With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

https://www.mdpi.com/1424-8220/19/9/2059/pdf

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections.

Agent-based approaches have gained popularity in engineering applications, but its potential for advanced traffic controls has not been sufficiently explored. This paper presents a multi-agent framework that models traffic control instruments and their interactions with road traffic. A constrained Markov decision process (CMDP) model is used to represent agent decision making in the context of multi-objective policy goals, where the policy goal with the highest priority becomes the single optimization objective and the other goals are transformed as constraints. A reinforcement learning-based computational framework is developed for control applications. To implement the multi-objective decision model, a threshold lexicographic ordering method is introduced and integrated with the learning-based algorithm. Moreover, a two-stage hybrid framework is established to improve the learning efficiency of the model. While the proposed approach is potentially applicable for different road traffic operations, this paper applies the framework for traffic signal control in a network of Stockholm based on traffic simulation. The computational results show that the proposed control approach can handle a complex case of multiple policy requirements. Meanwhile, the agent-based intelligent control has shown superior performance when compared to other optimized signal control methods.

A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System

Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters

The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

Vehicles equipped with Cooperative Adaptive Cruise Control (CACC) have the capability to broadcast their real-time speed and location information via wireless communications. They can also safely operate in multi-vehicle strings while keeping shorter than normal gaps among adjacent vehicles in the high-speed traffic stream. Such capabilities can greatly benefit the management of urban signalized intersections. In this study, we have developed a cooperative signal control algorithm that adopts the CACC datasets and the datasets collected by the traditional fixed traffic sensors to predict the future traffic conditions. The prediction allows the signal controller to assign signal priority to the intersection approach that accommodates the most CACC strings. Such a control strategy can significantly enhance the CACC string operation, which ultimately improves the overall intersection performance. The effectiveness of the algorithm has been tested in a simulated 4-way signalized intersection. The algorithm substantially outperforms the traditional actuated controller as it perceives the traffic flow more comprehensively and assigns the green time resource more efficiently than the traditional controller. Particularly, the average vehicle speed and the average vehicle miles travelled per gallon fuel consumed (MPG) can be increased by more than 10% when the CACC market penetration is 100%. In mixed traffic where CACC fleets frequently interact with manually driven vehicles, the algorithm is found to be more beneficial. The speed and MPG improvement exceed 30% when the CACC market penetration is 40%. The signal control algorithm can bring about significant benefit even when the CACC market penetration is 0%. In this case, it completely relies on the datasets obtained from the traditional traffic sensors. This finding demonstrates the robustness of the algorithm. It makes the proposed algorithm suitable to implement in real-world intersections under various CACC market penetrations and different levels of vehicle connectivity.

Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities

In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.

/pdf/real-time-estimation-of-lane-based-queue-lengths-at-isolated-3ssntik3zs.pdf

Real-time estimation of lane-based queue lengths at isolated signalized junctions

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Real-time estimation of lane-based queue lengths at isolated signalized junctions