Traffic Throughput Research Articles

A queueing system for analysing traffic throughput in mixed traffic with semi- and fully-autonomous vehicles

As autonomous vehicles (AVs) integrate into transportation systems, a transitional period will emerge where semi-autonomous vehicles (semi-AVs), which still require an attentive driver for steering, coexist with fully-autonomous vehicles (fully-AVs). Focusing on this phase, this study proposes a novel framework that incorporates Markov chains into an M / G n / n max / n max state-dependent queueing system to capture the distinct platooning dynamics and their impacts on the long-term travel efficiency of mixed traffic. The developed Markov chain-based traffic queueing system enables the derivation of traffic throughput in mixed semi- and fully-AV traffic, accounting for their diverse platooning and car-following behaviours. Numerical experiments are used to assess the influence of fully- and semi-AVs on throughput and driving experience in the mixed traffic. This study advances the modelling and analysis of traffic flow efficiency during the transitional phase of mixed traffic, offering insights for managing and controlling semi-AV traffic in the near future.

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Combined speed and distance control for optimized vehicle platooning

This paper presents a novel approach to vehicle platooning using combined formation-regulation control, aiming to enhance the operation of Connected and Autonomous Vehicles (CAVs). By integrating advanced control algorithms with precise positioning and synchronization tools, our methodology enables CAVs to autonomously navigate while maintaining minimal inter-vehicle distances, thus optimizing convoy synchronization. The proposed system consists of two main controllers: a speed consensus controller to synchronize vehicle speeds and a following distance controller to ensure safety. Our combined control approach combines these controllers dynamically based on real-time vehicle spacing, employing exponential functions to smooth the transition between control modes, thereby preventing abrupt changes in vehicle behavior. This method not only increases energy efficiency and traffic throughput but also significantly reduces the likelihood of accidents caused by sudden braking, contributing to decreased fuel consumption and enhanced road safety. Our work not only advances the technology of vehicle platooning but also promises broader implications for the overall transportation ecosystem.

Close-Range Coordination to Enhance Constant Distance Spacing Policies in Oversaturated Traffic Systems.

In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG's performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis.

Demonstration of a spatial-division multiplexing self-healing ring network.

This work demonstrates a spatial-division multiplexing self-healing ring protection scheme. A 3-node, 2-fiber ring with a recirculating length of 139.3 km is implemented using 4-core weakly-coupled multicore working and protection fibers and supported by core-pumped multicore amplifiers. Each node implements a spatial channel switching scheme with 8×8 traffic switches and arrays of 2×2 MEMS switches for ring protection switching with fiber bending taps to simultaneously monitor the power on all cores of the working and protection fibers. We achieve an average loss around 2 dB per network element along the protection path, which enables the use of ring protection with minimal performance impact. With this scheme, we demonstrate the protection of a line-side traffic throughput above 120 Tb/s using polarization-multiplexed 16-ary quadrature-amplitude modulation signals across the C-band.

Platooning of connected automated vehicles on freeways: a microsimulation approach

The platooning of connected automated vehicles (CAVs) is a promising cooperative driving strategy, especially suited for freeway uninterrupted traffic. This paper presents a microscopic simulation approach to the modelling of CAV platooning on freeways, addressing the most relevant factors for the improvement of traffic throughputs. The paper introduces the algorithms to be added to current simulation software so that CAV platoons can be adequately modelled. The goodness of the proposed methodology has been tested through its implementation in the Aimsun Next software and with the application to a mixed traffic scenario consisting of a three-lane ring-road with one on-ramp and one off-ramp, in which platooning is restricted to the leftmost lane. In this context, platooning has shown to increase traffic throughput significantly for CAV penetration rates over 25%. For a 50% penetration rate the infrastructure baseline capacity more than doubles.

Cooperative traffic optimization with multi-agent reinforcement learning and evolutionary strategy: Bridging the gap between micro and macro traffic control

The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM).

A platoon formation algorithm for intersections with blue phase control in mixed traffic

Increasing attention is being paid to intersection signal control with cooperative platoons. Assuming platoons being formed, such platoons cannot only improve the intersection capacity but also minimize the number of control units, especially when dedicated connected and automated vehicle (CAV) lanes are considered. However, the platoon formation process is often neglected, especially for lane-changing and overtaking maneuvers in mixed traffic. This may jeopardize the potential of signal control with platoons. This article proposes a platoon formation algorithm that computes the optimal lane, platoon sequence, and speed profiles of CAVs under the requirement of the central traffic controller. The algorithm is designed for mixed traffic conditions and hence the performance of human-driven vehicles is also considered. A mixed integer linear program model is formulated to minimize the deviation from the desired platoon configuration and the disturbance to overall traffic under any arbitrary initial condition. Numerical experiments are designed to test the effectiveness and the computational performance of the proposed algorithm. Results show that CAVs with signal control can form platoons with rational motion. Besides, the platoon penetration significantly affects platooning feasibility, while the platoon length does not. This suggests that CAVs can form long platoons at intersections to improve traffic throughput.

A simulation based comparative analysis of interconnected signalized intersections and roundabouts from the aspect of permeability

The utilization of the tools provided by digitalization became increasingly important for the micro-level modelling of road traffic, since it typically takes the form of a highly dynamic system. In the publication, the PTV Vissim traffic simulation software is used for the modelling of such a system, a traffic junction formed by two interconnected signalized intersections. The first goal of the analysis was to reveal how the optimization of the signal program in a specific way affects the operation of the entire system in terms of throughput during peak periods. After that, the possibility of modelling the same traffic junction with roundabouts was also explored. Finally, the results for the total traffic throughput of the system during the peak periods were compared in case of the different model variants. The resulting comparison present the permeability variation in a well-structured way and could also provide a useful example for the field.

Understanding route choice behaviors' impact on traffic throughput in a dynamic transportation network

Route planning is one of the most important and attractive topics in complex networks, geographical information science (GIS) and logistics. Travelers' route choice behaviors may affect the actual throughput of a transportation network. This study aims to analyze the influence of travelers' route choice behaviors on network traffic throughput. Two kinds of route choice behaviors, called continuous optimization behavior and hybrid optimization behavior, are defined. Based on this, two traffic scenarios are constructed to describe these behaviors and examine their effect on the network's actual traffic throughput. The application of our approach to urban street networks, Barabási-Albert scale-free network and Erdös-Rényi random network demonstrates that if all travelers choose optimal paths constraint with time from the start, network throughput is insensitive to the continuous optimization behavior but depends on the relationship between time and distance in the routing algorithm. Besides, network throughput is sensitive to the hybrid optimization behavior that the more travelers choose time-dependent optimal paths, the higher the throughput. The results indicate that the choice of initial paths and the time dependence of routing algorithms can directly affect the network's actual throughput which is enhanced >50 % when the time dependence parameter is >0.2 or the proportion of time-dependent shortest paths in initial paths is >0.2. Our findings provide supports for exploring efficient routing algorithms to enhance the throughput of urban street networks and other kinds of networks.

A cooperative interaction strategy for vehicle platoons to obtain merging gaps in connected environments

Vehicle platoons can significantly improve traffic throughput and reduce fuel consumption and emissions. In the formation of platoons, it is crucial to generate safe merging gaps. This process requires appropriate cooperative management and control strategies. This study proposes a cooperative interaction strategy for vehicle platoons, including a communication management system and vehicle control strategies. These strategies can reduce velocity fluctuation in the process of gap generation and improve traffic performance. First, a communication management system within a platoon was developed, according to the standard communication protocol (SAE J2735), ensuring that external vehicles can join the platoon efficiently and orderly. Next, a cooperative adaptive cruise control (CACC) system was designed, which adopts feedforward and feedback control. Furthermore, the influence of increasing gaps on the stability of the platoon was considered. A cooperative control strategy for a virtual guiding vehicle (VGV) was introduced to switch the following target and linearly change the distance input of the controller. In this way, the downstream vehicles were guided to smoothly generate a safe merging gap, which can reduce speed fluctuation, and ensure the stability and safety of the platoon. Finally, the entire process of interaction in a vehicle platoon was tested in a simulation environment. The results showed that, compared with the parameter adaptive control strategy, the maximum velocity overshoot of the platoon vehicles was reduced by 56%, recovery stabilization time was reduced by 47%, and vehicle jitter was reduced by 43%. The driving security and platoon stability were both within the control boundaries set for evaluation.

Lane management for mixed traffic flow on roadways considering the car-following behaviors of human-driven vehicles to follow connected and automated vehicles

The widespread adoption of emerging connected and automated vehicles (CAVs) highlights the need for identifying the roadway capacity of mixed traffic flow with CAVs and human-driven vehicles (HDVs) for future traffic management. Previous studies focus on analyzing the impacts of CAV technologies on the mixed traffic capacity. However, research on how different car-following behaviors of HDVs to follow CAVs affect the mixed traffic capacity is still lacking. This study proposes an analytical formulation of the mixed traffic capacity based on the probability distributions of HDVs and CAVs and three types of behavior willingness (i.e., believer, neutral, and skeptic) of HDV-following-CAV in a mixed traffic environment. The sufficient conditions are derived for mixed traffic capacity to increase or decrease with the behavior willingness and CAV penetration rate. Based on that, an analytical lane management (LM) model is further proposed to determine the optimal number of CAV dedicated lanes (CDLs) and CAV non-dedicated lane (CNL) strategies that maximize the mixed traffic throughput, considering the mixed traffic demand, behavior willingness, and CAV penetration rate. The results from numerical experiments show that the proposed LM strategies can significantly improve mixed traffic throughputs by deploying optimal CDLs and CNL strategies, compared to one without the LM strategy. The mixed traffic throughputs improve and reduce as the trust willingness and skeptic willingness increase and decrease, respectively. Moreover, the increment in CAV penetration rate does not certainly correspond to the high mixed traffic throughputs. The behavior willingness and heterogeneous headways could be jointly considered to guarantee the positive influences of CAV penetration rate on the mixed traffic throughputs when implementing the LM strategy for the mixed traffic flow.

Lane management for asymmetric mixed traffic flow on bidirectional multi-lane roadways

To manage the bidirectional asymmetric mixed traffic flow with connected and automated vehicles (CAVs) and human-driven vehicles, the study proposes a lane management (LM) model based on the joint policy of CAV dedicated lanes (CDLs), CAV reversible lanes (CRLs), and CAV access strategies (CASs) for non-managed lanes. An analytical model is developed to determine the optimal numbers of CDLs and CRLs and the optimal CASs that maximize overall mixed traffic throughputs, considering four parameters under aggressive, moderate, and conservative CAV technology scenarios. The results show that the LM policy can significantly improve overall throughputs compared with the benchmark policy by deploying optimal CDLs, CRLs, and/or CASs under the three scenarios. Moreover, for the LM policy, the overall throughput gradually rises, except for the asymptotical increments with the CAV penetration rate under the conservative scenario and the asymptotical reductions with the directional imbalance percent of traffic demand under the three scenarios.

Deep Multi-Agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem, where the AVs (on both merge lane and through lane) collaboratively learn a policy to adapt to HDVs to maximize the traffic throughput. We develop an efficient and scalable MARL framework that can be used in dynamic traffic where the communication topology could be time-varying. Parameter sharing and local rewards are exploited to foster inter-agent cooperation while achieving great scalability. An action masking scheme is employed to improve learning efficiency by filtering out invalid/unsafe actions at each step. In addition, a novel priority-based safety supervisor is developed to significantly reduce collision rate and greatly expedite the training process. A gym-like simulation environment is developed and open-sourced with three different levels of traffic densities. We exploit curriculum learning to efficiently learn harder tasks from trained models under simpler settings. Comprehensive experimental results show the proposed MARL framework consistently outperforms several state-of-the-art benchmarks.

The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal

Consider deploying a team of robots in order to visit sites in a risky environment (i.e., where a robot might be lost during a traversal), subject to team-based operational constraints such as limits on team composition, traffic throughputs, and launch constraints. We formalize this problem using a graph to represent the environment, enforcing probabilistic survival constraints for each robot, and using a matroid (which generalizes linear independence to sets) to capture the team-based operational constraints. The resulting “Matroid Team Surviving Orienteers” (MTSO) problem has broad applications for robotics such as informative path planning, resource delivery, and search and rescue. We demonstrate that the objective for the MTSO problem has submodular structure, which leads us to develop two polynomial time algorithms which are guaranteed to find a solution with value within a constant factor of the optimum. The second of our algorithms is an extension of the accelerated continuous greedy algorithm, and can be applied to much broader classes of constraints while maintaining bounds on suboptimality. In addition to in-depth analysis, we demonstrate the efficiency of our approaches by applying them to a scenario where a team of robots must gather information while avoiding dangers in the Coral Triangle and characterize scaling and parameter selection using a synthetic dataset.

Comparative Evaluation of Host-Based Translator Mechanisms for IPv4-IPv6 Communication Performance Analysis With Different Routing Protocols

The impending exhaustion of internet protocol (IP) version four (IPv4) addresses necessitates a transition to the more expansive IP version six (IPv6) protocol. However, this shift faces challenges due to the widespread legacy of IPv4 infrastructure and resistance among organizations to overhaul networks. Host-based translators offer a critical bridging solution by enabling IPv6-only devices to communicate with IPv4-only devices through software-level protocol translation. This paper comprehensively evaluates four pivotal host-based translator mechanisms—bump-in-the-stack (BIS), bump-in-the-application programming interface (API) (BIA), BIA version 2 (BIAv2), and bump-in-the-host (BIH). Using simulated networks with diverse configurations of IPv4/IPv6 applications, hosts, and routing protocols, the authors assessed performance through metrics including packet loss, convergence time, traffic throughput, and overhead. The results reveal variability in effectiveness across both translators and scenarios. BIAv2 demonstrated advantages in throughput and overhead due to stateless mapping. The research underscores the importance of selecting the optimal translation approach for specific network environments and goals. It guides smoother IPv6 adoption by demonstrating how host-based translators can facilitate coexistence during transition. Further exploration of performance tradeoffs can continue guiding effective deployment strategies.

Applying Toroidal k-ary Grids for Optimizing Edge Data Centers

IoT deployments are growing exponentially, leading to a huge increase in edge computing facilities. In order to cope with such a demand, data centers need to get customized for the specific requirements of edge computing, such as a small number of physical servers and the ability to scale and unscale according to the traffic flows running at a given time. In this context, artificial intelligence plays a key part as it may anticipate when traffic throughput will increase or otherwise by scrutinizing current traffic whilst considering other factors like historical data and network baselines. In this paper, a dynamic framework is outlined based on toroidal k-ary grids so as to organize and optimize small data centers, allowing them to increase or decrease according to the current and predicted capacity of IoT-generated traffic flows.

Evaluating Efficiency of Connected and Autonomous Vehicles with Different Communication Topologies

Connected and autonomous vehicles (CAV) employ vehicle-to-vehicle communications to safely drive in a platoon with short inter-vehicle distances, which can improve traffic throughput and reduce fuel consumption. With the development of wireless communication technology, more and more information can be used for vehicle controllers. However, is more information better? In this paper, a fuel economy-based performance evaluation index is established for evaluating the efficiency of CAVs driving in a platoon with different communication topologies. Four typical communication topologies that describe the CAV with different amounts of information are studied by the linear controller. The differential evolution algorithm is used to solve the parameters. Due to the increase in information, more control parameters need to be computed, and it is hard to find an optimal solution. So, the simulation results show that CAV with more information did not obtain a better fuel economy.

DMPC-based string stable platoon control with robustness against communication delays

Communication delays are inevitable in vehicle platoons and may inhibit control performance. To address this issue, existing delay compensation strategies often resort to a greater time headway based on the delay upper bound, which compromises the benefits on traffic throughput. Moreover, it remains a major challenge to strictly ensure string stability with delays for distributed model predictive control (DMPC)-based controller. This paper presents a DMPC method for the cooperation of heterogeneous platoons subject to time-varying communication delays and greatly improves robustness against communication delays by improving the DMPC mechanism. A third-order heterogeneous vehicle dynamics model is integrated into the proposed method to improve control precision when implemented in the field. A synchronization mechanism is designed and the constant spacing policy is applied, which eliminates the delay dependence of the time headway selection and may thus improve traffic throughput. By incorporating a dual-mode scheme, a robustness constraint and a string stability constraint in the optimization problem, feasibility, closed-loop stability, and string stability are theoretically proved. A sufficient condition on the controller parameters is derived to guarantee closed-loop stability under communication delays. Numerical simulations are conducted to demonstrate the effectiveness of the proposed delay-compensating DMPC controller. The results reveal that the proposed controller outperforms the traditional DMPC controller and delay-involved linear controller on string stability, tracking performance and desired spacing selection.