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Potentials for Platooning in U.S. Highway Freight Transport.


Connected and automated vehicles (CAVs) are receiving significant attention as a technology solution to realize safer, more cost-effective, and efficient operation of several transportation systems [1]. CAVs can also potentially help curb energy consumption and greenhouse gas (GHG) emissions from the transportation sector. In this paper we focus on the role of platooning for combination trucks (1) in the United States, one of the most promising C AV technologies that could experience widespread adoption in the next 5 to 10 years. Platooning is a demonstrated method of groups of vehicles travelling close together actively coordinated in formation at high speed that has the potential to reduce energy consumption resulting from aerodynamic drag [2] [3]. Trucks are ideal applications for platooning due to their technical characteristics and mode of operation (several vehicles driving for long distances along the same route, often concentrated in few corridors).

Combination trucks, currently powered by petroleum-derived fuels, account for the majority of the energy use in the U.S. freight sector (64.9% of freight, and 4.8% of total U.S. energy use in 2013 [4]) and an even larger share of GHG emissions (77.1% of freight, and 7.5% of total U.S. GHG emissions in 2013 [5]). Looking at the future, the importance of trucking on the U.S. energy use and GHG emissions is likely to increase, due mainly to three factors: a) freight transport has been growing more rapidly than passenger transport, and the trend is likely to continue in the future [6] [7]; b) a continued increase in the share of trucking in total freight activity [8] [9] [10]; c) transportation, and freight in particular, is more expensive to decarbonize compared to other sectors, and will experience lower energy and GHG emissions reduction in response to economy-wide climate change mitigation measures [11].

Several studies, reviewed in the Methods section, have been focusing on assessing the potential savings achievable by platooning operations for a group of two or more trucks, as well as extrapolating these savings on a national scale, based on overall miles traveled by trucks. However, a key element has been neglected in the existing literature: what is the "platoonable" fraction of traveled miles during real-world operations? Namely, in a fleet of trucks, what fraction of miles driven is amenable for platooning operation? Clearly not every mile driven can be driven in a platoon formation, and platooning operations at low speeds do not lead to significant fuel saving. However, for large trucks operating extensively on highways over long distances the fraction of platoonable miles at high speed can be significant (in estimating the potential savings related to trucks platooning, MacKenzie et al. [12] assume that every mile traveled by trucks is platoonable, leading to significantly different results compared to this study).

We provide an estimate of the platoonable fraction of miles driven by combination trucks in the United States based on a large set of driving data collected by the National Renewable Energy Laboratory (NREL) and others. This data set includes over 3 million miles of driving data across a variety of fleet operators, truck manufacturers, times of operation, and regions. In particular, we assume that a truck could potentially operate in a platoon if it continuously travels at a speed larger than a certain threshold for a significant period of time. A sensitivity analysis shows that the velocity and the time threshold significantly impact the resulting fraction of platoonable miles. These thresholds have been chosen to be 50 mph (80.5 km/h) and 15 minutes for representative operations in the United States.

This estimate represents a technical potential, or upper bound, which does not account for truck and fleet operators' willingness to platoon. This willingness, which will be assessed in future works, reduces the technical potential identified in this paper due to three main factors. First, the economic savings related to platooning operations (value of fuel saved) must outweigh the increased costs, namely the additional drivers' time cost during platoon formation (as most likely some drivers will have to wait for other trucks traveling towards the same destination) and the value of delayed delivery. (2) Second, truck and fleet operators must be willing to cooperate. While this might be easier for large fleets including hundreds of vehicles, smaller operators might not have the required connectivity and willingness to collaborate with direct competitors. Third, uniform and standard technologies are required across vehicle manufacturers and operators to allow for widespread implementation of platooning across fleets.

The remainder of this paper is structured as follows: The Methods section describes the data set and the methodology used to estimate the real-world fraction of platoonable miles for combination trucks in the United States and a review of literature of existing studies on energy savings achievable by operating trucks in platoons. The Results section reports the quantitative results on this analysis, including a sensitivity analysis aimed at understanding the impact of time and velocity thresholds in estimating the fraction of platoonable miles and additional insights for targeted applications (i.e., platoonable miles for vehicles performing only long-distance missions on highways). These insights are used in the National Impact section to calculate an upper-bound estimate of the potential energy savings and GHG emissions reductions related to widespread adoption of platooning for combination trucks. Concluding remarks and proposed future work are reported in the last section.


In this paper we use a large data set of about 200 real-world Class 8 tractors' driving data, which includes over 3 million miles of data, to estimate the fraction of platoonable miles in a variety of real-world operations in the United States. The data considered have been collected directly by NREL and other partners who have contributed data to NREL's Fleet DNA database using on-board data logging devices or telematics systems [13]. Vocations represented in the data set include line haul truck load, less than truck load, regional parcel movement, port drayage, refrigerated operations, tanker operations, transfer truck operations, and regional food delivery. The data set includes information on vehicle speed (1-second resolution), global positioning system position, road segments (classified as highway, freeway, or collectors and local), and various levels of engine/vehicle parameters such as fuel rate and engine temperatures.

Table 1 summarizes the data set considered, while Figure 1 and Figure 2 show the distribution of all the trips included in the data set based on trip length and duration, respectively. Trips shorter than 0.5 mile and 6 minutes have been excluded to avoid including logging errors and short vehicle movements that do not constitute trips.

From Figure 1 and Figure 2, it appears that very short trips (i.e., less than 25 miles and less than one hour) constitute a significant share of trips included in the data set considered. Nevertheless, these trips account for a small fraction of total miles driven, as shown in Figure 3 and Figure 4, which report the share of driven miles for several classes of trip length and duration. The majority of miles driven by the trucks included in the data set were driven in trips between 50 and 250 miles long that lasted between 2 and 6 hours. Some very long trips (i.e., over 500 miles and 8 hours) are also present in the data set (about 10% of total miles driven), resulting from vehicles being driven by multiple drivers who took turns driving without turning off the engine for prolonged periods of time.

The extensive and comprehensive data analyzed allow calculating a reasonable estimate of the total fraction of platoonable miles across different U.S. regions and truck applications.

State-of-the-Art for Trucks Energy Savings in Platooning Operations

Several analyses, based both on simulation studies and experiments, have estimated the energy savings during platoon operations of two or more trucks. While platooning opportunities for a variety of applications have been explored for over a decade (e.g., [14], [15]), no consensus has been reached in the open literature on the fuel savings related to platooning operations of more than two vehicles.

Lu and Shladover [16] tested a platoon of three Class 8 tractor-trailers under different driving conditions and following distances, reporting a fuel saving of 4%-5% for the leading truck and 10%-14% for the following trucks. Lammert et al. [17] performed ten modified SAE Type II J1321 fuel consumption track tests to evaluate fuel consumption results of two Class 8 tractor-trailer combinations platooned together compared to their standalone fuel consumption, reporting combined "Team" fuel savings ranging from 3.7% to 6.4% (between 2.7% and 5.3% fuel savings for the lead tractor and between 2.8% and 9.7% for the trailing vehicle).

A recent study by the North American Council for Freight Efficiency (NACFE) reviewed the results of ten analyses performed over the last decade directly comparing driving speeds from 43 to 70 mph, conventional and cab-over-engine configurations, and a range of vehicle curb weights, which showed a significant spread among the different test results. While lead vehicle savings had significant correlation across the variables with following distance being most important factor in the 0-9% range of observed fuel savings; for the trailing truck(s) fuel consumption reductions was reported to vary between 3% and 23% and showed much higher dependence on speed, mass and cab configuration variables [18]. Combining results with all the above variables, NACFE estimates the savings to be approximately 4% for the lead truck and 10% for the following truck when trucks are operated on a closed track in a consistent two-truck platooning arrangement. This equates to a 7% fuel efficiency improvement on average between the two trucks versus a truck operating in isolation. Moreover, NACFE identified road congestion and actual platoonable miles as the two most relevant factors influencing real-world fuel economy of trucks operating in platoon formation and offered an estimate of impact from these factors [18]. Significant correlation was observed between multiple track studies, wind tunnel testing, and computational fluid dynamics analyses when compared at the same speed, mass, and aerodynamic class/type over a range of following distances [19].

In this paper we consider a 6.4% potential fuel saving for platooning operations, based on the study by Lammert et al. [17], with the best combined result being for 55 mph and a 30-ft following distance. In future applications, platooning fuel savings can be enhanced by addressing barriers to closer platoon formation--such as reduced engine cooling--and by including more vehicles in each platoon [19]. Alam et al. suggested that a large-scale cooperative method to enhance safety and efficiency of truck platoons by increasing the level of cooperation between vehicles be used to maximize platooning benefits [20]. Additional benefits of truck platooning, such as road capacity optimization and accidents reduction, as well as additional truck safety and operational considerations have also been explored in previous studies ([21], [22], [23]).


Based on the data set described in the Methods section, we compute the fraction of miles that are continuously driven above a speed threshold V for at least T minutes, where T is a time threshold. This is intended to capture the fraction of driven miles that are suitable for platooning operations. In principle, V should equal the prescribed speed limit, and T should be a time long enough to offset the tradeoffs due to platoon formation.

Figure 5 shows the share of miles driven in each road segment based on the entire data set summarized in Table 1, as well as the fraction of miles continuously driven above a speed limit for time T for a set of different thresholds. The results show that for a time threshold of T = 15 minutes and a speed threshold V = 50 mph, 65.6% of vehicle miles are platoonable. The figure also shows how this number changes as different time and speed thresholds are selected.

Targeted Applications

The same methodology used to estimate the share of platoonable miles for the entire data set is applied to a subset of the data, including about 4,500 miles of mostly-highway long-distance driving to evaluate the fraction of platoonable miles for specific applications that might represent early adopters of this technology.

The results shown in Figure 6 indicate that for a time threshold of T = 15 minutes and a speed threshold V = 50 mph, 76.6% of vehicle miles are platoonable. The vocation represented in Figure 6 is a split-duty combination truck that runs local pickup and delivery trips during the day and regional line-haul operation at night (representing the majority of the miles driven, and making this application ideal for platoon operations).


In 2014 169.8 billion miles were driven by combination trucks in the United States [24], consuming a total of 29.1 billion gallons of fuel and emitting approximately 6.9 billion metric tons of carbon dioxide equivalent [25]. Based on the analysis provided in this paper, approximately 65.6% of those miles could potentially be driven in platoon formation. Assuming an energy (and emissions) savings of approximately 6.4% for each team of platooned vehicles (based on efficiency improvements previously published in a platooning benefits study [17]), widespread adoption of platooning operations can potentially reduce trucks energy use by approximately 4.2%.

With these bounding assumptions, the widespread adoption of platooning operations for combination trucks in the United States could lead to a total savings of 1.5 billion gallons of petroleum-derived fuels (equal to 1.1% of the current US import of oil: 2.7 billion barrels in 2015 [26]) and 15.3 million metric tons of C[O.sub.2] (a 0.22% emissions reduction).


In this paper we estimate a technical potential, or upper bound, for the fraction of platoonable miles for combination trucks in the United States based on an extensive data set of real-world driving data. This study complements existing literature on this subject that neglected to consider that not all miles driven by trucks are suitable for platooning applications.

Our results show that approximately 65.6% of the total miles driven by combination trucks (Class 7 and 8) could be driven in platoon formation, leading to significant energy and emissions savings. For targeted applications, which are likely to be early adopters of connected and automated technologies, this fraction increases to approximately 76.6%. A more comprehensive "Big Data" analysis considering a larger data set that covers multiple years and a wider array of applications is planned to further refine this estimate.

Based on an assumed energy saving of 6.4%, resulting from a review of recent literature, this translates into 2.7% potential energy savings in the U.S. freight sector and a reduction in U.S. GHG emissions on the order of 15.3 million metric tons of carbon dioxide per year.

As discussed, this technical potential study presents an upper bound because in the real world, truck and fleet operators may not be willing to participate to platoon operations under all the conditions considered here (e.g., an operator might not be willing to wait to form a platoon). Therefore, an expert elicitation study involving truck owners and fleet operators will be performed to assess the overall willingness to participate in platooning and the main barriers for the widespread adoption of this technology.


[1.] Wadud, Z., MacKenzie, D., and Leiby, P., "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part a: Policy and Practice 86:1-18, 2016, doi:10.1016/j.tra.2015.12.001.

[2.] Bergenhem, C., Shladover, S., Coelingh, E., Englund, C., and Tsugawa, S., "Overview of platooning systems," 2012.

[3.] Brown, A., Gonder, J., and Repac, B., "An Analysis of Possible Energy Impacts of Automated Vehicle," Road Vehicle Automation, Springer International Publishing, Cham, ISBN 978-3-319-05989-1: 137-153, 2014, doi:10.1007/978-3-319-05990-7_13.

[4.] Davis, S., Diegel, S., and Boundy, R., "Transportation Energy Data Book," 2015.

[5.] U.S. Department of Transportation, "Freight Facts and Figures" 2015.

[6.] Energy Information Administration (EIA), "International energy outlook,"," 2014.

[7.] ExxonMobil, "The Outlook for Energy: A view to 2040," Irving, 2013.

[8.] Kamakate, F. and Schipper, L., "Trends in truck freight energy use and carbon emissions in selected OECD countries from 1973 to 2005," Energy Policy 37(10):3743-3751, 2009, doi:10.1016/j.enpol.2009.07.029.

[9.] Greening, L.A., Ting, M., and Davis, W.B., "Decomposition of aggregate carbon intensity for freight: trends from 10 OECD countries for the period 1971-1993," Energy Economics 21(4):331-361, 1999.

[10.] Eom, J., Schipper, L., and Thompson, L., "We keep on truckin': Trends in freight energy use and carbon emissions in IEA countries," Energy Policy 45:327-341, 2012, doi:10.1016/j.enpol.2012.02.040.

[11.] Muratori, M., Smith, S.J., Kyle, P., Link, R., Mignone, B., and Kheshgi, H., "The Role of the Freight Sector in Future Climate Change Mitigation Scenarios." Environmental Science & Technology, Forthcoming.

[12.] MacKenzie, D., Wadud, Z., and Leiby, P., "A first order estimate of energy impacts of automated vehicles in the United States," Poster Presentation at the..., 2014.

[13.] Walkowicz, K., Kelly, K., Duran, A., and Burton, E., "Walkowicz: Fleet DNA project data," 2014.

[14.] Gehring, O. and Fritz, H., "Practical results of a longitudinal control concept for truck platooning with vehicle to vehicle communication," IEEE, ISBN 0-7803-4269-0: 117-122, 1997, doi:10.1109/ITSC.1997.660461.

[15.] Robinson, T., Chan, E., and Coelingh, E., "Operating platoons on public motorways: An introduction to the sartre platooning programme," 17th world congress on intelligent transport systems, 2010.

[16.] Lu, X.-Y. and Shladover, S.E., "Automated Truck Platoon Control and Field Test," Road Vehicle Automation, Springer International Publishing, Cham, ISBN 978-3-319-05989-1: 247-261, 2014, doi:10.1007/978-3-319-05990-7_21.

[17.] Lammert, M., Duran, A., Diez, J., Burton, K., "Effect of Platooning on Fuel Consumption of Class 8 Vehicles Over a Range of Speeds, Following Distances, and Mass," SAE Int. J. Commer. Veh. 7(2):626-639, 2014, doi:10.4271/2014-01-2438.

[18.] North American Council for Freight Efficiency (NACFE), "Confidence Report on Two-Truck Platooning ," 2016.

[19.] Lammert, M.P., Gonder, J., Kelly, K., Salari, K., and Ortega, J., "Class 8 Tractor Trailer Platooning: Effects, Impacts, and Improvements," 2016.

[20.] Alam, A., Besselink, B., Turri, V., Martensson, J., and Johansson, K.H., "Heavy-Duty Vehicle Platooning for Sustainable Freight Transportation: A Cooperative Method to Enhance Safety and Efficiency," IEEE Control Syst. 35(6):34-56, 2015, doi:10.1109/MCS.2015.2471046.

[21.] ATA Technology and Maintenance Council Future Truck Program, "Automated Driving and Platooning Issues and Opportunities," 2015.

[22.] Janssen, R., Zwijnenberg, H., Blankers, I., and de Kruijff, J., "Truck platooning: driving the future of transportation," 2015.

[23.] Identifying Autonomous Vehicle Technology Impacts on the Trucking Industry, "Identifying Autonomous Vehicle Technology Impacts on the Trucking Industry," 2016.

[24.] U.S. Federal Highway Administration, "Highway Statistics -Annual Vehicle Distance Traveled in Miles and Related Data."

[25.] U.S. Environmental Protection Agency (EPA), "Greenhouse Gas Inventory Explorer,", Oct. 2016.

[26.] U.S. Energy Information Administration (EIA), "US Imports of Crude Oil."

Matteo Muratori, Jacob Holden, Michael Lammert, Adam Duran, Stanley Young, and Jeffrey Gonder

National Renewable Energy Laboratory

(1.) Combination trucks include Class 7 and Class 8 trucks, as defined by the U.S. Federal Highway Administration. Class 8 trucks, which are the majority of combination trucks, are vehicles with a gross weight rating exceeding 33,001 lbs (14,969 kg). Class 8 includes tractor-trailer tractors as well as single-unit dump trucks. The typical 5-axle tractor-trailer combination, also called a "semi" or "18-wheeler," is a Class 8 vehicle.

(2.) Given the U.S. network and the large volume of freight moved on the road, we assumed that trucks will not modify their original route to travel in a platoon. Namely we assume that within a reasonable time a truck will be able to join others and form a platoon heading towards its final destination. This assumption might not be realistic for very early adoption in the United States or other countries.


Corresponding Author:

Matteo Muratori

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401, USA

Phone: 303-275-2927


This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. Funding was provided by the Vehicle Systems Program within the DOE Energy Efficiency and Renewable Energy's Vehicle Technologies Office. The authors particularly appreciate the support and guidance provided by DOE program managers David Anderson, Lee Slezak and Rachael Nealer. For many years the Vehicle Systems Program has additionally supported the Fleet DNA database of commercial vehicle in-use operating data, which was also instrumental in the completion of this study. The views and opinions expressed in this paper are those of the authors alone.

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Table 1. Summary statistics of the driving data set considered in this


Vehicles  194
Days        9,154
Trips      54,583
Hours      60,450
Miles       3,170,079
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Author:Muratori, Matteo; Holden, Jacob; Lammert, Michael; Duran, Adam; Young, Stanley; Gonder, Jeffrey
Publication:SAE International Journal of Commercial Vehicles
Article Type:Technical report
Date:May 1, 2017
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