon in a crosswind. Technical report, SAE technical paper
McAuliffe B, Croken M, Ahmadi-Baloutaki M, Raeesi A (2017) Fuel-economy testing of a three-vehicle truck platooning system. UC Berkeley. Retrieved from https://escholarship.org/uc/item/7g37w4fb
Miculescu D, Karaman S (2019) Polling-systems-based autonomous vehicle coordination in traffic intersections with no traffic signals. IEEE Trans Autom Control 65(2):680–694
Milanés V, Shladover SE, Spring J, Nowakowski C, Kawazoe H, Nakamura M (2013) Cooperative adaptive cruise control in real traffic situations. IEEE Trans Intell Transp Syst 15(1):296–305
Mirchandani P, Head L (2001) A real-time traffic signal control system: architecture, algorithms, and analysis. Transp Res Part C Emerg Technol 9(6):415–432
Namazi E, Li J, Lu C (2019) Intelligent intersection management systems considering autonomous vehicles: a systematic literature review. IEEE Access 7:91946–91965
Nannicini G, Delling D, Schultes D, Liberti L (2012) Bidirectional a* search on time-dependent road networks. Networks 59(2):240–251
Nie J, Zhang J, Ding W, Wan X, Chen X, Ran B (2016) Decentralized cooperative lane-changing decision-making for connected autonomous vehicles. IEEE Access 4:9413–9420
Noorvand H, Karnati G, Underwood BS (2017) Autonomous vehicles: assessment of the implications of truck positioning on flexible pavement performance and design. Transp Res Rec 2640(1):21–28
Nowakowski C, O’Connell J, Shladover SE, Cody D (2010) Cooperative adaptive cruise control: driver acceptance of following gap settings less than one second. In: Proceedings of the human factors and ergonomics society annual meeting, vol 54, pp 2033–2037, SAGE Publications, Los Angeles
Ojeda LL, Chasse A, Goussault R (2017a) Fuel consumption prediction for heavy-duty vehicles using digital maps. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), pp 1–7
Ojeda LL, Han J, Sciarretta A, De Nunzio G, Thibault L (2017b) A real-time eco-driving strategy for automated electric vehicles. In: 2017 IEEE 56th annual conference on decision and control (CDC), pp 2768–2774
Osorio C, Nanduri K (2015) Energy-efficient urban traffic management: a microscopic simulation-based approach. Transp Sci 49(3):637–651
Othman B, De Nunzio G, Di Domenico D, Canudas-de-Wit C (2019) Ecological traffic management: a review of the modeling and control strategies for improving environmental sustainability of road transportation. Annu Rev Control 48:292–311
Othman B, De Nunzio G, Di Domenico D, Canudas-de-Wit C (2020) Variable speed limits control in an urban road network to reduce environmental impact of traffic. In: 2020 annual American control conference (ACC), IEEE, pp 1179–1184
Papageorgiou M, Kotsialos A (2002) Freeway ramp metering: an overview. IEEE Trans Intell Transp Syst 3(4):271–281
Pasquale C, Papamichail I, Roncoli C, Sacone S, Siri S, Papageorgiou M (2015) Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control. Transp Res Part C Emerg Technol 55:85–99
Peloton technology (n.d.) https://peloton-tech.com/how-it-works. Accessed 14 Aug 2020
Pinto G, Oliver-Hoyo MT (2008) Using the relationship between vehicle fuel consumption and co2 emissions to illustrate chemical principles. J Chem Educ 85(2):218
Pulkrabek WW (2004) Engineering fundamentals of the internal combustion engine, 2nd Ed. Journal of Engineering for Gas Turbines and Power 126(1):198–198
Qi YG, Teng HH, Yu L (2004) Microscale emission models incorporating acceleration and deceleration. J Transp Eng 130(3):348–359
Qom SF, Xiao Y, Hadi M (2016) Evaluation of cooperative adaptive cruise control (CACC) vehicles on managed lanes utilizing macroscopic and mesoscopic simulation. In: Transportation research board 95th annual meeting, number 16-6384
Raboy K, Ma J, Stark J, Zhou F, Rush K, Leslie E (2017) Cooperative control for lane change maneuvers with connected automated vehicles: a field experiment. Technical report
Raboy K, Ma J, Leslie E, Zhou F (2020) A proof-of-concept field experiment on cooperative lane change maneuvers using a prototype connected automated vehicle testing platform. J Intell Transp Syst:1–16
Robinson M et al (2000) Examples of variable speed limit applications
Sciarretta A, Vahidi A (2020a) Energy saving potentials of CAVs. Springer International Publishing, Cham, pp 1–31
Sciarretta A, Vahidi A (2020b) Fundamentals of vehicle modeling. Springer International Publishing, Cham, pp 33–62
Sciarretta A, Nunzio GD, Ojeda LL (2015) Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst Mag 35(5):71–90
Shladover SE, Nowakowski C, Lu X-Y, Ferlis R (2015) Cooperative adaptive cruise control: definitions and operating concepts. Transp Res Rec 2489(1):145–152
Stern RE, Chen Y, Churchill M, Wu F, Monache MLD, Piccoli B, Seibold B, Sprinkle J, Work DB (2019) Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic. Transp Res Part D: Transp Environ 67:351–365
Stevanovic A, Stevanovic J, Zhang K, Batterman S (2009) Optimizing traffic control to reduce fuel consumption and vehicular emissions: integrated approach with VISSIM, CMEM, and VISGAOST. Transp Res Rec 2128(1):105–113
Tachet R, Santi P, Sobolevsky S, Reyes-Castro LI, Frazzoli E, Helbing D, Ratti C (2016) Revisiting street intersections using slot-based systems. PLoS One 11(3):e0149607
Tajali M, Hajbabaie A (2018). Dynamic speed harmonization in urban street networks. Comput-Aided Civ Inf Eng
Talavera E, Díaz-Álvarez A, Jiménez F, Naranjo JE (2018) Impact on congestion and fuel consumption of a cooperative adaptive cruise control system with lane-level position estimation. Energies 11(1):194
Thibault L, Nunzio GD, Sciarretta A (2018) A unified approach for electric vehicles range maximization via eco-routing, eco-driving, and energy consumption prediction. IEEE Trans Intell Veh 3:463–475
U.S. Department of Energy (2018) Autonomous vehicles: uncertainties and energy implications
U.S. Energy Information Administration (2017) International energy outlook
Van Arem B, Van Driel CJ, Visser R (2006) The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans Intell Transp Syst 7(4):429–436
Van de Hoef S (2016) Fuel-efficient centralized coordination of truck platooning. PhD thesis, KTH Royal Institute of Technology
Van De Hoef S, Johansson KH, Dimarogonas DV (2015) Fuel-optimal centralized coordination of truck platooning based on shortest paths. In: 2015 American control conference (ACC), IEEE, pp 3740–3745
Van den Berg M, Hegyi A, De Schutter B, Hellendoorn H (2007) Integrated traffic control for mixed urban and freeway networks: a model predictive control approach. Eur J Transport Infrastruct Res EJTIR 7(3)
Varaiya P (2013) The max-pressure controller for arbitrary networks of signalized intersections. In: Advances in dynamic network modeling in complex transportation systems, Springer, pp 27–66
Vinitsky E, Kreidieh A, Le Flem L, Kheterpal N, Jang K, Wu C, Wu F, Liaw R, Liang E, Bayen AM (2018) Benchmarks for reinforcement learning in mixed-autonomy traffic. In: Conference on robot learning, pp 399–409
Wadud Z, MacKenzie D, Leiby P (2016) Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transp Res A Policy Pract 86:1–18
Wan N, Vahidi A, Luckow A (2016) Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic. Transp Res Part C Emerg Technol 69:548–563
Wan N, Zhang C, Vahidi A (2017) Probabilistic anticipation and control in autonomous car following. IEEE Trans Control Syst Technol 27(1):30–38
Wang Z, Wu G, Hao P, Boriboonsomsin K, Barth M (2017) Developing a platoon-wide eco-cooperative adaptive cruise control (CACC) system. In: 2017 IEEE intelligent vehicles symposium (IV), IEEE, pp 1256–1261
Wang M, van Maarseveen S, Happee R, Tool O, van Arem B (2019) Benefits and risks of truck platooning on freeway operations near entrance ramp. Transp Res Rec 2673(8):588–602
Wu Y, Abdel-Aty M, Wang L, Rahman MS (2019) Improving flow and safety in low visibility conditions by applying connected vehicles and variable speed limits technologies. Technical report
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(1):31–41
Xu Z, Kang Y, Lv W (2017) Analysis and prediction of vehicle exhaust emission using ANN. In: 2017 36th Chinese control conference (CCC), IEEE, pp 4029–4033
Xu B, Ban XJ, Bian Y, Li W, Wang J, Li SE, Li K (2018) Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections. IEEE Trans Intell Transp Syst 20(4):1390–1403
Yang L, Hu X, Fang L (2018) Carbon emissions tax policy of urban road traffic and its application in Panjin, China. PLoS One 13(5)
Zegeye SK (2011) Model-based traffic control for sustainable mobility
Zegeye SK, De Schutter B, Hellendoorn J, Breunesse EA, Hegyi A (2012) A predictive traffic controller for sustainable mobility using parameterized control policies. IEEE Trans Intell Transp Syst 13(3):1420–1429
Zegeye S, De Schutter B, Hellendoorn J, Breunesse E, Hegyi A (2013) Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes. Transp Res Part C Emerg Technol 31:158–171
Zeng W, Miwa T, Morikawa T (2016) Prediction of vehicle co2 emission and its application to eco-routing navigation. Transp Res Part C Emerg Technol 68:194–214
Zhang Y, Cassandras CG (2018) The penetration effect of connected automated vehicles in urban traffic: an energy impact study. In: 2018 IEEE conference on control technology and applications (CCTA), IEEE, pp 620–625
Zhang L, Chen F, Ma X, Pan X (2020) Fuel economy in truck platooning: a literature overview and directions for future research. J Adv Transp
Zhao X, Xu W, Ma J, Li H, Chen Y, Rong J (2019) Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving. Accid Anal Prev 128:206–216
Zhu F, Ukkusuri SV (2014) Accounting for dynamic speed limit control in a stochastic traffic environment: a reinforcement learning approach. Transp Res Part C Emerg Technol 41:30–47
Zhu J, Ngo C, Sciarretta A (2019) Real-time optimal eco-driving for hybrid-electric vehicles. IFAC-Papers OnLine 52(5):562–567. 9th IFAC symposium on advances in automotive control AAC 2019
Zu Y, Liu C, Dai R, Sharma A, Dong J (2018) Real-time energy-efficient traffic control via convex optimization. Transp Res Part C Emerg Technol 92:119–136
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Othman, B., De Nunzio, G., Sciarretta, A., Di Domenico, D., Canudas-de-Wit, C. (2021). Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management. In: Lackner, M., Sajjadi, B., Chen, WY. (eds) Handbook of Climate Change Mitigation and Adaptation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6431-0_128-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6431-0_128-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6431-0
Online ISBN: 978-1-4614-6431-0
eBook Packages: Springer Reference Chemistry and Mat. ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics