Intersection Throughput Research Articles

Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory

AbstractThis paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.

A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

Traffic Flow Optimization for Metropolitan Cities

This research paper presents the design and implementation of an adaptive traffic signal timer system that utilizes real-time traffic density calculation and an intelligent signal switching algorithm to optimize traffic flow at intersections. The proposed system leverages computer vision techniques like object detection to count and classify vehicles, and then dynamically adjusts the green signal durations based on the detected traffic density. A simulation module is also developed to visualize the system's performance and compare it to a static traffic signal implementation. The results demonstrate the effectiveness of the adaptive approach in reducing vehicle waiting times and improving overall intersection throughput

A dynamic traffic signal scheduling system based on improved greedy algorithm.

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.

The importance of V2X simulation: An in-depth comparison of intersection control algorithms using a high-fidelity communication simulation

The rapid evolution of vehicle-to-vehicle and vehicle-to-infrastructure communication opens doors to various control algorithms, one particular domain being intersection control. Several researchers have proposed communication-based intersection control algorithms omitting traditional traffic lights with the promise of enhanced throughput and improved traffic safety. On the other hand, the majority of these algorithms ignore the uncertainties and delays of communication and overlook new cyberattack vectors that are opened by connected traffic. The present study employs a highly detailed simulation of vehicular communication to highlight the sensitivity of different autonomous intersection control algorithms (both centralized and decentralized) to communication-related imperfections. The paper investigates five different algorithms borrowed from the literature in a comparative way. This research focuses on traffic-related parameters such as average speed, occupancy, and network throughput while also analyzing communication-related parameters (e.g., packet loss, computational demand) and considering possible attack vectors. Simulation results suggest that even the simplest control logic is sensitive to communication failures, degrading intersection throughput below traditional intersection control or even compromising traffic safety. For centralized algorithms, in the presence of noise, average speeds drop significantly, suggesting reduced intersection throughput and even gridlock for the First Come First Serve algorithm. Decentralized algorithms can be heavily affected by incoming message orders, which can lead to dangerous situations. During the simulations, multiple collisions were registered for the Monte-Carlo Tree Search algorithm. More complex algorithms that rely on accurate prediction on vehicle trajectories are more sensitive to noise and even produce accidents in the simulation. In conclusion, regardless of control architecture, the evaluated algorithms require additional fallback solutions and redundancies to retain traffic safety.

Simulation of turning traffic at a controlled inter-section

Problem. Optimizing the parameters of traffic light regulation can reduce delays and queues of vehicles, increase the throughput of intersections, and thus increase the quality of the functioning of the city's transport network. This question is especially relevant for Ukraine, because in these conditions it is impossible to carry out a significant modernization of the city's transport network, but within the framework of the post-war reconstruction of the city, it is possible to carry out the improvement of the geometric parameters of the intersection. However, among all the factors that affect the throughput of a regulated intersection, in the scientific literature and normative documents, insufficient attention is paid to the geometrical parameters of the intersection itself. Goal. The geometric parameters of the intersection first of all affect the characteristics of the movement of the selected rotating streams, and through this - the saturation of the stream. Therefore, it is of interest to simulate the movement of turning traffic at an intersection and compare the results with empirical models. Methodology. To estimate the permeability of the selected turning lanes, the correction of the ideal saturation flow is most often used with coefficients, the values ​​of which are either calculated depending on the geometric parameters of the intersection (for example, F. Webster's methodology), or are taken as constant (Posіbnik НСМ 2000). The flow saturation value for the selected turning lane was also calculated taking into account the minimum safe distance of the vehicles in the traffic flow. It is shown that these methods give different values ​​of flow saturation. Results. To check the degree of influence of the rotary flow traffic radius on the flow saturation of the selected traffic, simulation modeling of the robot of the conditional adjustable intersection was carried out in the program PTV VISSIM. The parameters of the model were chosen in such a way that a sufficiently long queue was released before the stop line. In the process of imitation, after turning on the green signal, the virtual detector fixed the number of cars that passed through the dedicated right-turn lane. Practical value. The results showed that for selected turning lanes, the saturation flow increases with an increase in the radius of the turning flow trajectory, which corresponds to the classical formula of F. Webster, when the radius value is up to 15 m. Therefore, adjusting the saturation flow for selected turning flows is appropriate this time. When the flow moves along a path with a larger radius, the saturation flow does not depend on the geometry of the intersection.

The Autonomous Intersection Control Method Based on Reduction in Vehicle Conflict Relationships

Current autonomous intersection control strategies are facing issues, such as lack of foresight, frequent occurrence of deadlock, and low control system efficiency. To address these issues, a vehicle–road cooperative autonomous intersection control strategy based on reducing vehicle conflict relationships is proposed in this study. First, a conflict relationship graph that can describe the driving conflict relationship between vehicles is constructed. Second, the complement of the maximum clique in the conflict relationship graph is solved to determine the set of accepted vehicle reservation requests, enabling more vehicle reservation requests to be successfully processed in unit time while ensuring safe driving at the intersection and improving intersection throughput efficiency. Third, based on the maximum clique method, a taboo search method is used to search the neighborhood, thus improving the quality of the final solution with a smaller search cost. Simulation results show that compared to other control strategies, such as the FCFS (First Come First Served) strategy, the traffic signal control strategy (Traffic-Light), and the control strategy based on greedy algorithm search (Batch-Light), the proposed strategy can considerably reduce the average vehicle waiting time by 42%, 19%, and 10%, respectively, as well as increasing the number of vehicles passing through the intersection per unit of time by 35%, 20%, and 12%, respectively. These results demonstrate the effectiveness of the proposed strategy in improving the throughput of the intersection and reducing the average vehicle waiting time.

Autonomous Intersection Crossing With Vehicle Location Uncertainty

To date, a large body of the literature has looked into the problem of autonomous intersection crossings facilitated by Connected Autonomous Vehicles (CAVs). Nevertheless, existing approaches assume that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized intersection management, which considers vehicle location uncertainties for linear-Gaussian systems. Building upon the proposed framework, a family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0-1$ </tex-math></inline-formula> integer linear programming optimizations are presented that can set, sequentially or simultaneously, the acceleration profiles of all vehicles in the intersection. Extensive simulation results are presented, proving that the proposed framework represents a real-time near-optimal approach that maximizes intersection throughput with probabilistic collision avoidance guarantees.

Decentralized cooperative crossing at unsignalized intersections via vehicle-to-vehicle communication in mixed traffic flows

Connected Autonomous Vehicles (CAVs) are going to share the road environment with human drivers until their full market deployment achievement. In this context, this paper tackles and solves the problem of decentralized crossing at unsignalized intersections for mixed traffic flows, composed of both connected human-driven vehicles (CHVs) and CAVs. To this aim, a novel cooperative fully-distributed control protocol for CAVs is proposed, augmenting the Adaptive Cruise Control (ACC) action based on proximity sensors measurements, with an additional networked protocol exploiting Vehicle-to-Vehicle (V2V) information for the cooperative evaluation of the Time-to-Intersection of all incoming vehicles within the communication range. This further collaborative action automatically adapts the CAVs motion at intersection avoiding both collisions with other vehicles and intersection deadlocks, while improving the intersection throughput. Safety and mobility performances are evaluated via the purposely designed Virtual Testing platform - Mixed Traffic Simulator (MiTraS)- combining the Matlab/Simulink environment with the SUMO traffic simulator. The analysis is carried out for an exemplary two-lane four-way unsignalized intersection considering both different traffic demands and different CAVs penetration rates. The unavoidable presence of variable delays in information delivery due to the wireless communication network has been also explicitly accounted for in both control design and validation. The extensive simulation analysis confirms how the inclusion of CAVs, equipped with the proposed control algorithm, within the mixed traffic flow strongly improves both the safety and mobility performances of the intersection. Lastly, we disclose how the benefits of the proposed controller increase with CAV penetration.

An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections

Efficient operations of traffic signals are of critical importance in urban areas, as signalized intersections prevent the smooth flow of traffic and cause delays. This paper devises an eco-driving algorithm based on connected vehicle technologies, with basic kinematic wave and car-following models. The objectives of the proposed algorithm are to increase the throughput of signal intersections and decrease fuel consumption. Specifically, we focus on a signalized intersection under mixed traffic flows with connected and autonomous vehicles (AVs) and human-driven vehicles (HVs). Through the proposed algorithm, the vehicle speeds at the intersection (i.e., the intersection control speed) and signal timings can be adjusted in response to the real-time traffic conditions. According to the signal timing and the speed at the intersection, the algorithm estimates the time points of each vehicle entering the intersection. An advisory speed limit approach is formulated for each AV, making the vehicle enter the intersection at the allocated timing with the control speed. An onboard alert is set for each HV to stop or pass through. The algorithm is evaluated under various market penetration rates of AVs, different congestion levels, and with signal actuation. The results indicate that the eco-driving algorithm can increase the throughput and average travel speed at signalized intersections in addition to gaining fuel savings.

The Effects of Vehicle-to-Infrastructure Communication Reliability on Performance of Signalized Intersection Traffic Control

Vehicle-to-infrastructure communications can inform an intersection controller about the location and speed of connected vehicles. Recently, the design of adaptive intersection control algorithms that utilize this information received substantial research attention. These studies typically assume perfect communications. This study explores the possible effects of a temporal decrease in the reliability of the communication channel, on the intersection throughput. Road traffic and DSRC-VANET communications are modelled by integrating traffic and communication simulation tools (Vissim and OMNeT++, respectively). Simulations of scenarios with challenging, but realistic communication distortions conditions show significantly larger average delays to vehicles compared to scenarios with perfect communication conditions. These additional delays are largely independent of whether all or only some of the intersection approaches are affected by the communication distortions. Furthermore, delays do not increase uniformly on all signal groups. They may even decrease for some, which causes unfair allocation of green times. The control may be corrected for the lost communications using the information received in previous time intervals and simple assumptions about the vehicle movements. This correction decreases delays in all scenarios both for isolated and connected intersections. It performs similarly to the case with perfect communications when the communication distortions are distributed uniformly among all intersection approaches. Overall, the results demonstrate that the impact of the communication distortions should be considered in the design of the adaptive intersection control algorithms.

Adaptive control for reliable cooperative intersection crossing of connected autonomous vehicles

AbstractRapid advances in vehicle automation and communication technologies enable connected autonomous vehicles (CAVs) to cross intersections cooperatively, which could significantly improve traffic throughput and safety at intersections. Virtual platooning, designed upon car‐following behavior, is one of the promising control methods to promote cooperative intersection crossing of CAVs. Nevertheless, demand variation raises safety and stability concerns when CAVs adopt a virtual platooning control approach. Along this line, this study proposes an adaptive vehicle control method to facilitate the formation of a virtual platoon and the cooperative crossing of CAVs, factoring demand variations at an isolated intersection. This study derives the stability conditions of virtual CAV platoons depending on the time‐varying traffic demand. Based on the derived stability conditions, an optimization model is proposed to adaptively control CAVs dynamics by balancing approaching traffic mobility and safety to enhance the reliability of cooperative crossing at intersections. The simulation results show that, compared to the nonadaptive control, our proposed method can increase the intersection throughput by 18.2%. Also, time‐to‐collision results highlight the advantages of the proposed adaptive control in securing traffic safety.

Capacity Analysis of Intersections When CAVs Are Crossing in a Collaborative and Lane-Free Order

Connected and autonomous vehicles (CAVs) improve the throughput of intersections by crossing in a lane-free order as compared to a signalised crossing. However, it is challenging to quantify such an improvement because the available frameworks to analyse the capacity of the conventional intersections do not apply to the lane-free ones. This paper proposes a novel framework including a measure and an algorithm to calculate the capacity of the lane-free intersections. The results show that a lane-free crossing of CAVs increases the capacity of intersections by 127% and 36% as compared to a signalised crossing of, respectively, human-driven vehicles and CAVs. The paper also provides a sensitivity analysis indicating that, in contrast to the signalised ones, the capacity of the lane-free intersections improves by an increase in the initial speed, maximum permissible speed and acceleration of vehicles.

Field Assessment of Intersection Performance Enhanced by Traffic Signal Optimization and Vehicle Trajectory Planning

The development of Connected Automated Vehicle (CAV) technology introduces vehicle connectivity and automation into urban intersection management. It offers powerful means for accurate traffic state perception and effective traffic control actuation. This enabled the development of an intersection control algorithm that included an optimal traffic signal control algorithm and a trajectory planning function. The intersection controller aimed to maximize the intersection throughput while improving the vehicle energy efficiency via implementing adaptive signal phasing and time plans and sending reference trajectories to CAVs. Its effectiveness was tested on a closed test track equipped with a real-time traffic simulation platform, a traffic signal controller, and three physical test vehicles. The test results suggest that the intersection control algorithm was able to improve the average speed of the test vehicles by 9%, although the optimal signal controller or the trajectory planning algorithm alone could only provide marginal speed benefits. This demonstrated the advantage of combining multiple advanced traffic management functions for obtaining an elevated performance increase. For the energy efficiency analysis, both traditional gasoline-engine and hybrid vehicles were used. The hybrid test vehicles obtained fuel savings mostly via the optimal traffic signal controller (e.g., 17% fuel consumption reduction). On the other hand, the gasoline-engine test vehicle’s fuel savings could be largely attributed to the trajectory planning (e.g., 21% fuel consumption reduction). Overall, the proposed intersection controller demonstrated significant vehicle mobility and energy efficiency improvements for vehicles with different powertrains in a nearly realistic traffic condition.

Networked Control Design for the Assisted Start-Up of Vehicle Convoys using V2V Communication

The use of information via communication within the control design is an important aspect for realizing dependable and efficient autonomous driving functions. In this contribution, we focus on the assisted start-up of a vehicle convoy at an intersection. It is illustrated how vehicle-to-vehicle (V2V) communication can be used in the control design in order to satisfy both demands – an increase of the intersection's throughput and a guaranteed collision avoidance. The key idea is to first design an overshoot-free closed loop with a general speed-dependent target distance. Subsequently, this function can be extended to meet safety requirements in the presence of delays and constraints. Input and speed constraints are included in the control design to address the passenger comfort and the compliance with road traffic regulations. The benefit of the networked controller is demonstrated by simulations.

Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks

This paper proposes a decentralized network-level traffic signal control method addressing the effects of queue spillbacks. The method is traffic-responsive, does not require data communication between intersections’ controllers, uses lane-based queue measurements, and is acyclic. Each traffic controller operating at an intersection aims at maximizing the effective outflow rate locally and independently with the goal of maximizing global throughput of the entire network. At each intersection, the signal control method estimates and adopts the maximum possible phase time in which all active movements discharge at their full capacity. This is modeled using a shockwave based queue length estimation model while capturing the spillback at the downstream links. The method demands real-time data including, the queue lengths, the arrival flows, and the downstream queue lengths in all the lanes at the control decision times. The proposed method results in a feasible solution in all conditions in the entire network with any scale within a short amount of time. A stability concept for the traffic network is defined, and asymptotic stability of the controlled traffic network are verified. Moreover, a sufficient condition for the optimality of the proposed control algorithm for maximizing the instantaneous total throughput of the network intersections is demonstrated. Numerical results show that the proposed method outperforms benchmark methods in both isolated intersection and network configurations.

TRANSPORT DELAYS ON THE STREETS OF BISHKEK

This article examines the study of the conditions for the formation of congestion at regulated intersections of the city. Possible ways of solving the problem are proposed to increase the throughput of intersections and streets in general.

The effect of elimination the left-turn movements on the intersections Capacity-A simulation study

Left-turn movement is an important element to determine the capacity of intersections due to high conflicts points. Therefore, finding the alternatives for such left turn movements may increase the intersection throughput and make it safer. This study discusses the effect of eliminating the left turn movements on the intersection capacity by adding U-turn sections after each approach to apply the left turning away of the intersection. This means that the left turn traffic will pass the intersection using through movements, use the added U- turns and use the intersection again to reach the destination direction using right turns. To apply such scenario, traffic simulation has been applied as a tool using Paramics traffic simulator which is one of the most confident tools over the world. Simulation models have been developed using Paramics considering both signalized and un-signalized intersections with different effective green times. The results suggested that applying such alternatives will significantly increase the intersection capacity. However, further research is needed to examine the geometric layout that would be useful for such alternatives and to confirm the validity of the findings by validation the developed simulation models.

Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections

Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.