Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles

It is agreed that connected vehicle technologies have broad implications to traﬃc management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traﬃc capacity, fuel eﬃciency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traﬃc ﬂow by enhancing vehicle fuel eﬃciency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.


Introduction
e traffic capacity of urban road network is restricted by the time delay, potential safety hazards, and environmental pollution caused by traffic congestion. According to the US Department of Transportation Statistics issued in 2019, 27% of highways in the United States are blocked at the peak of urban traffic and 54% of vehicles are in the state of congestion [1]. Intersections, as important nodes of urban road network, cause 80% of these urban traffic congestions [2]. erefore, alleviating urban traffic congestion by taking the intersection as the optimization object is a challenging work. e realization of spatiotemporal trajectory optimization mainly includes two parts: (1) realizing the dynamic speed control of multiple vehicles in the longitudinal direction; (2) achieving the cooperative lane-changing control of multiple vehicles in the horizontal direction. In the intelligent traffic system (ITS) strategic plan published by the US Department of Transportation in 2010, dynamic speed coordination based on spatiotemporal trajectory was denoted as one of the important methods for traffic flow optimization of road network [3]. Grumert and Tapani [4] established a variable speed limit (VSL) algorithm based on the traffic occupancy.
e results show that VSL can increase traffic flow by 16%. Jo et al. [5] updated a VSL algorithm by detecting vehicles at multiple stations from the perspective of road safety, which solved the guidance delay problem. With the development of cooperative vehicle infrastructure system (CVIS) and connected vehicles (CVs), the problem of discontinuous dynamic speed limit can be solved by continuous dynamic speed control [6]. Considering the random traffic conditions, Zhu and Ukkusuri [7] proposed a method to realize dynamic speed limit control through RL. In this method, a link based dynamic network load model was established in the environment of CVs, which can reduce the total travel time by 18%. Aldana-Muñoz et al. [8] proposed a method by combining environmental driving and speed guidance to optimize the continuous linear spatiotemporal trajectory.
e experimental results show that the linear spatiotemporal trajectory optimization can stabilize the change of disturbance on traffic flow. Wang et al. [9] proposed a speed guidance model based on model predictive control (MPC), by which the spatiotemporal trajectory can be constructed in advance. Wei et al. [10] proposed a cooperative optimization method for multivehicle spatiotemporal trajectories, which is established based on the simplified Newell linear carfollowing model. Considering different road environments and weather conditions, De Mello and Chiodi [11] proposed a fuzzy logic control model of spatiotemporal trajectory, which increases the effectiveness of decision-making process by 13%. Liang et al. [12] presented a method to optimize the common spatiotemporal trajectories for both autonomous vehicles and manual driving vehicles. With the proportion of autonomous vehicles increasing, average delay time is reduced by 25% and average stopping time is reduced by 47%. Wang et al. [13,14] designed a joint control model for CVs and traffic signal, which aims to optimize vehicular platoon and signal timing to improve traffic efficiency at intersections.
e simulation results show that the joint control model can reduce the stopping time by 54%. Mirheli et al. [15] proposed a distributed cooperative control logic based on spatiotemporal trajectory, which can be applied to unsignalized intersections. In this study, vehicles with this logic application scheme can pass through the unsignalized intersection in optimal trajectories without collision. Wang et al. [16] developed a cooperative optimization method of spatiotemporal trajectory with multiple intersection phases. In this method, a lower bound estimator based on dynamic load network was constructed to improve robustness and stability of vehicle dynamic network.
Urban roads or expressways with single lane or dual lane were mainly taken in the above studies; however, the multilane spatiotemporal trajectory optimization is still lacked in the research. e main challenge of vehicles in the multilane spatiotemporal trajectory lies in how to solve its complex lane-changing problem. Moreover, accidents caused by lane-changing behaviors make the multiple lanes occupied, which makes the road congestion more serious [17]. Considering the risk of lane-changing produced by vehicle-to-vehicle (V2V) communication delay, Hongil and Jae-Il [18] proposed an oriented bounding box (OBB) method which reduces communication delay by defining the lateral and longitudinal gradient controllers. Li et al. [19] proposed a new lane-changing model based on CVs to evaluate the critical vehicle distance in lane-changing process. e safety potential field theory was integrated, and the vehicle movement state under different speed and acceleration conditions is dynamically presented. Finally, the lanechanging route can be determined through the selection of spatiotemporal trajectory. e majority of studies provide ideas and theoretical support for multivehicle cooperative lane-changing based on spatiotemporal trajectory. However, due to the massive amount of data calculation and high system complexity in multilane spatiotemporal trajectories, the common multilane road section traffic efficiency problem has not been effectively solved and improved through the optimization of multiple single lanes method.
To solve the above problems, MSTTOM for connected vehicles is established in this paper. e spatial temporal trajectories are generated and combined with vehicular current state information and the signal timing information. In addition, during the driving process, the trajectories can be optimized by RL. Moreover, the vehicle can change the lane and update its trajectory through the cooperative lanechanging strategy designed in this model. e significant contribution of this research is that vehicles can pass through intersections in optimal spatiotemporal trajectory and further solve the congestion problem in current multilane road section. e rest of this paper is organized as follows: e typical application scenario and the structure of the method are introduced in Section 2. e specific content of MSTTOM is described in Section 3, where the trajectory is generated and optimized.
e simulation experiment results through SUMO/Python are presented and analyzed in Section 4, and the stability of headway, fuel consumption rate, pollutant emission, and calculation time are also selected as the evaluation indexes. Finally, Section 5 concludes the paper.

Application Scenario.
e upstream section of the intersection, as the application of multilane scenario, is covered by vehicle-to-everything (V2X) wireless communication and shown in Figure 1. e vehicular trajectory from upstream to intersection process will be dynamically calculated based on MSTTOM.

System Structure.
e flow chart of MSTTOM is shown in Figure 2. e procedure can be described as follows: the traffic signal and vehicular driving information is collected firstly by road-side unit (RSU) and on-board unit (OBU), so that the target lane and arrival time at current intersection can be calculated; secondly, the optimal trajectory is generated through connected vehicular state and constraints, where traffic capacity of current intersection can be optimized online through the cooperative lane-changing strategy; and finally, every connected vehicle can pass through the intersection with least travel time through the optimization result of RL. e spatiotemporal trajectory and the optimized state are clarified in Figure 3.

Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM)
Firstly, state vectors defined in this paper contain the information of vehicular driving, such as position, speed, and acceleration, and the information of traffic signal timing. Secondly, the cost function and constraints of vehicular trajectory are formulated. Finally, the optimal vehicular trajectory is dynamically calculated through maximum principle and RL.  from the right side to the left side (the target vehicle is selected as the reference) and the n-th vehicle from the downstream to the upstream, respectively. erefore, we define the state vector of vehicle C mn as x mn (t): where (x mn (t), y mn (t)) is the position of vehicle C mn at time t, v mn (t) is the driving speed of vehicle C mn at time t, and v′ mn (t) is the lateral speed of vehicle C mn at time t. e method input is the vehicle acceleration u mn (t), which can be expressed as follows: where u mn (t) is the longitudinal acceleration of vehicle C mn at time t and u′ mn (t) is the current lateral acceleration of vehicle C mn at time t.
Meanwhile, the signal timing information of vehicle target lane φ mn (t) and the traffic flow information I m−1 and I m−1 (t) of adjacent lanes m + 1 and m − 1 are defined as where φ mn (t) is the target phase state of vehicle C mn where N is the total number of vehicles, N max m is the maximum number of vehicles in lane m, N min m is the minimum number of vehicles in lane m, and L is the length of road section.
e system state equation is described as

Cost Function.
In order to ensure that the vehicle can accurately follow the designed trajectory of the system, fixed and variable costs in the process from upstream to downstream are considered and formulated, which can be represented by where t out mn is the time when vehicle C mn leaves the road section, t 0 mn is the time when the vehicle C mn enters the road section, g(x mn (t out mn )) is the fixed cost of the process, and t out mn t 0 mn h(x mn (t), u mn (t))dt is the variable cost of the process. e fixed cost includes the cost of fixed items (such as the driving distance and expected speed at the intersection) and the travel time of the road section. e process can be described as  Journal of Advanced Transportation where t out mn is the target time of vehicle C mn driving out of the road section, v out mn is the expected speed of vehicle C mn at the intersection, m is the target lane of vehicle, and d is the lane width. w 1 , w 2 , w 3 , w 4 are the weights of the travel time of the road section, driving length of the road section, expected speed at the intersection, and the target lane at the intersection, respectively. w 1 , w 2 , w 3 , w 4 ∈R+.
Variable cost involves acceleration and deceleration of the vehicle driving in longitudinal and lateral lanes. Its function can be expressed as where w 5 and w 6 are the weights of energy changes caused by longitudinal and lateral acceleration of the vehicle, receptively. w 5 , w 6 ∈R+. χ(u mn (t)) is the Heaviside function of longitudinal acceleration of networked vehicles and can be used to separate the variable cost induced by acceleration in longitudinal deceleration process: t free mn denotes the time of vehicles passing through the intersection under the condition of low-density traffic flow, also known as the time for vehicles C mn leaving the road without restriction. e time consists of four parts: initial time t 0 mn , time t acc mn at acceleration state, time t c.s. mn at constant speed state, and time t de c mn at deceleration state, which can be described as follows: where t acc mn , t c.s. mn , and t de c mn can be expressed as where v lim 0 is the speed limit of the road section, v lim m is the speed limit at the downstream exit of the lane m, u +max mn is the maximum acceleration of vehicle C mn , and u −max mn is the maximum deceleration of vehicle C mn .
However, it is impossible for the urban road section to maintain a low-density state. erefore, connected vehicles have to pass through current intersection as a temporary platoon. e candidate time t temp mn of vehicle C mn can be calculated as where t h2h m is the minimum headway between two adjacent vehicles in lane m at the downstream exit of the section.
In order to improve the traffic capacity of intersections, the target time t out mn of vehicle C mn at current intersection is controlled within the passable green signal, which can be selected according to where ξ m is the set of green light periods of lane m, and floor (t) is the function of rounding down. e expected speed of vehicles is defined as the speed limit at the downstream exit to ensure the maximum efficiency of vehicles and can be described as In conclusion, the cost function can be integrated as Journal of Advanced Transportation 3.3. Constraints. In order to minimize the above cost function J, seven constraints, namely, initial vehicular state, vehicle spacing, speed, acceleration, jerk, signal timing, and wireless communication, should be satisfied in the optimization problem.

Initial Vehicular State.
Suppose that a connected vehicle C mn enters the road section with detectors; when n ≥n max , the counter will reset. e initial state of vehicle C mn is defined as

Vehicle
Spacing. e vehicle will inevitably be in the vehicle-following state in the driving process.
erefore, vehicle C mn on the lane m and the front vehicle C m (n-1) should ensure a certain displacement in space and time. e safety constraint can be formulated as where τ mn is the time displacement of vehicle C mn and the front vehicle C m (n-1) , and d mn is the spatial displacement of vehicle C mn and the front vehicle C m (n-1) .

Speed.
In order to ensure the safety of the vehicles in the road section, the speed constraint is applied to them. e longitudinal speed constraint embraces the speed information involving speed limit within the section and the minimum speed 0. us, we have For the lateral speed constraint, the vehicle deflection angle is mainly constrained by vehicle dynamics, which can be expressed as where α max is the maximum angle through which the front wheel of the vehicle can turn to the left, α mn (t) represents the steering angle of the front wheel of the current vehicle, and α max represents the maximum angle through which the front wheel of the vehicle can turn to the right. erefore, the lateral restraint condition of the vehicle is shown as 3.3.4. Acceleration. In order to guarantee that the engine can provide enough power and the brake pads can provide the vehicle enough power limit, the acceleration or deceleration of the vehicle should be specified as a constraint. e acceleration constraints can be expressed as where u max is the acceleration of the maximum longitudinal braking deceleration of the vehicle, u max is the acceleration of the maximum longitudinal acceleration of the vehicle, u′ max is the maximum transverse acceleration of the vehicle to the left, and u′ max is the maximum acceleration of the vehicle to the right.

Jerk.
e jerk constraint is the change rate constraint of vehicle acceleration, also known as the impact constraint or comfort constraint. e purpose of jerk constraint is to eliminate the negative impact of the acceleration change in the driving process. e jerk constraint is shown as where j max represents the maximum longitudinal backward jerk, j max represents the maximum longitudinal forward jerk, j′ max represents the maximum lateral left acceleration, and j′ max represents the maximum lateral right acceleration.

Signal
Timing. e signal timing constraint of vehicles can ensure that vehicles avoid violations when passing through the intersection, which can be represented as where τ mn (t) and P lp|mn (t) represent the time delay and the packet loss probability of the vehicle C mn at time t.

Solution Method Based on Maximum Principle.
MSTTOM is solved by Pontryagin's maximum principle, and the Hamilton function of the problem is established as where λ is defined as the costate vector of vector x, which represents the additional cost of the change of J caused by the small change zx of vector x.
In the admissible set U, the minimum input u * of the cost must satisfy the minimum state of the Hamilton function, which can be expressed as follows: erefore, the Hamilton function of MSTTOM is shown as where the variation of the lateral movement trajectory is optimized in the process of vehicular cooperative lanechanging.
In the process of solving the problem, the state vector λ should meet the condition of the fixed cost, which is shown as

Cooperative Lane-Changing Optimization.
In order to accurately describe the process of cooperative lane-changing strategy, the request of the vehicular lane-changing is displayed in Figure 4. e vehicle C mn , selected as the subject vehicle, is a red one in the middle of lane m and ready to change lanes from the current lane m to the target lane m + 1. Besides, yellow vehicles are defined as the primary threat vehicles of C mn , orange vehicles are defined as the secondary threat vehicles of C mn , and blue vehicles are defined as the non-threat vehicles of C mn . If target lane is not the adjacent lane, it can be decomposed into multiple lane-changing processes. e optimal trajectory is loaded into the traffic environment, and the potential lane-changing conflict safety inspection can be carried out. e flow chart of lane-changing safety inspection is shown in Figure 5.
If two adjacent vehicles receive the same requests concurrently, there is a priority for the vehicle with a higher speed. If adjacent vehicles based on daily driving habits have similar speed, there is a priority for the vehicle in the left lane.
Different traffic saturation should be satisfied in the cooperative lane-changing strategy. e higher the saturation involved in the case is, the higher the complexity of the lane-changing strategy will be supposed. erefore, the case of higher saturation is discussed as follows: (i) e subject vehicle C mn initiates a lane-changing request to the target vehicle C (m+1)n after the target gap selected. (ii) e target vehicle C (m+1)n and its rear vehicle C (m+1)(n+1) adjust their speeds to confirm a safe gap for vehicle C mn .

Trajectory Optimization Based on RL.
In order to improve the efficiency of MSTTOM, this paper designs an optimization algorithm based on the RL paradigm, which can quickly match the optimal trajectory. e optimization algorithm is formulated with the current position and speed of vehicles. e target lane and time period are modeled as inputs, and the sets of vehicle acceleration are formulated as outputs. When the lane-changing request is initiated, the vehicle can be connected to the trajectory of the cooperative lane-changing strategy through the RL matching network. Once the lane-changing is completed, the spatiotemporal trajectory will be matched by the RL to achieve the multilane trajectory optimization. e state vector of MSTTOM satisfies Markov property; that is, the next state s t+1 of the system is only related to the current state s t , but not directly related to the preceding state.
us, the state vector can be shown as follows: where P is the probability matrix of transition between state.

Journal of Advanced Transportation
Lane-changing safety inspection   Journal of Advanced Transportation rough integration, the set of action vectors will be presented in the form of trajectory data. e action vector a t at current state s t is shown as Five tuples (S, A, P, R, c) are defined to describe the process of MSTTOM. Among them, S is the state set, which includes the current state of vehicles and the traffic flow state in the road section; A is the set of executing actions, that is, the set of lateral and longitudinal acceleration output by optimization; R is the reward function of the process, which is negatively linear with the cost function J; and c is the discount factor when calculating the value function. For a fixed policy π, the value function v π (s) can be calculated as An action value function q π (s, a) can be calculated by defining the value of each action a, which is described as Bellman optimal recursive equations of the optimal state value function v * s and the optimal action value function q * s can be calculated by introducing Markov property into (31) and (32), which can be shown as follows: where s ′ and a ′ are the state and action of the next moment, respectively. e optimal strategy derived by maximizing the above functions can be represented as , a) , e function can be calculated by Q-learning algorithm, and the algorithm process is shown in Algorithm 1.
In Algorithm 1, s T is the termination state in the fifth row.
e current value function of the subsequent state estimation is used to update the optimal action function in the seventh row. In order to enhance the diversity of the exploration ability of the algorithm, the specific formula is shown as s, a), where N (a) is the total number of actions, ε-greedy is the optimal action strategy taken based on 1-ε probability, and random action is the policy of ensuring the possibility of being selected for each action according to the probability of ε.

SUMO/Python Simulation and Analysis
In this section, we will simulate and analyze the proposed MSTTOM through the SUMO/Python-based platform to verify the feasibility and effectiveness of the scheme. Meanwhile, the optimal level of mobility at the signal intersection will be guaranteed in the experiment.  Journal of Advanced Transportation

Experiment Platform Based on SUMO.
SUMO is microscopic traffic simulation software [20]. e simulation of SUMO is discrete in time and continuous in space, and the location of each vehicle is described internally. In SUMO, the vehicle model is collision free, so that the variation caused by incomplete model is not allowed to appear in the simulation. SUMO allocates appropriate routes to vehicles considering different traffic demands through Dijkstra algorithm. e structure of SUMO map is shown in Figure 7.

Experimental Scheme.
In the paper, the Netedit of SUMO is used to carry out the multilane experimental scenario based on Figure 1, and the simulation scenario is shown in Figure 8. e intersection is a typical cross intersection, and the traffic light is set as a typical four-phase signal light. Due to the similar spatial characteristics of each section, the 4-lane section from west to east is selected for the experiment.
In the simulation process, the benchmark scheme in the software, the glidepath prototype application (GPPA) scheme [21], and the MSTTOM scheme proposed in this paper are used to simulate and compare the performance of traffic flow conditions with various vehicle saturation 0.6, 0.8, and 1.0. Vehicles simulation diagram is shown in Figure 9. e three schemes compared in this paper are defined as follows: (i) e benchmark scheme: in this scheme, the traditional human driving vehicle model in SUMO software is adopted, which means that there is no controlled vehicle and no network communication. erefore, the driving situation of vehicles under the current traditional driving habits can be simulated in this scenario. Simulation conditions and parameter settings for these three schemes are shown in Table 1. Meanwhile, car-following state is simulated by Krauss car-following model [22].
In order to eliminate the influence of unrelated factors in the scenario, the simulation settings in SUMO are simplified by considering the following conditions: (i) In the simulation environment, there are the same model, size, and kinematic characteristics with all vehicles. (ii) ere is no special weather effect in the simulation environment, and the road adhesion coefficient remains constant. (iii) e roads are straight in the simulation environment. ere are no ramps, nonmotorized vehicular lanes, or parking spaces, which lead to vehicles entering or exiting. (iv) ere is no inclination change in the road section, which means that the vehicle will not travel uphill or downhill. (v) e arrival probability of vehicles in the simulation environment is stochastic and obeys the Poisson distribution.
e simulation process based on SUMO is shown in Figure 10. It can be seen that the red vehicle from the west to the east attempts to go straight through the intersection within the green duration from the rightmost lane. In this process, the red vehicle completes cooperative lane-changing with vehicles in the left lane through MSTTOM.

Result Analysis.
rough the simulation of SUMO software, the output data of the vehicle can be exported and analyzed. (2) While (s t � s T ) (3) {Select the initial state s 0 and action a 0 according to the ε-greedy strategy; (4) While (s t � s T ) (5) {Select the action a t the state s t according to the ε-greedy strategy, get reward r t and the next state s t+1 ; (6) q(s t , a t )←q(s t , a t ) + a(r t+1 + c max a q(s t+1 , a) − q(s t , a t )); (7) s t � s t+1 ; } } (8) Get the optimal strategy π(s) � arg max   According to the traffic control characteristics of signalized intersections, the increase of effective green time and saturation flow rate will directly lead to an increase in the number of vehicles passing through the stop line at the intersection, which means that the throughput of the intersection will be improved.
e vehicle fuel efficiency and corresponding profit of the whole system under MSTTOM are analyzed in Figure 13.
e results of T-test show that there is a significant statistical difference in the vehicle fuel efficiency data between the MSTTOM scheme and the benchmark scheme. rough observation, it can be seen that the use of MSTTOM can effectively improve the vehicle fuel efficiency and reduce the vehicle fuel consumption.
After calculation, the vehicle fuel consumption profit is shown in Figure 14. In the case of different vehicle saturation, there is a significant fuel efficiency benefit in GPPA scheme and MSTTOM scheme. Comparing GPPA scheme with the scheme of optimization method proposed in this paper, the advantages of MSTTOM scheme in improving traffic mobility, enhancing vehicle fuel efficiency, and reducing pollutants emissions are obvious.
In MSTTOM scheme, the system calculation time analysis under different iteration time steps and optimization time spans is demonstrated in Figure 15. e maximum calculation time is up to 0.93 when the optimization time span is 50 seconds, and the iteration time step is 0.2 seconds. In the simulation process, the optimization time spans of all CVs are less than 50 seconds, and each iteration time step is greater than 0.2 seconds. erefore, the calculation time of the system is less than 1 second; that is, the effective performance of the system time meets the requirements of realtime optimization. e objectives of ensuring the optimal mobility of vehicles at signalized intersections, enhancing vehicle fuel efficiency, and reducing pollutants emissions can be achieved in MSTTOM scheme. Compared with the existing GPPA algorithm, the strengths of MSTTOM are as follows: (i) e function of multilane cooperative lane-changing strategy under the condition of random traffic flow is well realized in MSTTOM scheme. (ii) Compared with the benchmark scheme, the mobility of vehicles within the road section is improved, the vehicle fuel efficiency is enhanced by 32%, and the emissions of pollutants are reduced by 17% in MSTTOM scheme. (iii) Compared with the advanced GPPA scheme, there is a 24% reduction in the vehicle fuel consumption in MSTTOM scheme. (iv) e traffic fluctuation caused by the intersection signal control is smooth and the traffic flow in the road section is more reasonable in MSTTOM scheme. (v) For the randomness in the traffic process, the MSTTOM scheme shows its superiority in robustness than other existing schemes. (vi) e calculation time in MSTTOM scheme is less than 1 second, and the distribution is mainly concentrated in 0.3 seconds to 0.7 seconds, which satisfies the requirements of real-time optimization.

Conclusions
is paper proposes a new scheme, MSTTOM, to ease traffic pressure and reduce traffic congestion in multilane road sections. e problem of low traffic efficiency at the multilane intersections is studied and analyzed in this paper. Four stages of work, namely, literature summary, scenario and structure introduction, method optimization design and experimental validation, and optimization objects determination, are conducted. e research results and innovations are as follows: (i) A vehicular lane-changing method based on V2X is proposed. Firstly, the lane-changing environment information is analyzed to classify and define the potential threat vehicles in the driving environment. Secondly, the lane-changing behaviors of potential threat vehicles are analyzed, and conflict detection is carried out to determine the priority order in the lane-changing process. en, lane-changing gap and target cooperative vehicles are determined and declared. Finally, the spatiotemporal trajectories of related vehicles are updated.
e vehicular cooperative lane-changing strategy lays a foundation for the reasonable planning of MSTTOM. (ii) is paper designs a multilane spatiotemporal trajectory optimization method. Based on the optimal control theory, the state control models of CVs driven by Pontryagin's maximum principle are constructed. e cost function is formulated by continuous functional target completion and  optimal profit. e constraints in line with the actual situation of the vehicle are also established. Finally, the trajectories data are optimized by Q-learning algorithm to achieve the real-time optimization.
(iii) SUMO software is used to verify the feasibility and effectiveness of MSTTOM by comparing it with GPPA scheme of FHWA in the United States. e experimental results demonstrate that MSTTOM can effectively optimize the traffic flow, improve the vehicle fuel efficiency, reduce pollutants emissions, and improve the traffic efficiency of vehicles at intersections.

Data Availability
All data generated or analyzed during this study are included in this article.

Conflicts of Interest
e authors declare that there are no conflicts of interest regarding the publication of this paper.