Growth in the number of vehicles causes excessive traffic congestion and travel delay on urban roads, especially at signalized intersections. The recent advances in connected and automated vehicle (CAV) technology and the upgrade of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Vehicle (I2V) communications have been proposed as potential solutions to efficient and effective urban transportation. CAVs enable the capability to share data, communicate with neighboring vehicles and roadside infrastructures, and connect to traffic control systems, and therefore offer the benefits to reduce congestion and pollution levels and improve comfort and road safety. CAV platoons can coordinate member vehicles for a common goal in platooning. In this way, vehicles can be cooperative to accelerate/decelerate facing the traffic signal controllers on urban roads. The challenge is posed by the diversity of signal control approaches, such as fixed-timing, actuated, and adaptive signals. However, the benefits and effectiveness of CAV platoon trajectory optimization for all those various systems in the vicinity of signalized intersections remain unclear in research and also practice...@en

A joint control approach that simultaneously optimizes traffic signals and trajectories of cooperative (automated) vehicle platooning at urban intersections is presented in this paper. In the proposed approach, the signal phase lengths and the accelerations of the controlled platoons are optimized to maximize comfort and minimize travel delay within the signal cycle, subject to motion constraints on speeds, accelerations and safe following gaps. The red phases are initially considered as logic constraints, and then recast as several linear constraints to enable efficient solutions. The proposed approach is solved by mixed integer linear programming (MILP) techniques after linearization of the objective function. The generated outputs of the MILP problem are the optimal signal timings and the optimal accelerations of all vehicles. This joint control approach is flexible in incorporating multiple platoons and traffic movements under different traffic demand levels and it does not require prespecified terminal conditions on position and speed at the signal cycle tail. The performance of the proposed control approach is verified by simulation at a standard four-arm intersection under the balanced and unbalanced vehicle arrival rates from different arms, taking the released traffic movement numbers, turning proportions, signal cycle lengths and the controlled vehicle numbers into account. The simulation results demonstrate the platoon performance of the joint controller (such as split, merge, acceleration and deceleration maneuvers) under the optimal signals. Based on the simulation results, the optimal patterns of trajectories and signals are explored, which provide insights into the optimal traffic control actions at intersections in a cooperative vehicle environment. Furthermore, the computational performance of the proposed control approach is analyzed, and the benefits of the proposed approach on the average travel delay, throughput, fuel consumption, and emission are proved by comparing with the two-layer approaches using the car following model, the signal optimization models, and the state-of-the-art approach.

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Due to the manoeuvre complexity, models describing the platoon formation process on urban roads are lacking in the literature. Inspired by flocking behaviours in nature, we proposed a two-dimensional model to describe connected automated vehicle (CAV) group dynamics based on the potential theory, which is composed of the elastic potential energy for the inter-vehicle spring-mass system and the cross-section artificial potential field. The inter-vehicle elastic potential energy enables CAVs to attract each other at long distances, and repel each other otherwise. It also generates incentives for lane changes. The cross-section artificial potential field is able to mimic the lane-keeping behaviour and creates resistance to avoid unnecessary lane changes at very low incentives. These modelling principles can also be applied to human-driven vehicles in a mixed traffic environment. The behavioural plausibility of the model is demonstrated analytically and further verified in a simulation of typical driving scenarios.

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An integrated approach for optimising traffic signals and cooperative vehicle trajectories at urban intersections is proposed. The upper layer determines the optimal signals using enumeration and the lower layer optimises trajectories under each feasible signal plan. In the lower layer, platoon accelerations are optimised considering comfort and delay while satisfying motion constraints and safe requirements. The red phase is enforced as a logic constraint, which restricts vehicles to stay behind the stop-line. Typical platoon manoeuvres such as split and approach can be included in the lower layer. The integrated control approach is adaptive to traffic demands and flexible in incorporating different traffic movements during multiple signal phases. The controller performance is verified by simulation of three designed scenarios. The comparison with trajectory optimization and signal optimization demonstrates the advantages on throughput, fuel economy, delay and vehicle stops, and reveals insights into the optimal patterns on signals and trajectories.

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In this paper, a trajectory control approach using model predictive control is proposed for cooperative (automated) vehicles. This control approach optimizes accelerations of the controlled connected and automated vehicle (CAV) platoon along a corridor with signalized intersections. The objectives of the proposed approach are to maximize the throughput first and optimize comfort, travel delay, and fuel consumption simultaneously after that. The throughput is determined according to the maximal number of CAVs that can pass the intersection during the green phase. Safety is included by penalizing smaller gaps between CAVs in the running cost. The red phase is taken into account as a virtual vehicle at the stop-line during the red time, thus the safe gap penalty with the virtual vehicle causes the first-stopping vehicle to decelerate or even stop facing the red phase. The acceleration and speed are constrained within the upper and lower bounds. The proposed approach is flexible in dealing with platoon merging, splitting, stopping, and queue-discharging characteristics at signalized intersections. Finally, the proposed control approach is verified by simulation under a baseline scenario and four scenarios, which consider signal settings and the anticipation of the red phase. The simulation results demonstrate the benefits of the proposed control approach on fuel savings, compared with the state-of-art approach which used the virtual vehicle term without anticipation. The adjustments of signal parameters in Scenario 3 and Scenario 4 demonstrate the applicability of the control approach under actuated signal control.

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Cooperative (automated) vehicles have the potential to enhance traffic efficiency and fuel economy on urban roads, especially at signalized intersections. An optimal control approach to optimize the trajectories of cooperative vehicles at fixed-timing signalized intersections along an arterial is presented. The proposed approach aims to optimize throughput first, and then to maximize comfort while minimizing travel delay and fuel consumption. The proposed approach is flexible in dealing with both quadratic and more complex cost functions. Assuming fixed timing signal control in a cycle and vehicle-to-infrastructure communication, the red phase is taken into account in position constraints for vehicles that cannot pass the intersection in the green phase. Safety is guaranteed by constraining the inter-vehicle distance larger than some desired value. The approach is scalable and can be used for joint trajectory planning of one platoon approaching another stationary platoon. It can also be extended to multiple intersections with fixed signal plans. To verify the performances of the controlled platoon, simulation under three different traffic scenarios is conducted, namely: an isolated intersection with/without downstream vehicle queues, and platoon control at multiple intersections. Three baseline scenarios without control are also designed to compare performances in relation to both mobility and fuel consumption in each controlled scenario. The results demonstrate that the controlled vehicles generate plausible behavior under control objectives and constraints. Moreover, the consideration of downstream vehicle queues and the application at both an isolated signalized intersection and arterial corridors on urban roads verify the flexible characteristics of the control framework.

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Adding lanes is a common method to increase the capacity at intersections. The additional turning lanes are often limited by the road configuration. The paper presents a probabilistic model to calculate the total capacity at signalized intersections for a full through lane and an additional left-turn lane. The proposed model has an advantage over current estimation methodologies by considering not only stochastic arrivals of vehicles but also the signal timing plan, especially signal phasing sequence. Two blockage situations under this channelization form are taken into account. The proposed capacity model shows that the capacities of the through and left-turn movements are related to the proportion of the through traffic volume, the length of the additional turning lane, and the effective green time of both through and left-turn signal phases. It is also found that the through capacity increases with the increase of the proportion of the through traffic and the length of the additional lane, whereas the left-turn capacity declines with an increase of the proportion of the through traffic but increases with an increase of the length of the additional lane. Both capacities decrease with the increase of the green time when the g=C ratio is fixed. The proposed model is compared with design recommendations and verified using the microscopic traffic software VISSIM as well as field data. The results show that the capacities obtained by theoretical models are consistent with the simulated results, and have an advantage over the design recommendations. The capacity models can provide theoretical suggestions for the design of additional turning lanes and signal timing plans.

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