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Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management

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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.

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References

  • Aarts L, Feddes G (2016) European truck platooning challenge. In: International symposium on heavy vehicle transport technology

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Chapter  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • De Nunzio G, Thibault L (2017) Energy-optimal driving range prediction for electric vehicles. In: 2017 IEEE intelligent vehicles symposium (IV), pp 1608–1613

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dinopoulou V, Diakaki C, Papageorgiou M (2006) Applications of the urban traffic control strategy TUC. Eur J Oper Res 175(3):1652–1665

    Article  MATH  Google Scholar 

  • Dollar A, Sciarretta A, Vahidi A (2020a) Information and collaboration levels in vehicular strings: a comparative study. In: IFAC proceedings volumes

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Goodall NJ, Smith BL, Park B (2013) Traffic signal control with connected vehicles. Transp Res Rec 2381(1):65–72

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • Hadiuzzaman M, Qiu TZ (2013) Cell transmission model based variable speed limit control for freeways. Can J Civ Eng 40(1):46–56

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hunt P, Robertson D, Bretherton R, Winton R (1981) SCOOT-a traffic responsive method of coordinating signals. Technical report

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Khondaker B, Kattan L (2015b) Variable speed limit: an overview. Transp Lett 7(5):264–278. Taylor & Francis

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Kuo Y (2010) Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Comput Ind Eng 59(1):157–165

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Liard T, Stern R, Delle Monache M-L (2020) Optimal driving strategies for traffic control with autonomous vehicles. In: The 21rst IFAC world congress

    Google Scholar 

  • Lin D, Jabari SE (2020) Pay for intersection priority: a free market mechanism for connected vehicles. arXiv preprint arXiv:2001.01813

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Lioris J, Pedarsani R, Tascikaraoglu FY, Varaiya P (2016) Doubling throughput in urban roads by platooning. IFAC-PapersOnLine 49(3):49–54

    Article  Google Scholar 

  • Litman T (2017) Autonomous vehicle implementation predictions. Victoria Transport Policy Institute, Victoria

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Lu X-Y, Shladover SE (2011) Automated truck platoon control. Technical report, Institute of Transportation Studies, UC Berkeley

    Google Scholar 

  • Marcu B, Browand F (1999) Aerodynamic forces experienced by a 3-vehicle platoon in a crosswind. Technical report, SAE technical paper

    Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Namazi E, Li J, Lu C (2019) Intelligent intersection management systems considering autonomous vehicles: a systematic literature review. IEEE Access 7:91946–91965

    Article  Google Scholar 

  • Nannicini G, Delling D, Schultes D, Liberti L (2012) Bidirectional a* search on time-dependent road networks. Networks 59(2):240–251

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Osorio C, Nanduri K (2015) Energy-efficient urban traffic management: a microscopic simulation-based approach. Transp Sci 49(3):637–651

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Google Scholar 

  • Papageorgiou M, Kotsialos A (2002) Freeway ramp metering: an overview. IEEE Trans Intell Transp Syst 3(4):271–281

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Pulkrabek WW (2004) Engineering fundamentals of the internal combustion engine, 2nd Ed. Journal of Engineering for Gas Turbines and Power 126(1):198–198

    Google Scholar 

  • Qi YG, Teng HH, Yu L (2004) Microscale emission models incorporating acceleration and deceleration. J Transp Eng 130(3):348–359

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Robinson M et al (2000) Examples of variable speed limit applications

    Google Scholar 

  • Sciarretta A, Vahidi A (2020a) Energy saving potentials of CAVs. Springer International Publishing, Cham, pp 1–31

    Google Scholar 

  • Sciarretta A, Vahidi A (2020b) Fundamentals of vehicle modeling. Springer International Publishing, Cham, pp 33–62

    Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Tajali M, Hajbabaie A (2018). Dynamic speed harmonization in urban street networks. Comput-Aided Civ Inf Eng

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • U.S. Department of Energy (2018) Autonomous vehicles: uncertainties and energy implications

    Google Scholar 

  • U.S. Energy Information Administration (2017) International energy outlook

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Van de Hoef S (2016) Fuel-efficient centralized coordination of truck platooning. PhD thesis, KTH Royal Institute of Technology

    Google Scholar 

  • 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

    Google Scholar 

  • 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)

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wan N, Zhang C, Vahidi A (2017) Probabilistic anticipation and control in autonomous car following. IEEE Trans Control Syst Technol 27(1):30–38

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • Zegeye SK (2011) Model-based traffic control for sustainable mobility

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

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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

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