All for one: Centralized optimization of truck platoons to improve roadway infrastructure sustainability

doi:10.1016/j.trc.2020.02.002

Transportation Research Part C: Emerging Technologies

Volume 114, May 2020, Pages 84-98

https://doi.org/10.1016/j.trc.2020.02.002 Get rights and content

Highlights

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The pavement-related challenges of truck platooning are highlighted.
•
V2I based optimization framework for truck platooning is proposed.
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The results showed that pavement life cycle cost can be reduced up to 50%.

Abstract

With the introduction of connected and autonomous trucks (CATs), truck platooning is expected to be more feasible and prevalent. The reported benefits of the truck platooning include regularizing traffic, reducing congestion, increasing highway safety, and decreasing fuel consumption and emission. Truck platooning may, however, decrease pavement longevity because it would cause channelized load application and hinder the healing properties of asphalt concrete. This study proposes a centralized control strategy that converts the pavement-related challenges of truck platooning into opportunities. This strategy leverages the auto-pilot technologies in CATs by optimizing the lateral position of each platoon or group of platoons. The efficiency of the proposed control strategy was demonstrated in a case study. Results showed that pavement life-cycle costs could be reduced up to 50% by controlling the lateral position of the platoons for each day.

Introduction

Passenger cars and shuttles have primarily driven connected and automated vehicle (CAV) technology research, but recently there has been significant interest in developing connected and autonomous trucks (CATs). In fact, many major CAV technology manufacturers, such as Google-owned Waymo, have launched subsidiaries or departments for CAT development. CATs are expected to bring many advantages into freight industry such as improving operational efficiency of freight shipments and overcoming shortage in truck drivers.

The introduction of CATs may result in drastic changes in freight shipment operation. One important change is the formation of truck platoons, a convoy of trucks traveling in close distance. With the advancements in intelligent technologies used in CATs that enable the connection among vehicles and between vehicles and infrastructure, truck platooning is set to become a feasible, efficient and prevalent practice in the future. Truck platooning has benefits and potential challenges. Reducing congestion and braking/accelerating, and improving safety, traffic flow, and fuel efficiency are some of the expected benefits of platooning (Alam et al., 2015, Bonnet and Fritz, 2000, Nowakowski et al., 2015, Tsugawa et al., 2016, Browand et al., 2004, Lu and Shladover, 2011, Tsugawa et al., 2011, Tsugawa, 2014, Eilers et al., 2015, Lammert et al., 2014, Humphreys et al., 2016, Ramezani et al., 2018). However, platooning, if implemented without caution, may accelerate pavement damage accumulation.

Platooning is expected to cause channelized truck loading application because the lateral positions of trucks in a platoon are expected to be similar as opposed to scattered lateral position of human-driven trucks. This channelized loading is expected to increase the damage accumulation rate and ultimately reduce the pavement’s service life. Noorvand et al., 2017, Chen et al., 2019 acknowledged this challenge and quantified of the impact of trucks’ positionings within a lane. Additionally, the time between two consecutive truck loads will be shorter because of reduced inter-vehicle distance in platoons, which may hinder the self-healing characterization of asphalt concrete (AC) and consequently reduce pavement service life.

This study proposes a centralized control strategy that converts the pavement-related challenges of truck platooning into opportunities. This strategy leverages the auto-pilot technologies in CATs by optimizing the lateral position of each platoon or group of platoons. Fig. 1a demonstrates a default scenario where the platoons are aligned with each other, which may lead to accelerated damage accumulation within pavements. Fig. 1b shows the proposed control strategy, where the platoons communicate with a center (either with a cloud or an infrastructure). In the proposed control strategy, the position of each platoon (or group of platoons) is adjusted to minimize the pavement damage and consequently increase the pavement service life.

The de-centrailized control strategy, where the lateral position of each truck in a platoon was optimized considering the trade-off between the truck aero-dynamics and pavement damage, was introduced by the authors elsewhere (Gungor et al., in preparation). In the de-centralized optimization, while the lateral shift of trucks in a platoon increased the pavement service life, the fuel efficiency of trucks was comprised (Fig. 2). The centralized optimization, on the other hand, maximizes the fuel efficiency because there is no truck’s lateral shifting in a platoon. Application of a centralized strategy, however, may require significant investment because it assumes existence of reliable, centralized vehicle-to-infrastructure (V2I) communication as opposed to the de-centralized strategy’s vehicle-to-vehicle (V2V) communication requirement, which is already part of the platooning technology.

It is important to note that the ideal implementation of platooning requires to study more variables in addition to infrastructure damage. For example, Sun and Yin (2019) investigated a behavioral stability of platooning which addresses the willingness of vehicles to join a platoon. Behavioral stability may especially be important for platoons formed by CATs owned by different companies. Also, this study developed a fair allocation mechanism to redistribute to platooning benefits to incentive vehicles to join a platoon. Calvert et al. (2019) studied the effects of truck platooning on traffic flow. The results did not show any positive impact of platooning on traffic flow. The study also concluded that the platoons of two or three trucks have a negligible impact on traffic efficiency. Furthermore, the scheduling of truck platoons to maximize the efficiency (Milanés and Shladover, 2014, Wang, 2018, Luo et al., 2018) and string stability for improving safety (Larson et al., 2016, Boysen et al., 2018, Chen et al., 2017) have been studied and should be considered for optimal control of platoons.

Section snippets

Pavement design and analysis

To solve the optimization problem given in Fig. 1b, one needs to simulate the effects of any arbitrary positioning of platoons on pavement service life. In other words, the lateral position of each platoon should be an explicit input while simulating the damage accumulation within the pavement. Additionally, the impact of reduced resting time should be considered. To the best of the authors’ knowledge, no pavement design guidelines account for these two variables as explicit inputs. The authors

Optimization

The objective of optimization is maximizing the pavement service life by manipulating the lateral position of truck platoons (Eq. (19)). The service life of a pavement is defined as the year when accumulated damage reaches its serviceability limit. When a pavement reaches its service life, it is rehabilitated to recover its functionality and structural capacity. The serviceability limit, also called a rehabilitation triggering criterion, may be determined in terms of roughness (i.e., IRI),

Life cycle cost analysis

Minimizing the pavement damage by shifting the platoons results in increased service life as compared to channelized traffic with no lateral shifting. In this study life cycle cost analysis (LCCA) was performed to quantify the economic impact of the proposed control strategy on pavements.

Pavement LCCA has two main parts: agency and user costs. Agency cost covers pavement rehabilitation and construction expenses for transportation agencies. In this study, the only agency costs considered are

Case study

Two pavement sections were considered - thin and thick sections. The thin section has 5-in (125 mm) AC layers over a 6-in (150 mm) base layer, while the thick section has 12-in (300 mm) AC layers (wearing surface, intermediate and binder layers) and a 12-in (300 mm) base layer. Elastic moduli for the base and subgrade are 60 ksi (414 MPa) and 10 ksi (69 MPa), respectively. The AC is modeled as linear viscoelastic and Prony coefficients were used for the AC layers. All viscoelastic parameters

Results and discussions

The optimum number of hypothetical grids on the road surface (Fig. 4) is a key variable for the performance of the proposed control strategy. Hence, a sensitivity analysis was performed to determine the grids’ size. Service lives were computed for each grid size, which were used to determine the pavement LCC. The results are presented in Fig. 7. The grid size 1 refers to the channelized traffic; no lateral shifting of platoons. The pavement LCC logarithmically decaying with respect to the

Summary and conclusions

Truck platooning penetration is expected to increase with the advancement in CAT. The potential benefits of the truck platooning include but not limited to, regularizing traffic, reducing congestion, increasing highway safety, decreasing fuel consumption and emission. However, truck platoons may accelerate the pavement damage because of the developed of channelized load application and hindering the healing properties of AC. This study introduces a centralized control strategy that converts

Acknowledgments

This publication is based on the results conducted in cooperation with the University of Transportation Center (UTC), Illinois Center for Transportation of the University of Illinois at Urbana-Champaign. The authors would like to acknowledge the assistance provided by many individuals including Yanfeng Ouyang for his inspiring idea of discretiziation.

The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The

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All for one: Centralized optimization of truck platoons to improve roadway infrastructure sustainability

Highlights

Abstract

Introduction

Section snippets

Pavement design and analysis

Optimization

Life cycle cost analysis

Case study

Results and discussions

Summary and conclusions

Acknowledgments

EURO J. Transp. Logist.

Transp. Res. Part B: Methodol.

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Procedia

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Part C: Emerg. Technol.

Constr. Build. Mater.

Transp. Res. Part C: Emerg. Technol.

Transp. Res. Part C: Emerg. Technol.

J. Cleaner Prod.

Impact of wide-base tires on pavements: results from instrumentation measurements and modeling analysis

Transp. Res. Rec. J. Transp. Res. Board

Impact of wide-base tires on pavements: a national study

Transp. Res. Rec.

Heavy-duty vehicle platooning for sustainable freight transportation: a cooperative method to enhance safety and efficiency

IEEE Control Syst. Mag.

Fatigue Failure Testing in Section f

Fuel saving achieved in the field test of two tandem trucks. California PATH Program

Importance of heterogeneity in asphalt pavement modeling

J. Eng. Mech.