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Vehicle platoon composed of a group of connected and automated vehicles (CAVs), a coordinated movement strategy, has been widely proposed to address a range of traffic problems. The motion of vehicle in the platoon passing signalized intersections can significantly affect their total trip time and fuel consumption. With the development of advanced communication technology such as V2V and V2I, CAVs can automatically obtain and use the upcoming traffic light timing information to find optimal velocity profiles that can avoid idling at red lights. This paper proposes an optimal velocity control and separation strategy for the platoon to minimize the trip time and reduce fuel consumption as much as possible. Simulation results show that with the introduction of the velocity control and separation strategy, the total trip time and fuel consumption decrease by 19.2% and 18.1%, respectively. Thus the effectiveness of the proposed strategy is demonstrated.

In the connected and automated vehicles (CAVs) system, vehicles are capable of sharing information and sensing local environment with each other via the advanced communication technologies (e.g., V2V and V2I). The vehicles’ information (e.g., location and velocity) and the road transportation infrastructure information (e.g., the traffic light timing, including the phase cycle length, the green phase length, and the start of the first green phase) will be obtained by every vehicle. After receiving such information, the internal decision-making mechanism will make corresponding driving decisions and then achieve the level of automatic driving. Under this circumstance, all CAVs will be platooned through communication and automated control technologies [

All these potential benefits are linked to the expectation that CAVs can significantly improve traffic capacity, efficiency, and safety [

World Oil Outlook 2016, issued by Organization of Petroleum Exporting Countries (OPEC), predicted that most of the oil consumed today and in the future will come from the road transportation sector. By 2040, the road transportation sector will represent 44% of global oil demand [

Even if vehicle platooning has a certain advantage in traffic capacity improvement and fuel-saving, a huge wastage of traffic capacity and fuel will occur due to the stoppage at signalized intersection during its red phase. Idling at red lights will decrease traffic capacity and increase fuel consumption from many aspects:

Therefore, a number of benefits can be obtained in limiting the idling time at red light. These benefits include increasing traffic efficiency, saving in fuel use, reduction in exhaust emissions, and even vehicle life extension. In recent years, the exponential increase in the number of vehicles in urban city has resulted in congestion and more fuel consumption at intersections. Traffic efficiency improvement and fuel economy have been paid more attention than ever. Thus, from the perspective of traffic efficiency improvement, travel comfort, and traffic energy conservation, it is of great importance to keep the traffic flow smooth and reduce red light idling.

Besides the fuel wastage at intersection due to the operation of signals, fuel consumption can also be affected by other factors along the entire trip, such as cruising speed, the intervehicle distance, and traffic conditions [

The rest of this paper is organized as follows. Section

In this section, the conceptualization of the vehicle platoons in a short length is firstly introduced. Then the objective function of reducing the platoons idling at red lights is formulated. Finally, the formula for the minimum fuel consumption is introduced.

Maiti et al. provided a detailed concept of vehicle platoon [

A platoon of CAVs is actually a network of dynamical systems, S. E. Li et al. presented a four-component framework to model, analyze, and synthesize a platoon of CAVs from the perspective of multiagent consensus control [

Platoon driving schematic diagram.

The process of vehicle stopping at the red light and leaving when the light turns green is actually the stop-and-go motion, as mentioned in Section

The schematic of the candidate trajectory and velocity of the leader vehicle at each intersection is shown in Figure

The traffic light information of

Let

Schematic of the trajectory and velocity of leader vehicle.

Our goal is to find a permit velocity of platoon which aids in minimizing idling at the red light. The problem of idling at the red light can be transformed into the platoon’s waiting time at the traffic light. Based on the signal timing information and platoon information, total waiting time of all vehicles at all traffic lights can be calculated as

Vehicles in the same platoon share the same velocity;

Before studying the influence of idling at red light and velocity on fuel consumption, we introduce the fuel consumption model. Many efforts have been made to understand the relationship between traffic activities and fuel consumption rate; many researchers modeled fuel consumption as a function of vehicle load and average speed [

where

Hence, during the whole trip, for each vehicle in the same platoon, the fuel consumption can be calculated as

where

Assuming that all the vehicles run at constant speed in each segment ignoring the acceleration or deceleration process, then, for all vehicles in a platoon passing all the intersections without idling, the total fuel consumption can be calculated as

The objective of this paper is to minimize two performance indexes

Y. Zheng et al. analyzed the complexity of a known green light optimal velocity (GLOV) problem, finding, e.g., optimal velocity that can avoid idling at red lights and minimize the trip time [

In order to ensure effective solution to our proposed problem, approximation algorithm is proposed in the following optimization strategy. We consider finding optimal velocity profiles for each platoon to save fuel consumption as much as possible while ensuring the improvement of traffic efficiency, that is, to let the maximum number of vehicles pass without idling at red light as much as possible even if the current velocity does not guarantee the lowest fuel consumption.

This section focuses on the introduction and analysis of optimization strategy to solve the problem defined in Section

To ensure that all the vehicles in the platoon could pass all the traffic lights without idling at red lights, it must be firstly met that the leader vehicle in a platoon can pass through the intersection during its green phase. The allowable velocity bound for a platoon is shown in Figure

Let

Allowable velocity bound for platoon.

For example, if

However, if

To sum up, the leader vehicle will find the possibility of passing during

The detailed process of separation strategy of platoon will be introduced in this section.

According to the concept of vehicle platooning, all vehicles in the same platoon share the same velocity, which means the follower vehicles’ trajectory will be parallel to the trajectory of the leader one. If we draw a line parallel to the

Schematic of the separation strategy.

Let

If

The maximum number of vehicles that can pass during the first green phase at the first upcoming intersection can be calculated as

As introduced in Section

Vehicles before the separation point (SP) belong to the original platoon, which will pass the first intersection during its first green phase. The new platoon consists of those vehicles behind SP that need to decelerate to

Let

The fuel consumption at different velocity.

On the one hand, our first goal in this paper is to improve traffic efficiency by avoiding idling at red light; at each section for a platoon, we can choose the maximum passing velocity

The process of our proposed velocity control and separation strategy.

Take the first intersection, for example; one optimal solution to the problem defined in Section

Check if

Compare the maximum platoon size

Compare the platoon size

where

If

The original platoon is separated into two new platoons: platoon 1.1 and platoon 1.2. To ensure that as many vehicles as possible can pass the intersection within a green phase, the new platoon 1.1 will pass the intersection with velocity

For the remaining intersections, the process to find the optimal velocity solution is almost similar to the above steps, except that at Step

The route is assumed to have 4 intersections, and the parameters of the traffic light location and timing information are shown in Table

Traffic light location and timing information.

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Platoon and road information.

Parameter | Value | Unit |
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| 20 | veh |

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Parameters value of fuel consumption.

Coefficient | Value | Unit |
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Firstly, we study the case of conventional strategy; the separation strategy is not activated.

In the simulation, we consider a platoon of 20 vehicles. Figure

Trajectory of vehicles of platoon without separation strategy.

To solve the deficiency of conventional strategy, we propose a velocity control and separation strategy that takes into account the traffic efficiency and fuel-saving simultaneously as introduced in Section

Trajectory of vehicles of platoon with separation strategy (minimum fuel consumption).

In order to better understand how our proposed velocity control and separation strategy achieves fuel-saving under the premise of guaranteeing that maximum number of vehicles can pass the intersection without idling, we implement another simulation in which all the vehicles choose the maximum velocity to pass intersections without considering fuel-saving. This simulation is almost similar to simulation 2 except that all vehicles pass all the intersections with maximum passing velocity

Vehicles’ trajectory of platoon with separation strategy (

Comparing with our proposed separation strategy that considers fuel-saving, we find that the main difference between these two different situations is reflected at the third intersection. Under the premise of ensuring that all vehicles can pass without idling, our proposed separation strategy chooses the velocity that can minimize the fuel consumption, but this strategy chooses the maximum velocity to pass the intersection.

Figure

The total fuel consumption and the total travel time of each vehicle.

One can see that the fuel consumption of each vehicle decreases dramatically with our proposed separation strategy. For the platoon with 20 vehicles, the total fuel consumption decreases by 18.1% comparing with the conventional strategy. By minimizing the fuel costs, we also implicitly increase some of the societal benefits of our proposed platoon separation strategy. Minimizing fuel consumption is equivalent to minimizing emissions [

With our proposed separation strategy, the total travel time decreases by 19.2% compared with the conventional strategy. The first ten cars are particularly noticeable thanks to velocity control and separation strategy. In other words, traffic efficiency has improved. And longer platoons are associated with more efficient road utilization since the vehicles within a platoon drive closer together. The reduced space utilization as a result of platooning might help improve the traffic throughput.

Intuitively, passing with the maximum velocity means that the travel time is the minimum. It is worth noting that this conclusion is only valid for a single intersection. Interestingly, for multiple intersections, the total travel time is not necessarily the smallest even if the maximum velocity is selected at each intersection, which is determined by the difference of signal phase between two consecutive signalized intersections. If the green phase difference of two adjacent intersections is very gentle, vehicles cannot pass these two intersections continuously during the same green phase due to the maximum velocity limit. In other words, even if the maximum speed is selected at the previous intersection, the platoon can only pass the consecutive intersection until its next green phase by reducing more velocity. This will probably lead to a decrease in the overall average speed, which in turn increases the total travel time. As we can see in Figure

A velocity control and separation strategy aimed at avoiding idling at red light and reducing fuel consumption as much as possible was proposed in this paper. The simulation results suggested that our proposed strategy effectively improves the performance of the platoon. The total travel time and the fuel consumption were reduced by 19.2% and 18.1%, respectively. The ultimate objectives of platooning are to enhance highway safety, improve traffic utility, and reduce fuel consumption. The main novelty and contribution of this work is providing an optimal platoon velocity control method and a separation strategy at signalized intersection that considers both traffic utility improvement and fuel economy. Additionally, it is worth noting that the using scenarios of this strategy involve multiple intersections instead of only one single signalized intersection, and this strategy can be applied to full autonomous or semiautonomous vehicles in the future.

Several extensions to the present study are desired in the future. Some assumptions made in this study could be violated, and we caution against generalizing the results. We would like to mention that the simulation results are based on the assumption that all vehicles run at a constant velocity ignoring the acceleration or deceleration process. Actually, the fuel consumption and the state of the platoon system may change with the acceleration or deceleration process. Additionally, this paper only considers the separation strategy of a single static platoon and ignores the dynamics of platooning process between multiple platoons. The dynamics increase the complexity of the decision-making process.

Therefore, each problem discussed above presents an important and very challenging research topic. In the future work, the impact of acceleration and deceleration process on fuel consumption and travel time needs to be investigated to examine the validation of the simulation results. Nevertheless, this paper provides an explicit strategy to better improve the traffic efficiency and fuel-saving in a vehicle platoon.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

This work was supported by the National Natural Science Foundation of China (Grant no. 61573098).

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