The deployment of unmanned aerial vehicles (UAVs) as dynamic infrastructure nodes in intelligent transportation systems requires simultaneous consideration of spatiotemporal traffic dynamics, complex urban airspace constraints, and stringent safety requirements. This paper presents a hierarchical learning framework that integrates physics-informed neural networks (PINNs) with adaptive control policies through differentiable safety layers. We develop a multi-level architecture where macroscopic traffic flow dynamics are captured via Lighthill-Whitham-Richards (LWR) PINNs, geometric and environmental constraints are encoded through Eikonal equation solvers, and deployment decisions are made by liquid neural network (LNN) policies. To guarantee safety during both discrete site selection and continuous navigation, we introduce a differentiable control barrier function (CBF) layer that enforces hard constraints through convex quadratic programming while maintaining end-to-end differentiability. The framework addresses uncertainty in learned physics models through risk-aware formulations incorporating distribution-free chance constraints via concentration inequalities and conditional value-at-risk (CVaR) penalties. We establish theoretical guarantees including forward invariance under CBF constraints, probabilistic safety bounds, and convergence to stationary points under joint physics-policy training. Comprehensive experiments on multi-region urban scenarios demonstrate that the integrated approach significantly outperforms both heuristic baselines and ablated variants, with the physics-informed features proving essential for achieving high deployment accuracy while maintaining safety. The modular architecture enables real-time operation and adaptation to diverse urban environments, offering a principled pathway toward autonomous aerial infrastructure management.

This paper presents a novel two-dimensional traffic simulation framework for unsignalized intersections, where traditional lane-based models fail to capture complex vehicle interactions and right-of-way competition. We develop a deep discrete grid-based approach with an integrated prediction-decision-planning behavior model that addresses these fundamental limitations. Our methodological contributions include: (1) a high-fidelity two-dimensional grid network with optimized precision parameters enabling precise spatial representation of traffic elements; (2) an interaction topology framework incorporating multinomial logistic regression and priority transitivity pruning, reducing computational complexity from O(n!) to O((n/2)!) for multi-agent interactions; and (3) a hierarchical trajectory optimization method combining improved A* algorithms with cubic polynomial interpolation to satisfy vehicle kinematic constraints in discrete environments. Validation against the inD dataset demonstrates superior performance, achieving 96.49% trajectory spatial coverage compared to VISSIM’s 13.50%, with no statistically significant differences from measured data in safety indicators (PET: p=0.068) and travel time distributions (p=0.185), both p>0.05 at 95% confidence level. The proposed method provides a high-precision analytical tool for intersection safety evaluation and traffic organization optimization.

Effective urban traffic signal control in large-scale networks remains challenging due to complex interdependencies among intersections and unpredictable fluctuations in traffic conditions. To address these challenges, this paper proposes a novel Adaptive Hybrid Multi-Objective Optimization Algorithm with Reinforcement Learning (AHMOA-RL) for robust and scalable traffic signal management. The core innovation of AHMOA-RL lies in a hierarchical optimization framework that efficiently decomposes the problem into global region-level coordination and local intersection-level refinements, significantly reducing computational complexity while ensuring synchronized control across extensive urban networks. A Q-learning agent dynamically selects among multiple evolutionary operators-Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Local Search-to strategically balance exploration and exploitation during optimization. Additionally, a memory-based evaluation mechanism leveraging historical data is integrated to smooth transient traffic anomalies and provide stable performance estimates. Extensive simulations on large-scale city networks inspired by Manhattan, Paris, S & atilde;o Paulo, and Istanbul demonstrate that AHMOA-RL consistently outperforms state-of-the-art methods, achieving substantial reductions in average vehicle delays, improved network stability, and enhanced robustness under diverse traffic conditions. The algorithm's compact Pareto fronts and superior convergence characteristics validate its effectiveness for practical deployment in complex urban environments.

Vehicular edge-computing (VEC) systems face unprecedented challenges in emergency scenarios, where mission-critical applications demand simultaneous optimization of latency, reliability, and energy consumption under rapidly evolving conditions. These systems are fed by dense vehicular IoT telemetry, including onboard perception sensors, roadside units, and uncrewed aerial vehicle (UAV) relays whose data streams must be scheduled in real time. Existing schedulers either rely on heuristic methods that lack theoretical guarantees or employ computationally intensive optimization that cannot meet real-time constraints. This article presents a novel bilevel scheduling framework that synergistically combines hierarchical decision-making with projected convex optimization to achieve both computational efficiency and provable performance bounds. The proposed hierarchical+projected (HP) scheduler operates through two complementary layers: a lower level mission-aware hierarchical scorer that generates warm-start allocations by evaluating vehicle-tier pairs using forecast costs, queue states, and group coordination factors; and an upper level projected subgradient coordinator that iteratively refines these allocations to enforce tier reservations and future commitment constraints derived from workload predictions. We establish theoretical foundations including convergence rates of O(1/root K) for the projected layer, bounded reservation violations decreasing exponentially with iteration count, and computational complexity of O(KNT\log T) enabling submillisecond execution. The framework integrates seamlessly with forecast-driven orchestration pipelines, incorporating mobility, link quality, and workload predictions, while maintaining safety fallback mechanisms for degraded conditions. Comprehensive evaluation across large-scale emergency traces, calibrated disaster scenarios, and urban mobility benchmarks demonstrates that HP substantially reduces reservation violations compared with pure heuristic methods, improves the system reliability over baseline particle swarm optimization, and significantly expands the Pareto hypervolume (HV) while maintaining the real-time decision latency. Comparative analysis against ten state-of-the-art approaches-including adaptive particle swarm optimization (PSO), convex alternating direction method of multiplier (ADMM), NSGA-II/-III, MOEA/D, deep reinforcement learning (RL), and theoretical hierarchical variants-reveals that HP uniquely balances real-time responsiveness with constraint satisfaction, achieving near-zero reservation gaps while preserving allocation diversity across heterogeneous mission profiles.

To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). Within these VANETs, vehicles participate in multiple task-specific networks with asymmetric coupling and dynamic topologies. We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions, deriving sufficient stability conditions using Lyapunov theory and linear matrix inequalities (LMIs). Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency. Comparative analysis with alternative optimization methods demonstrates that our approach outperforms random search, high-degree selection, and greedy methods by achieving significant reduction in required control nodes while maintaining superior control performance. Extensive simulations across various network scales demonstrate that our approach achieves rapid consensus with a reduced number of control nodes, particularly when leveraging network overlaps. This work provides a comprehensive solution for efficient control node selection in complex vehicular networks, offering practical implications for deploying large-scale intelligent transportation systems.

Vehicular Ad-hoc Networks (VANETs) encounter significant communication challenges in highly dynamic highway environments due to rapid topology changes, varying vehicle densities, and unpredictable channel conditions. To address these challenges, this paper proposes a multi-objective robust optimization framework specifically designed to optimize communication delay, load balance, link quality, and temporal stability of dynamic multi-hop vehicular communication networks on highways. The proposed framework explicitly incorporates temporal continuity constraints to ensure stable and persistent communication paths despite frequent network changes. Moreover, a robust optimization model is formulated to mitigate performance deterioration arising from uncertainties in highway vehicle densities and channel fluctuations. An improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to solve the multi-objective optimization problem efficiently, achieving optimal trade-offs among conflicting objectives. Extensive simulation experiments using realistic highway vehicle trajectories demonstrate that the proposed method achieves balanced performance across multiple objectives. Compared to traditional routing protocols, the framework provides substantial improvements in load balancing and link quality, while maintaining superior path stability through temporal continuity constraints. The framework demonstrates robust performance under various traffic scenarios and maintains effectiveness despite GPS positioning errors and network partitions. The results validate the framework's applicability and effectiveness in real-world highway-based intelligent transportation systems, offering a comprehensive solution that addresses the multi-faceted challenges of vehicular communications.

Train stations as public spaces representing the city, have become gathering points for the city and its inhabitants, acting almost as “gateways to the city” in addition to serving as intersections connecting different rail networks. High-speed rail stations have become multi-storey buildings with different functions; they have become social centers of the city, catering to different needs beyond transportation, with stores, restaurants, hotels and offices where people can spend their time outside of transportation. The Santa Maria Novella (SMN) train station in Florence and the Atocha train station in Madrid are two representative train stations in Europe that play an important role in their respective cities. Using Santa Maria Novella Train Station in Florence, Italy, and Madrid Atocha Train Station in Madrid, Spain, as case studies, this study explores the multifaceted relationship between cities and train stations, how these central train stations have become landmarks and key nodes in their respective cities, and how train stations can be analyzed in terms of their architectural, social, cultural, aesthetic, and landscape dimensions, which can shed light on the future development of transportation hubs and cities provides insights into the future development of transportation hubs and cities, and serves as a reference for urban planning and the construction of transportation facilities.

A major challenging issue related to the emerging mixed traffic flow system, composed of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs), is the lack of adequate traffic control measures, especially in a large freeway corridor with multiple bottlenecks. Multi-agent deep reinforcement learning exhibits significant advantages, such as fast response, high flexibility, strong adaptability, low computational burden, and collaborative optimization. These features enable it to achieve superior efficiency and robustness in handling dynamically changing traffic environments and large-scale traffic control problems. Inspired by this, we propose a novel Integrated Traffic Control (ITC) strategy based on an Improved MultiAgent Twin Delayed Deep Deterministic Policy Gradient (IPMATD3) algorithm in the mixed traffic environment (abbreviated as IPMATD3-based ITC). Specifically, the proposed IPMATD3based ITC approach seeks to coordinate multiple Ramp Metering (RM) and Variable Speed Limit (VSL) controllers along a freeway corridor, with the objectives of improving traffic mobility and efficiency, enhancing safety, and reducing emissions. The proposed method utilized a centralized training with decentralized execution paradigm to learn the joint actions of all traffic controllers in a high-dimensional state and action spaces. A hybrid reward function is developed by synchronously considering the above objectives to optimize traffic control performance. Then, the rank-based prioritized experience replay mechanism is incorporated into the conventional MATD3 algorithm to improve learning efficiency. A real-world freeway corridor is selected to test the proposed control method. Moreover, its performance is compared with the several state-ofthe-art methods. The simulation results demonstrate that the proposed method achieves remarkable control performance at a 10% CAV Penetration Rate (PR), effectively reducing the spatiotemporal extent of freeway traffic congestion. The proposed method outperforms other approaches in improving freeway traffic efficiency, mobility, safety, and environmental sustainability. Increasing the PR can improve the performance of various methods and benefit traffic operations. However, when the PR reaches higher levels, the marginal benefits of further increases become less pronounced.

This paper addresses driving behavior modeling and trajectory optimization on low-grade roads lacking lane markings, using a high-precision grid model with 0.2 m resolution. We propose a discrete spatiotemporal framework to characterize unstructured traffic behaviors. Based on psychological safety zone theory, we construct a free driving behavior model capturing lateral positioning preference and speed selection mechanisms. A multi-objective passing decision model integrating safety, efficiency, and coordination is established, with an improved particle swarm optimization (PSO) algorithm for efficient parameter determination. We design a modified A* path planning algorithm with vehicle kinematic constraints and multi-attribute hybrid heuristics, complemented by piecewise cubic polynomial speed planning to ensure smooth transitions. Simulation results demonstrate that our method increases safety gaps by 16.7%-53.7%, reduces travel time by 21.3%-68.4%, and decreases jerk fluctuations by 45.7% compared to existing techniques, significantly improving safety and efficiency during oncoming vehicle passing.

The significance of transportation efficiency, safety, and related services continues to increase in urban vehicular networks. Within such networks, roadside units (RSUs) serve as intermediaries in facilitating communication. Therefore, the deployment of RSUs is of utmost importance in ensuring the quality of communication services. However, the optimization objectives, such as time delay and deployment cost, are commonly developed from diverse perspectives. As a result, it is possible that conflicts may arise among the objectives. Furthermore, in urban environments, the presence of various obstacles, such as buildings, gardens, lakes, and other infrastructure, poses challenges for the deployment of RSUs. Consequently, the deployment encounters significant difficulties due to the existence of multiple objectives, constraints imposed by obstacles, and the need to explore a large-scale optimization space. To address this issue, two versions of multi-objective optimization algorithms are proposed in this paper. By utilizing a multi-population strategy and an adaptive exploration technique, the proposed methods efficiently explore a large-scale decision-variable space. In order to mitigate the issue of an overcrowded deployment of RSUs, a calibrating mechanism is adopted to adjust RSU density during the optimization procedures. The proposed methods also address data offloading between vehicles and RSUs by setting up an iterative best response sequence game (IBRSG). Comparative analyses against several state-of-the-art algorithms demonstrate that our strategies achieve superior performance in both high-density and low-density urban scenarios. The results indicate that the proposed solutions significantly enhance the efficiency of vehicular networks.

The study conducts a comparative analysis of population elasticity before and after the introduction of High Speed Rail (HSR) across the regions of China. It focuses on the elasticity of population change in relation to development of HSR in China and associated shifts in economic conditions. The concept of elasticity in this context quantifies the percentage change in population resulting from a one percent change in economic growth, using the GDP as the key economic indicator. Through the application of log transformations, this research ensures normalized data distributions, enhancing interpretability and meeting the assumptions required for robust statistical analysis. After the introduction of HSR, significant shifts in population elasticity are observed, so that the findings of this study emphasize significant regional differences—or meso level analysis—in how HSR influences population elasticity with respect to economic growth. The findings underscore the critical role of HSR in promoting economic integration and urban development in China. The infrastructure not only enhances transportation efficiency but also fosters a dynamic and responsive population landscape. As such, HSR can contribute to the realignment of population growth patterns, encouraging balanced regional development and enabling greater economic connectivity between urban and rural areas.

The escalating need for efficient biodiversity monitoring motivates this investigation into lightweight deep learning solutions for automated fish species classification. While conventional convolutional neural networks achieve notable accuracy, their computational complexity hinders deployment in resource-limited ecological monitoring scenarios. To address this challenge, this paper presents a neural architecture search-driven framework utilizing differentiable architecture search (DARTS) to automatically design compact models optimized for edge devices. The proposed methodology systematically explores optimal operator combinations with DARTS, such as the size of the convolution kernel and the kinds of pooling operators, etc. Furthermore, the proposed framework incorporates data augmentation strategies to enhance generalization across degraded field images. In the experiment, our derived Model-D achieves 44.62

Vehicular Ad-hoc Networks (VANETs) operate in highly dynamic environments characterized by high mobility, time-varying channel conditions, and frequent network disruptions. Addressing these challenges, this paper presents a novel temporal-aware multi-objective robust optimization framework, which for the first time formally incorporates temporal continuity into the optimization of dynamic multi-hop VANETs. The proposed framework simultaneously optimizes communication delay, throughput, and reliability, ensuring stable and consistent communication paths under rapidly changing conditions. A robust optimization model is formulated to mitigate performance degradation caused by uncertainties in vehicular density and channel fluctuations. To solve the optimization problem, an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed, integrating dynamic encoding, elite inheritance, and adaptive constraint handling to efficiently balance trade-offs among conflicting objectives. Simulation results demonstrate that the proposed framework achieves significant improvements in reliability, delay reduction, and throughput enhancement, while temporal continuity effectively stabilizes communication paths over time. This work provides a pioneering and comprehensive solution for optimizing VANET communication, offering critical insights for robust and efficient strategies in intelligent transportation systems.

During the proliferation process of Intelligent Connected Vehicles (ICVs), mixed traffic consisting of Human-Driven Vehicles (HDVs) and ICVs is inevitable. Notably, varying trust attitudes of HDV drivers towards ICVs lead to diverse driving behaviors when they interact with ICVs. The impact of these differential driving behaviors on the operational characteristics of the mixed traffic flow is currently unclear. This study focuses on examining the capacity and stability of ICV mixed traffic flow in a single-lane, no-overtaking scenario. We investigate the car-following behaviors of HDV drivers with different trust attitudes towards ICVs. From this, we discern the expected distribution probabilities of vehicles exhibiting varying behaviors in the mixed flow. Utilizing these findings, a theoretical model has been developed to analyze the fundamental diagram and stability of mixed traffic flow, considering the trust attitudes of HDV drivers toward ICVs. Through numerical analysis and simulation experiments, the impact of HDV drivers' trust attitudes in ICVs on the capacity and stability of mixed traffic flow was examined. The results show that ICV integration can bolster traffic flow capacity. Further, heightened trust attitude in ICVs among HDV drivers magnifies the positive effect of ICVs on traffic operational efficiency. However, if the ICV penetration rate remains below 70% and the trust level of HDV drivers towards ICVs does not reach a critical threshold, the integration of ICVs could potentially reduce traffic stability. Conversely, once the ICV penetration rate surpasses 70%, a simultaneous increase in ICV penetration rate and the trusting attitude of HDV drivers towards ICVs significantly improves the stability of mixed traffic flow.

Intelligent traffic signal control is essential to modern urban management, with important impacts on economic efficiency, environmental sustainability, and quality of daily life. However, in current decades, it continues to pose significant challenges in managing large-scale traffic networks, coordinating intersections, and ensuring robustness under uncertain traffic conditions. This paper presents a scalable multi-objective optimization approach for robust traffic signal control in dynamic and uncertain urban environments. A multi-objective optimization model is proposed in this paper, which incorporates stochastic variables and probabilistic traffic patterns to capture traffic flow dynamics and uncertainty. We propose an algorithm named Adaptive Hybrid Multi-Objective Optimization Algorithm (AHMOA), which addresses the uncertainties of city traffic, including network-wide signal coordination, fluctuating patterns, and environmental impacts. AHMOA simultaneously optimizes multiple objectives, such as average delay, network stability, and system robustness, while adapting to unpredictable changes in traffic. The algorithm combines evolutionary strategies with an adaptive mechanism to balance exploration and exploitation, and incorporates a memory-based evaluation mechanism to leverage historical traffic data. Simulations are conducted in different cities including Manhattan, Paris, Sao Paulo, and Istanbul. The experimental results demonstrate that AHMOA consistently outperforms several state-of-the-art algorithms and the algorithm is competent to provide scalable, robust Pareto optimal solutions for managing complex traffic systems under uncertain environments.

This study addresses the inefficiencies in how idle taxis determine their cruising strategy, which currently rely heavily on drivers' personal experiences. Such reliance often leads to inefficient roaming and delays in service. We propose a novel strategy for managing a small scale taxi-fleet, which is common for alliance business, utilizing real-time data and multi-agent reinforcement learning to predict potential travel demands and strategically direct idle taxis to high-demand zones. This method aims to optimize the utilization of idle taxis, reduce passenger wait times, and enhance the ride-hailing ecosystem's overall efficiency. Our approach integrates a spatial-temporal service request model with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It innovates further by employing varied reward mechanisms informed by actual order data and the fluctuating of supply and demand, which does not only enhance the training efficiency in complex scenarios but also captures the dynamics across the service area, effectively managing supply-demand imbalances. Experimental results demonstrate that our strategy significantly outperforms several state-of-the-art methods, representing a major advancement in the optimization of transportation services. This study not only provides a more efficient framework for managing idle taxi fleets but also offers insightful implications for the future enhancement of the transportation sector.

In advanced freeway traffic management systems, variable speed limit control (VSLC) is frequently discussed as one of the control measures. However, in a mixed traffic environment where connected and automated vehicles (CAVs) and human-driven vehicles coexist, the existing VSLC strategies for multi-lane freeways have two major shortcomings: the lack of precise control at the individual vehicle level, and the implementation of uniform VSLC across all lanes. This paper proposes a novel differential variable speed limit control (DVSLC) strategy based on multi-agent reinforcement learning (MARL) in a mixed traffic environment (abbreviated as MARL-DVSLC). The proposed MARL-DVSLC approach utilized a centralized training with decentralized execution paradigm to learn the joint actions of variable speed limit controllers across all lanes, thereby setting different speed limits for each lane. The reward function takes into account the total time spent (TTS) on freeways to improve traffic mobility. Note that MARL-DVSLC disseminates speed limit information to CAVs via infrastructure-to-vehicle (I2V) communication. The effectiveness of MARL-DVSLC is verified under different simulation scenarios. Moreover, its performance is compared with the feedback-based VSLC method, the DVSLC method based on deep deterministic policy gradient (DDPG) (abbreviated as DDPG-DVSLC), and the no-control case in relation to performance. The results indicate that the proposed strategy can effectively improve traffic efficiency and reduce the spatiotemporal range of traffic congestion at a 30% penetration rate of CAVs. Compared with the suboptimal DDPG-DVSLC method, the proposed strategy can improve TTS by 12.88% with stable traffic demand and 10.24% with fluctuating traffic demand.

现有的可变限速(VSL)控制策略灵活性较差,响应速度较慢,对驾驶人遵从度和交通流状态预测模型的依赖性较高,且单纯依靠可变限速标志(VMS)向驾驶人发布限速值,难以在智能网联车辆(CAVs)与人工驾驶车辆(HDVs)混行的交通环境中实现较好的控制效果.对此,结合深度强化学习无需建立交通流预测模型,能自动适应复杂环境,以及CAVs可控性的优势,提出一种混合交通流环境下基于改进竞争双深度Q网络(IPD3QN)的VSL控制策略,即IPD3QN-VSL.首先,将优先经验回放机制引入深度强化学习的竞争双深度Q网络(D3QN)框架中,提升网络的收敛速度和参数更新效率;并提出一种新的自适应ε-贪婪算法克服深度强化学习过程中探索与利用难以平衡的问题,实现探索效率和稳定性的提高.其次,以最小化路段内车辆总出行时间(TTS)为控制目标,将实时交通数据和上个控制周期内的限速值作为IPD3QN算法的输入,构造奖励函数引导算法输出VSL控制区域内执行的动态限速值.该策略通过基础设施到车辆通信(I2V)向CAVs发布限速信息,HDVs则根据VMS上公布的限速值以及周围CAVs的行为变化做出决策.最后,在不同条件下验证IPD3QN-VSL控制策略的有效性,并与无控制情况、反馈式VSL控制和D3QN-VSL控制进行控制效果上的优劣对比.结果表明:在30％渗透率下,所提策略即可发挥显著控制性能,在稳定和波动交通需求情境中均能有效提升瓶颈区域的通行效率,缩小交通拥堵时空范围,与次优的D3QN-VSL控制相比,两种情境中的TTS分别改善了14.46％和10.36％.

AbstractMerging activities at freeway merging areas can cause significant recurrent and non‐recurrent bottleneck congestion due to vehicles’ mandatory lateral conflicts. The Connected and Automated Vehicles (CAVs), with their capabilities of real‐time communication and precise trajectory control, hold great potential to prevent or mitigate such critical conflicts at merging areas. However, the performance of CAVs may be impaired by the imbalance of lane flow distribution, and non‐cooperative movements of Human‐driven Vehicles (HDVs) in the mixed traffic environment (i.e. traffic mixed with CAVs and HDVs). In this paper, a novel two‐level hierarchical traffic control framework for multilane merging areas under the mixed traffic environment is developed. Note that this paper assumes that the merging sequence is determined by the high control level and focuses on the low level of the control framework. The low control level not only establishes a trajectory optimization strategy for CAVs with lane‐changing optimization and cooperative merging control, but includes a human‐like merging strategy for HDVs. First, to balance downstream lane flow distribution and provide sufficient merging space for on‐ramp vehicles, a lane‐changing optimization method is proposed to choose a certain number of designated mainline CAVs to perform early lane changes at the upstream mainline. Second, a cooperative merging control method is presented to optimize the longitudinal trajectories of both mainline and on‐ramp CAVs while accounting for the movement of HDVs. Third, Gipps car‐following model and heuristic control are combined to represent the HDVs’ merging maneuvers. The proposed algorithm simulates and performs merging maneuvers at a typical two‐lane freeway merging area and verifies it in various scenarios considering demand level, demand splits and CAV Penetration Rate (PR). The simulation results show that the proposed algorithm can effectively facilitate merging operations, reduce the Total Travel Time (TTT), and increase the Average Travel Speeds (ATS) compared to other merging algorithms. Specifically, compared to the case of using only cooperative merging control method, the proposed algorithm can further reduce TTT by 25.5% and increase ATS by 33.38%. When PR gradually increased, the control performance of the proposed algorithm can be further improved.

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.