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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"10 Intelligent Transportation Systems and Automation." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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10 Intelligent Transportation Systems and Automation 10.1 INTRODUCTION In this chapter we review the intelligent transportation systems (ITSs) and automation technologies which appear poised to have the greatest impact on the fuel efficiency and environmental impacts of medium- and heavy-duty vehicles (MHDVs), including • Platooning, a promising technology which is being heavily tested and even implemented; • Intelligent truck parking, another fairly straightforward and very important technology; and • Connected vehicles (sometimes referred to as cooperative intelligent transportation systems (C-ITSs) or ad hoc vehicular networks), which are a precursor to automation or partial automation. Though it does not seem likely that fully autonomous driving will have a major impact on the fuel economy of MHDVs in the near term, it may well be that in certain situations it will be important. In the near term these will be fully grade- separated operations such as large ports and agricultural centers. In the longer term these systems will have an enormous impact on the efficiency of freight and fleet systems. While the most important near-term impacts of adoption of connected vehicle technologies for MHDVs will be primarily safety related, fuel consumption and environmental impacts will also be impacted. 10.2 TRUCK PARKING TECHNOLOGY One area in which ITS technologies are sure to have an immediate impact is in the area of truck parking. While the primary reason for these systems is a pressing need to improve rest opportunities for drivers, the combination of a lack of available parking spaces and a lack of information about those spaces results in drivers wasting fuel and generating pollution, either while searching for parking or because they then park in spaces without appropriate parking facilities, using engine idling for power. Therefore, these systems will have an immediate impact on heavy vehicle fuel economy. Simple sensor and communication technologies are being developed in order to provide drivers with information on available parking spaces in their vicinity and will also enable reservation systems which will guarantee parking to drivers upon their arrival at a parking facility. Such systems have been discussed for many years (for a review of earlier research see Garber et al., 2004), only in the past few have they begun to see widespread testing across the United States and internationally. It seems likely that there will be large-scale adoption of these systems in developed countries within the next 5 years. In several U.S. states and European countries variable message signs are already providing real- time information to truck drivers about available spots, and reservation systems are being tested (see, for example, Figure 10-1). Many studies have been performed regarding the sensor or imaging technologies that are appropriate for cataloging parking inventories. These technologies include video imaging (including stereovision and computer vision technologies), which is very promising (Chachich and Smith, 2011; Cook et al., 2014; Deruytter et al., 2012; Gertler and Murray, 2011; Modi et al., 2011); inexpensive, but apparently insufficiently accurate, magnetometers (Fallon and Howard, 2011); in-pavement sensors (Thinking Highways, 2014); human-in-the-loop closed-circuit television monitoring systems; and simple systems to monitor entrance and exit from truck parking facilities (Easyway, 2012; Woodrooffe et al., 2016). While most of the pilot studies have focused on roadside variable message systems for communicating with drivers, information is increasingly made available via standard web interfaces, phone apps, or instant messaging (see, for example, Thinking Highways, 2015). Prepublication Copy – Subject to Further Editorial Correction 10-1

FIGURE 10-1 Changeable message road signs identify the vacancy level at respective rest areas and exits. SOURCE: Woodrooffe et al. (2016). A number of reservation systems have also been proposed and several have been tested. These range from simple web- or phone-based reservation systems (Mbiydzenyuy et al., 2012) to more sophisticated multi-agent-based negotiation platforms (Garcia et al., 2014). 10.3 TRUCK PLATOONING Interest in truck platooning has been strong for more than 25 years now. Tests of prototype systems were conducted in the United States, Europe, and Japan in the late 1990s (Franke et al., 1995; Fritz, 1999), but the technologies required to make these systems operational were rather slow to mature. In the past 3 years, connected vehicle technologies have advanced to the extent that these systems are being widely tested and, in a few isolated cases, becoming operational. Bergenhem et al. (2012) details tests in the United States, several European countries, and Japan. Tests have also been conducted in Australia and several other Asian countries. Though estimates vary, the consensus is that platooning reduces fuel consumption by about 7.5 percent. In 2016, NACFE conducted a study for potential fuel consumption savings versus a separation distance of two tractor-trailers in platooning mode. For a separation distance of 40 to 50 feet, the average fuel savings for the trailing truck in a platoon is about 10 percent and for the lead truck is about 4 percent (NACFE, 2016). While platoon testing in a controlled environment can produce 7.5 percent fuel consumption savings, it is not practical to expect this level in the real world (NACFE, 2016). Considering that not all miles can be in platooning mode and that the platoon will be disturbed and affected by traffic surrounding the two trucks, the real-world fuel consumption savings would be closer to 4 percent, which is still a significant amount for fuel economy savings (NACFE, 2016). In order to realize these savings along significant parts of any heavy-duty vehicle trip, coordinated scheduling of trips within and among companies would be required. Larsson et al. (2015) define an optimization problem that maximizes platooning opportunities for all trucks in a network and present heuristics that work well with problems involving 200 trucks. They point out that real-world problems of this type would include several hundreds or even thousands of trucks in a network. Van de Hoef et al. (2015) introduced an optimization “framework for a fuel-optimal coordination where the trucks adapt their speeds in order to form platoons during their journeys. The influence of speed on the fuel consumption is explicitly considered and trucks are guaranteed to arrive at their destinations by their arrival deadlines.” The opportunity to improve safety while also saving fuel and lowering greenhouse gases is evident in platooning. A recent study (Mihelic et al., 2015) examined the relative merits of Prepublication Copy – Subject to Further Editorial Correction 10-2

platooning compared with longer combination vehicles (LCVs), concluding that LCVs provide more benefit in economics and fuel and emissions reduction compared with platooning. Finding: Automated operation that enables truck platooning has the opportunity to save fuel and greenhouse gas emissions achieved through decreases in drag of both trucks. Figure 10-2 shows platooning trucks next to longer combination vehicles. FIGURE 10-2 Platooning vehicles. SOURCE: Mihelic et al. (2015). 10.4 CONNECTED VEHICLES/COOPERATIVE-INTELLIGENT TRANSPORTATION SYSTEMS Connected vehicle technologies, also known as C-ITS technologies, are seeing rapid development in all major automotive manufacturing companies and original equipment manufacturers (OEMs). We include technologies enabling semiautonomous or assisted driving in this category even though in some cases those technologies do not involve cooperation or communication between vehicles. As mentioned above, the fuel economy impacts realized by platooning can be significant. The fuel economy benefits of eco-cooperative cruise control, which employs vehicle-to-vehicle (V2V) communication to determine the preceding vehicle’s speed, acceleration, and distance to compute the most fuel efficient vehicle trajectory using additional information such as roadway characteristics and vehicle configuration, should also be significant. A recent research project (Kamalanathsharma and Hesham, 2012) estimates the potential fuel savings for connected vehicles in the vicinity of signalized intersections to be 30 percent. While those savings apply less to long-distance trucking operations, they will impact the performance of urban delivery, transit, and vocational vehicles. Another variant of cooperative adaptive cruise control (CACC) is the infrastructure-to-vehicle to vehicle-to-infrastructure (V2I) systems in which the roadway infrastructure, through its traffic management center and roadside devices, provides recommended speeds to the vehicle speed control systems. This infrastructure-based information could be static (providing average best speeds) or dynamic (providing variable suggested speeds). Shladover et al. (2015) and Nowakowski et al. (2015) provide definitions and operational concepts related to CACC. Other technologies aimed at eco-driving could also have an impact on the fuel economy of MHDVs. Some of these systems provide drivers with more information about the real-time performance of their vehicles and recommended speeds, while others could take control over the driving speed of vehicles in order to optimize fuel efficiency. Prepublication Copy – Subject to Further Editorial Correction 10-3

Impediments to near-term success of these technologies include standards and interoperability, issues that must be worked out by automotive manufacturers and OEMs. And, security is a key issue. Recently, several researchers demonstrated how vulnerable these systems are to various kinds of attacks (Markey, 2015; Wilonsky, 2015; Zhang et al., 2014), although some credible sources have questioned the reliability of those reports (see, for example, Newcomb, 2015; Pogue, 2016). 10.4.1 Vehicular Ad Hoc Networks Vehicular Ad Hoc Networks (VANETs) operate with little or no permanent infrastructure and are characterized by (1) high mobility, (2) fixed road networks, (3) predictable speed and traffic patterns in congested conditions, and (4) very few power constraints or storage limitations. Unlike other communication systems, in which the primary goal is to achieve high message throughput, VANETs aim primarily for communication reliability and fast dissemination. Communication pathways include • V2V, whereby messages are transmitted between neighboring vehicles. This includes “one- hop” and “multi-hop” messaging scenarios in which vehicles communicate directly with other vehicles or through intermediary vehicles. • V2I, whereby messages are transmitted between vehicles and road-side units located on nearby arterial road intersections or highway on-ramps. • Vehicle-to-pedestrian (V2P), whereby messages are transmitted between vehicles and pedestrians who send and receive messages via their phones or other wireless devices. These vehicular communication networks can help improve safety, the environment, and mobility. For example, V2V and V2I systems use information on acceleration and braking behaviors of nearby vehicles to track dangers beyond a driver’s line of sight, helping to prevent collisions. V2P systems will improve the safety of pedestrians crossing at intersections and facilitate carpooling and ridesharing. The primary impediments to large-scale implementation of VANETs are related to communication challenges, reliability issues, security and privacy issues, and societal issues such as liability and jurisdiction/cost allocation and control of public-sector components of such systems. And, as originally imagined, these systems will likely be rendered obsolete by private-sector investments automated vehicles, though they can be considered precursors to those technologies. It is critical that vehicular communications networks have robust and fault-tolerant software that has the ability to recover gracefully from connection downtime, system errors, and unforeseen scenarios. For V2V and V2I applications, a few seconds’ delay in downtime may be the deciding factor in a traffic accident. And unlike consumer electronics that may involve frequent hardware turnover (for example, smartphones or tablets), the computing hardware for vehicular communication networks (on-board units, vehicle sensors, etc.) needs to have a much longer life span, closer to the decade or more operating life of the vehicle. In addition, future repair or maintenance may or may not happen according to manufacturers’ guidelines. The reliability of both the computing software and the hardware components remain major technical obstacles that must be overcome in order for these systems to achieve their promise. Finally, issues related to liability must be addressed. For a recent detailed discussion of all of these issues please see Regan and Chen (2015). 10.4.2 Automated Vehicles It seems likely that in the distant future there will be a considerable number of fully automated (referred to as “independent operation”) MHDVs. In the short and mid term, trucks will at a minimum operate by driver assistance, but wil be “cooperatively operated” by the equipment. In September 2015, the Technology and Maintenance Council of the American Trucking Associations issued a white paper, “Automated Driving and Platooning Issues and Opportunities” Prepublication Copy – Subject to Further Editorial Correction 10-4

(ATA/TMC, 2015). The paper details a number of automated operations opportunities for trucks, including level 1 and greater automation. These include • Truck platooning, • Highly automated trucks, • Traffic jam assist, • Automated trailer backing, • Highway pilot, and • Automated movement in queues (ports and other intermodal facilities). The report details some important tasks for the trucking industry to undertake in order to facilitate automation: • “Prepare, as needed, model legislation for states regarding operation of near-term truck AV systems. • Assess current insurance approaches for suitability for increasing levels of automation. Work with insurers to develop new models as needed. Address the particular needs of self-insured fleets. • Assess and facilitate public acceptance of near-term truck AV systems. • Address specific technical and scientific evaluation tasks that are needed to adequately verify performance of these systems. • Conduct research to determine what technology is needed to control the platooning engagement/disengagement process such that corporate policies are so enforced, if so desired. Current platooning models assume the driver will make the decision as to whether to platoon or not. However, some companies may wish to restrict this decision-making to a preferred list of carriers/operators. • Conduct research on developing criteria for joining a platoon based on vehicle configuration, loads, weather conditions, routes, internal company cost, customer contracts, etc. This, because not all runs are profitable and not all platooning may result in economic savings even if fuel costs are reduced. • Assess the impact that local, state and federal government may have on regulating the distance between platooned vehicles, since size and weight regulations are often addressed at a state or local level.” There will be many benefits to such systems, including improved safety, around-the-clock operation, and fuel and freight efficiency improvements, but many technology and public policy issues must be worked out before widespread adoption can be achieved. For large-scale autonomous driving to be a success, issues of reliability, security, and privacy are gravely important (see, for example, Kalra et al., 2009; Marchant and Lindor, 2012). Automakers and public-sector officials are all concerned that these issues must be properly addressed before these systems are fully operational. 10.5 SAFETY-IMPROVING TECHNOLOGIES While the goal of safety-improving technologies is to reduce the negative impacts of accidents— deaths, injuries, congestion, and costs due to damage to vehicles and the environment—many of these technologies provide opportunities for improving the overall efficiency of freight and fleet movements. Additionally, these safety technologies are the key to the success of driver-assisted or driverless vehicles. The primary technologies available are electronic stability and roll stability control, forward collision and mitigation systems, adaptive cruise control, and lane-keeping systems. Prepublication Copy – Subject to Further Editorial Correction 10-5

10.6 REFERENCES ATA/TMC (American Trucking Association/Technology and Maintenance Council). 2015. Future Truck Program Position Paper 2015-3: Recommendations Regarding Automated Driving and Platooning Systems. http://www.trucking.org/ATA%20Docs/About/Organization/TMC/Documents/Position%20Paper s/Future%20Truck%20Position%20Papers/FT_PP_2015_3.pdf. Bergenhem, C., H. Pettersson, S. Shladover, E. Coelingh, C. Englund, S. Tsugawa. 2012. "Overview of platooning systems". Proceedings of the 19th ITS World Congress, Oct 22-26, Vienna, Austria. Available at http://publications.lib.chalmers.se/records/fulltext/174621/local_174621.pdf (accessed September 23, 2019). Chachich, A., and S. Smith. 2011. Smart Park: Truck Parking Field Operation Test Results, Volpe National Transportation Systems Center. http://ntl.bts.gov/lib/43000/43000/43029/Chachick_Smart_Park.pdf. Cook, D.J., T. Morris, V. Morellas, and N. Papanikolopoulos. 2014. An automated system for persistent real-time truck parking detection and information dissemination. Pp. 3989-3994 in IEEE International Conference on Robotics and Automation. IEEE, Hong Kong, China, May 31-June 7. Deruytter, M., K. Maddelein, W. Favoreel, D. Tsishkou, S. Bougnoux, and R. Bendahan. 2012. Stereovision truck parking occupancy detection. In 19th ITS World Congress, Vienna, Austria, 22 to 26 October 2012. ITS America, Washington, D.C. Easyway. 2012. Freight & Logistics Services Intelligent and Secure Truck Parking Deployment Guideline. http://www.rits-net.eu/uploads/media/EW-DG-2012_FLS- DG01_IntelligentAndSecureTruckParking_02-00-00.pdf. Fallon, J., and K. Howard. 2011. Smartpark Truck Parking Availability System: Magnetometer Technology Field Operational Test Results. FMCSA-RRT-10-041. Federal Motor Carrier Safety Administration, Washington, D.C. http://ntl.bts.gov/lib/51000/51300/51359/SmartPark- Magnetometer.pdf. Franke, U., F. Bottiger, Z. Zomotor, and D. Seeberger. 1995. Truck platooning in mixed traffic. Pp. 1-6 in Proceedings of the IEEE Intelligent Vehicles ‘95 Symposium. IEEE, Detroit, MI, September 25-26. Fritz, H. 1999. Longitudinal and lateral control of heavy duty trucks for automated vehicle following in mixed traffic: Experimental results from the CHAUFFEUR project. Pp. 1348-1352 in Proceedings of the 1999 IEEE International Conference on Control Applications, Volume 2. IEEE, Kohala Coast, HI, August 22-27. Garber, N. H. Teng, Y. Lu. 2004. A Proposed Methodology for Implementing and Evaluating a Truck Parking Information System, Research Report No. UVACTS-15-5-86. Charlottesville: Center for Transportation Studies, University of Virginia. Garcia, J. F., V.R. Tomas, L.A. Garcia, and J.J. Martinez. 2014. An autonomic system for intelligent truck parking. Pp. 810-916 in Proceedings of the IEEE 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Volume 2. IEEE, Vienna, Austria, September 1-3. Gertler, J., and J. Murray. 2011. Smartpark Truck Parking Availability System: Video Technology Field Operational Test Results. FMCSA-RRT-10-002. Federal Motor Carrier Safety Administration, Washington, D.C. http://ntl.bts.gov/lib/51000/51300/51360/SmartPark-Video.pdf. Kalra, N., J. Anderson, and M. Wachs. 2009. Liability and Regulation of Autonomous Vehicle Technologies. California PATH Research Report, UCB-ITS-PRR-2009-28. University of California Institute of Transportation Studies, Berkeley, CA. Kamalanathsharma, R.K., and R. Hesham. 2012. Agent-based modeling of Eco-Cooperative Adaptive Cruise Control systems in the vicinity of intersections. Pp. 840-855 in Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, Anchorage, AK, September 16-19. Prepublication Copy – Subject to Further Editorial Correction 10-6

Larsson, E., G. Sennton, and J. Larson. 2015. The vehicle platooning problem: Computational complexity and heuristics. Transportation Research Part C: Emerging Technologies 60:258-277. Marchant, G.E., and R.A. Lindor. 2012. The coming collision between autonomous vehicles and the liability system. Santa Clara Law Review 52(4):1321-1340. Markey, E. 2015. Tracking & Hacking: Security & Privacy Gaps Put American Drivers at Risk. http://www.markey.senate.gov/imo/media/doc/2015-02-06_MarkeyReport- Tracking_Hacking_CarSecurity%202.pdf. Mbiydzenyuy, G., J.A. Persson, and P. Davidsson. 2012. Proposed core services for the deployment of intelligent truck parking. In 19th ITS World Congress, Vienna, Austria, 22 to 26 October 2012. ITS America, Washington, D.C. Mihelic R., J. Smith, and M. Ellis. 2015. Aerodynamic comparison of tractor trailer platooning and A- train configuration. SAE International Journal of Commercial Vehicles 8(2):740-746. Modi, P., V. Morellas, and N. Papanikolopoulos. 2011. Counting Empty Parking Spots at Truck Stops Using Computer Vision. CTS 11-08. Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, Minneapolis, MN. NACFE (North American Council for Freight Efficiency). 2016. Confidence Report on Two-Truck Platooning. https://nacfe.org/technology/two-truck-platooning/ (accessed September 23, 2019). Newcomb, D. 2015. “Congress, 60 Minutes exaggerate threat of car hacking.” Forbes, February 9. http://www.forbes.com/sites/dougnewcomb/2015/02/09/60-minutes-joins-car-hacking- hype/#5fdb8cb04f4d. Accessed May 10, 2016. Nowakowski, C., S.E. Shladover, X.Y. Lu, D. Thompson, and A. Kailas. 2015. Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives. California Partners for Advanced Transportation Technology, University of California, Berkeley, CA. http://eprints.cdlib.org/uc/item/7jf9n5wm. Accessed May 10, 2016. Pogue, D. 2016. “Why car hacking is nearly impossible.” Scientific American, February 22. http://www.scientificamerican.com/article/why-car-hacking-is-nearly-impossible/. Accessed May 10, 2016. Regan, A.C., and R. Chen. 2015. Ad hoc vehicular networks. Pp. 29-36 in Vehicular Communications and Networks, edited by W. Chen. Cambridge, UK: Elsevier. Shladover, S.E., C. Nowakowski, X.Y. Lu, and R. Ferlis. 2015. Cooperative adaptive cruise control: Definitions and operating concepts. Transportation Research Record: Journal of the Transportation Research Board 2489:145-152. Thinking Highways. 2014. “Nedap deploys smart truck parking systems in Denmark.” http://thinkinghighways.com/nedap-deploys-smart-truck-parking-system-in-denmark/. Accessed June 4, 2015. Thinking Highways. 2015. “Truck Parking Europe now available as a web portal.” http://thinkinghighways.com/truck-parking-europe-now-available-as-a-web-portal/. Accessed June 4, 2015. van de Hoef, S., K.H. Johansson, and D.V. Dimarogonas. 2015. Fuel-optimal centralized coordination of truck platooning based on shortest paths. Pp. 3740-3745 in American Control Conference (ACC). IEEE, Chicago, IL, July 1-3. Wilonsky, R. 2015. “Dallas attorney sues Ford, Toyota and GM, claiming their cars are ‘susceptible to hacking’.” Dallas Morning News, March 10. http://bizbeatblog.dallasnews.com/2015/03/dallas- attorney-sues-ford-toyota-and-gm-claiming-their-cars-are-susceptible-to-hacking.html/. Accessed May 10, 2016. Woodrooffe, J., D. Blower, and J. Sullivan. 2016. Evaluation of Michigan DOT truck Parking Information and Management System. University of Michigan Transportation Institute Report 2016-15. Ann Arbor, MI. Zhang, T., H. Antunes, and S. Aggarwal. 2014. Defending connected vehicles against malware: Challenges and a solution framework. IEEE Internet of Things Journal 1(1):10-21. Prepublication Copy – Subject to Further Editorial Correction 10-7

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Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report Get This Book
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Medium- and heavy-duty trucks, motor coaches, and transit buses - collectively, "medium- and heavy-duty vehicles", or MHDVs - are used in every sector of the economy. The fuel consumption and greenhouse gas emissions of MHDVs have become a focus of legislative and regulatory action in the past few years. This study is a follow-on to the National Research Council's 2010 report, Technologies and Approaches to Reducing the Fuel Consumption of Medium-and Heavy-Duty Vehicles. That report provided a series of findings and recommendations on the development of regulations for reducing fuel consumption of MHDVs.

On September 15, 2011, NHTSA and EPA finalized joint Phase I rules to establish a comprehensive Heavy-Duty National Program to reduce greenhouse gas emissions and fuel consumption for on-road medium- and heavy-duty vehicles. As NHTSA and EPA began working on a second round of standards, the National Academies issued another report, Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: First Report, providing recommendations for the Phase II standards. This third and final report focuses on a possible third phase of regulations to be promulgated by these agencies in the next decade.

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