SWSCAV: Real-time traffic management using connected autonomous vehicles

Variable speed limits (VSLs) have a potential to improve mobility and safety of motorway through harmonisation of traffic flow. However, the success of VSL is highly dependent on drivers’ compliance to the displayed speed limits. Emerging technologies in the field of connected autonomous vehicles (CAVs) is likely to revolutionise the way VSL will be operated in the near future. VSL can be integrated with CAVs where CAVs will automatically obey the displayed speed limits. However, 100% penetration rate of CAVs is a long‐term goal. At the initial stage of deployment, CAVs will coexist with manually driven vehicles on motorways. This study explores ways to implement VSL under a mixed traffic condition where CAVs and manually driven vehicles coexist in the traffic stream. A hybrid approach combining particle swarm optimisation with proportional‐integrated‐derivative is proposed to improve VSL implementation under a range of mixed traffic conditions. The simulation results showed that the proposed method successfully achieved the desired speed limits and rapidly stabilised the system at the initial and transient points at penetration rates of 50 and 75%, whereas the method encountered serious overshoots at lower penetration rates.

This study evaluates the effects of various market penetration rates of connected autonomous vehicles (CAV) on traffic operations at roundabouts. We have utilized a simulation-and an optimization-based approach for this purpose. The simulation-based approach included calibrated car following models with different driving behavior parameters for a mixed fleet of conventional vehicles and CAVs with various penetration rates ranging from 0% to 100%. We also used an optimization-based approach for the 100% CAV market penetration rate case to evaluate operations while trajectories of CAVs were optimized by a central controller. The simulation results showed that CAVs improved traffic operations in under-and semi-saturated flow conditions. Nevertheless, the optimization of CAV trajectories resulted in significant delay reductions and improvements in the roundabout performance. These results indicate that CAVs have great potentials for improving traffic operations once an effective control algorithm is available.

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR.

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability.

Harmonization with Variable Speed Limits on Motorways

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.

Automated and partially automated vehicles will substantially change the way traffic flows, even at low market penetration rates. While the primary tool for analysis of mixed human-piloted and automated traffic has been microscopic simulation, the use of macroscopic traffic simulation allows for larger-scale simulations. With that in mind we propose a simple numerical method to estimate the composite fundamental diagram of mixed human-piloted and automated traffic flow from the fundamental diagrams of 100% human-piloted and 100% automated traffic flow and conduct calibrated microscopic traffic simulations to produce fundamental diagrams for how mixed traffic flow may behave at different market penetration rates. This method is demonstrated for both an automated vehicle and a commercially available adaptive cruise control vehicle. The proposed method is shown to be able to accurately estimate the shape of the fundamental diagram for arbitrary automated vehicle market penetration rates and the results provide insight into how the mixed traffic flow may behave at the macroscopic level.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane‐changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane‐changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane‐changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two‐lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane‐changing location matching scheme can save 5.9% total travel time when compared to the near‐end location lane‐changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane‐changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane‐changing vehicles increase, our method could perform better.

In the past few decades, Intelligent Transport System (ITS) has gained the popularity in smart society. Global automotive stakeholders such as Google, Uber, and Toshiba are the driving force in that and also looking for the interconnection of autonomous vehicles (AVs) for road traffic management, that is, connected AVs (CAV). CAVs are vulnerable to various safety and security issues, for instance, denial‐of‐service, global positioning system (GPS) spoofing, man‐in‐the‐middle, and timing attacks, which raise serious concerns for their development in application specific areas. Although many solutions exist in the literature, most of them are focused on centralized systems, which is having a single‐point‐failure. In light of the above facts, we analyze and classify the security and privacy issues in CAV and proposed a solution taxonomy in this article. We emphasized the defense mechanism for these attacks on the CAV system along with their enactment on issues and discovered how emergent blockchain technology could handle it. Furthermore, we proposed a blockchain‐based secure and decentralized CAV architecture to mitigate security and privacy issues such as denial of service, man‐in‐the‐middle, hijacking, GPS spoofing, and replay attacks. Finally, we reconnoiter the open research challenges associated with the blockchain‐based CAV system. To validate the efficacy of the proposed system, a real‐time case study on smart city integration over edge computing using computation and communication costs is proposed in this article.

Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management

It is interesting to realize that traffic intersections provide an essential mechanism to handle traffic flows in different directions while ironically traffic bottlenecks, gridlocks, and accidents tend to occur in the vicinity of the intersections. Meanwhile, with the recent developments in internet of things (IoT) technologies, there is a great potential for integrating them for improving operational efficiencies of road infrastructure and connected autonomous vehicles (CAVs). This paper aims to leverage the capabilities of both autonomous intersection manager (AIM) and CAVs for more energy-saving and safe-traffic management. A mixed-traffic environment where human-driven vehicles (HDVs) and CAVs sharing the same road is considered. A two-layer framework is adopted to handle signal and vehicle controls effectively. The first layer is a signal control layer where the AIM receives the traffic network states, trains with the data through machine learning (ML), and outputs a set of optimal green times for each intersection phase. The second layer is a decentralized-vehicle control layer where the CAVs receive the signal phase and timing (SpaT) information from the AIM to compute the optimal speed values. The proposed solution helps the CAVs to minimize idling at red signals or to speed up to safely arrive at and pass through a green signal. Our proposed framework is designed to optimize intersection efficiency and minimize vehicle average delay and fuel consumption. All experiments have been conducted in a microscopic traffic simulation environment, the PTV-VISSIM, simulating real-world dynamics of vehicles and drivers’ behaviors based on decades of field-data.

Variable speed limit (VSL) systems are important active traffic management tools that are being deployed across the U.S. and indeed around the world for relieving congestion and improving safety. Oregon’s first variable advisory speed limit signs were activated along Oregon Highway 217 in the summer of 2014. The variable advisory speed system is responsive to both congestion and weather conditions. This seven-mile corridor stretches around Western Portland and has suffered from high crash rates and peak period congestion in the past. VSL systems are often deployed to address safety, mobility and sustainability related performance. This research seeks to determine whether the newly implemented variable advisory speed limit system has had measurable impacts on traffic safety and what the scale of the impact has been. The research utilizes a before-after crash analysis with three years of data prior to implementation and around 16 months after. Statistical analysis using an Empirical Bayes (EB) approach will aim to separate the direct impacts of the variable advisory speed limit signs from the long term trends on the highway. In addition, the analysis corrects for the changes in traffic volumes over the study period. Three data sources will be utilized including Washington County 911 call data, Oregon incident reports, and official Oregon Department of Transportation crash data reports. The analysis results are compared between data sources to determine the reliability of 911 call data as a proxy for crash statistics. The conclusions should be able to provide an indication of whether variable advisory speed limits can provide increased safety along high crash corridors.

In the future, mixed traffic flow will include two types of vehicles: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to disrupt the traditional transportation system. This new solution shares real-time data with each other and the roadside units (RSU) for network management. Reinforcement learning (RL) is a promising approach for traffic signal management in complex urban areas by leveraging information gathered from CAVs. In particular, coordinating signal management at many intersections is a critical challenge in multi-agent reinforcement learning (MARL). According to this vision, we propose an approach that combines an actor–critic network–based multi-agent deep deterministic policy gradient (MADDPG) model and a rerouting technique (RT) to increase traffic performance in vehicular networks. This algorithm overcomes the inherent non-stationary of Q-learning and the high variance of policy gradient (PG) algorithms. Based on centralized learning with decentralized execution, the MADDPG model employs one actor and one critic for each agent. The actor network uses local information to execute actions, while the critic network is trained with extra information, including the states and actions of other agents. Through a centralized learning process, agents can coordinate with each other, diminishing the influence of an unstable environment. Unlike previous studies, we not only manage traffic light systems but also consider the effect of platooning vehicles on increasing throughput. Experimental results show that our model outperforms other models in terms of traffic performance in different scenarios.

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.