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The openness and interconnectedness of industrial control systems (ICSs) is increasing, leading to a heightened risk of network-based attacks. Although research on industrial intrusion detection is ongoing, current methods often overlook the unique characteristics of industrial control flows. This study introduced an industrial network intrusion detection algorithm based on the improved gray wolf optimizer (IGWO) gated recurrent unit (GRU) model. Starting with the temporal aspects of industrial control network traffic, a simple GRU was chosen as the network model. By integrating the gray wolf optimizer (GWO) with autonomous learning methods, the algorithm could address the slow convergence caused by large volumes of industrial control network traffic. In response to the slow convergence of the GWO and its low optimization accuracy, this study developed the improved gray wolf optimizer (IGWO). By simulating an intrusion detection system (IDS) using datasets from the Natural Gas Pipeline Control System and Secure Water Treatment (SWaT) datasets, the experimental results demonstrated that the IGWO-GRU algorithm exhibited considerable advantages in terms of accuracy, false alarm rate, and false report rate, thereby enhancing the security capabilities of ICSs. [ABSTRACT FROM AUTHOR]

Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

In urban system management and public safety, accurate traffic flow forecasting is pivotal for real-world applications such as traffic control, resource-sharing scheduling platforms, and intelligent transportation systems. The challenge lies in effectively capturing complex spatio-temporal correlations. To address this, we introduce ST-DSTN, a novel spatial-temporal fusion attention-based deep neural network that significantly advances the current state-of-the-art. ST-DSTN utilizes three distinct temporal branches to capture temporal dependencies and a convolutional neural network to encapsulate spatial dependencies. Our innovative contribution includes a spatio-temporal fusion attention module, effectively modeling the dynamic spatio-temporal dependencies of traffic data, thus improving upon existing methods. Additionally, we propose a hierarchical fusion method that permeates the entire model, capturing complex correlations at various levels through multi-level fusion. Our comprehensive experiments on the BikeNYC dataset demonstrate ST-DSTN's superior performance, supported by numerical results that substantiate its improvement over current methods, surpassing the DeepSTN+ model by 4.31%. [ABSTRACT FROM AUTHOR]

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles. [ABSTRACT FROM AUTHOR]

Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, this paper presents a novel Twin Delayed Deep Deterministic Policy Gradient with Dual Buffer (TD3_DB) for traffic signal control. In the proposed framework, two experience buffers are used to store important samples and normal samples, separately, and the proportion of the two buffers is adjusted adaptively. In addition, lane pressure, describing the dynamic feature of lane traffic flow, is used for the state design of the TD3 agent, which enhances the perception of the agent toward intersections. Comprehensive experiments on different traffic flow modes has shown, the dual experience replay scheme can improve the sample utilization, and the proposed TD3_DB performs better than other methods such as original TD3, Proximal Policy Optimization (PPO), etc., effectively reducing vehicle queue length and waiting time. [ABSTRACT FROM AUTHOR]

One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road networks through specific algorithms. Deep Reinforcement Learning (DRL) algorithms have become mainstream, yet they suffer from inefficient training sample selection, leading to slow convergence. Additionally, enhancing model robustness is crucial for adapting to diverse traffic conditions. Hence, this paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate the overestimation issue of DRL. Additionally, it introduces a priority sampling mechanism to enhance the utilization efficiency of samples in memory. Moreover, noise parameters are integrated into the neural network model during training to bolster its robustness. By representing high-dimensional real-time traffic information as matrices, and employing a phase-cycled action space to guide the decision-making of intelligent agents. Additionally, utilizing a reward function that closely mirrors real-world scenarios to guide model training. Experimental results demonstrate faster convergence and optimal performance in metrics such as queue length and waiting time. Testing experiments further validate the method's robustness across different traffic flow scenarios. [ABSTRACT FROM AUTHOR]

Deep-buried tunnels with weak surrounding rock are frequently encountered issues in traffic engineering. It plays an important role in the excavation process and the project operation. This paper applies the theoretical analysis and laboratory test related to four different conditions in terms of their thickness to determine the mechanical response of deep-buried tunnel lining. Then, the energy dissipative structure theory is employed to explain the experimental results. This paper has made the following achievements: firstly, it is found that the toughness of the secondary lining was found to be often the most important indicator of tunnel safety, with better-toughness linings having higher tensile strength and crack resistance. Secondly, it suggests that the inclusion of steel reinforcement in the concrete lining can effectively improve the secondary lining toughness. Finally, it proves that the more ductile liner had more energy, higher load-carrying capacity, and was better able to maintain the overall stability of the structure. [ABSTRACT FROM AUTHOR]

In recent years, the problem of traffic congestion has become increasingly serious, and research on traffic system control has become a new hotspot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable support points can alleviate traffic congestion from a new perspective. This article improves the full speed differential model considering strong wind models from the perspective of bifurcation control to adjust traffic flow. This article theoretically proves the existence conditions of Hopf bifurcation and saddle node bifurcation in the model and finds the stability mutation point of the transportation system stability. A nonlinear system feedback controller was designed for unstable bifurcation points using Chebyshev polynomial approximation and random feedback control methods. Without changing the system equilibrium point, the advance, delay, and elimination of Hopf bifurcation were achieved, and the abrupt behavior of the transportation system was controlled, thereby alleviating traffic congestion. This article explains the changes in the stability of complex transportation systems from the perspective of bifurcation analysis, which can better capture the characteristics of traffic flow. By adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the transportation system is fully described, and the influence of unstable focal points and saddle points on the system is suppressed, thereby slowing down the traffic flow. In addition, unstable bifurcation points can be eliminated, and the Hopf bifurcation can be controlled to advance, delay, and disappear, thereby achieving control over the stable behavior of the transportation system. This helps alleviate traffic congestion and also helps describe actual traffic phenomena. [ABSTRACT FROM AUTHOR]

Network traffic classification is a fundamental and intricate component of network management in the modern, high-tech era of 5G architectural design, planning of resources, and other areas. Investigation of traffic classification is a key responsibility of traffic engineering in SDN. SDN is a network programmability technology used in 5G networks that divides the control plane from the data plane. It also points the way for autonomous and dynamic network control. SDN needs data from the classification system's flow statistics to apply the appropriate network flow policies. To control the volume of heterogeneous network traffic data in 5G network service, the network administrator must implement a carefully supervised traffic investigation system. This study uses machine learning techniques to examine alternative ways of handling heterogeneous network traffic. The suggested approach is Ensemble Learning for Automated Network Traffic Categorization. i.e., CatBoosting for Automated network traffic classification for multiclass (Cat-ANTC) predicts traffic categorization and offers a higher prediction accuracy than individual models and a more regularized model formalization to decrease over-fitting and boost efficiency. The Cat-ANTC is evaluated using benchmark network traffic datasets that are openly accessible and contrasted with current classifiers and optimization methods. It is clear that when compared to the currently used ensemble techniques, the suggested ensemble methodology produces promising outcomes. Additionally, the proposed method is tested and shown to perform better than the classification of traffic flow using the current model. [ABSTRACT FROM AUTHOR]

Due to recent advancements in connected and autonomous vehicles (CAVs), freeway traffic control (FTC) has become a trendy area of research. Combining CAVs and control measures has opened more efficient traffic flow possibilities. However, while there is extensive research on how traffic control and CAVs can enhance traffic flow performance, their safety benefits should be evaluated more comprehensively. It is challenging to distinguish their contributions to traffic flow safety. Studies on the safety of CAVs in traffic flow indicate that traffic flow safety improves with higher market penetration rates (MPR) of CAVs. However, this finding only sometimes aligns with the traffic flow performance effects of CAVs on freeways. This paper introduces a framework for assessing the safety effects of FTC strategies in mixed traffic scenarios involving human-driven vehicles and CAVs. The proposed framework is tested through a microsimulation-based case study in Istanbul, Turkey. The results show that, despite varying MPRs, the safety effects of CAVs and FTC methods do not consistently reduce the number of conflicts in the traffic flow context. [ABSTRACT FROM AUTHOR]

In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning, many single-agent deep reinforcement learning algorithms have been extended to multi-agent scenarios. Overestimation in value estimation of Q-learning is a significant issue that has been studied comprehensively in the single-agent domains, but rarely in multi-agent reinforcement learning. In this paper, we first demonstrate that Q-learning-based multi-agent reinforcement learning (MARL) methods generally have notably serious overestimation issues, which cannot be alleviated by current methods. To tackle this problem, we introduce the double critic networks structure and the delayed policy update to Q-learning-based multi-agent MARL methods, which reduce the overestimation and enhance the quality of policy updating. To demonstrate the versatility of our proposed method, we select several Q-learning based MARL methods and evaluate them on several multi-agent tasks on the multi-agent particle environment and SMAC. Experimental results demonstrate that the proposed method can avoid the overestimation problem and significantly improve performance. Besides, application in the Traffic Signal Control verifies the feasibility of applying the proposed method in real-world scenarios. [ABSTRACT FROM AUTHOR]

Recently, there has been considerable interest in the classification of flows among existing applications in SDN, such as traffic engineering, service quality, and network management. Particularly, the classification methods used for elephant flow detection play an essential role. With elephant flow detection, the high bandwidth provided by data centers is used effectively, and traffic routing can be performed on the network without delay. The most important goals in elephant flow classification are to detect elephant flows in the shortest possible time and with high accuracy. This study aims to lead further studies by analyzing different classifiers for elephant flow detection, which is the preliminary step of elephant flow routing in SDN. For this purpose, a new framework is proposed for elephant flow detection. The framework is tested with six different classifiers; most have not been used in the literature before in elephant flow detection on SDN. The proposed framework's first step is to convert UNI1 and UNI2 datasets containing actual network data into flowing datasets using the Flowrecorder tool. The OTSU thresholding method is used for ground-truth labeling. Then, feature extraction is made for the six different classifiers. In the proposed framework, 11 various features are extracted for each flow. In addition, the most suitable parameters for each classifier are determined by the grid search method. The success of classification techniques is calculated using accuracy, precision, recall, F-score, and running time measures. When the results are examined, the proposed framework obtained pretty successful results in SVM and decision tree methods. [ABSTRACT FROM AUTHOR]

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency. [ABSTRACT FROM AUTHOR]

The research of traffic congestion control has been developed rapidly in recent years. Researching the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation characteristics and nonlinear control of traffic flow based on the continuum model considering the new energy vehicles effect. First, a macroscopic traffic flow model considering the number of new energy vehicles and their range is proposed. Then, we transform it into a traffic flow stability model suitable for branching analysis using the traveling wave transformation. Therefore, we can transform the traffic flow problem into the stability analysis problem of systems and reflect unstable traffic characteristics such as congestion. Based on the stability model, the existence conditions of the Hopf bifurcation are proved and some bifurcation points of the traffic system are found. Second, we study the congestion and stability abrupt change behaviors near the equilibrium point and branching point to seek for the formation mechanism of traffic congestion. Finally, for the unstable bifurcation point, the control scheme is designed by Chebyshev polynomial approximation, stochastic feedback control, and other methods to delay or disappear the unstable bifurcation point and alleviate traffic congestion. It has important theoretical significance and application value. [ABSTRACT FROM AUTHOR]

Recently, the Non-Standalone Architecture option 3 standard is proposed to facilitate the deployment of 5th Generation (5G) New Radio (NR) cellular systems. According to this standard, 5G NR base stations can utilize the 4th Generation (4G) Long-Term Evolution (LTE) core network to provide network services. Additionally, the network can be configured to support LTE-NR dual connectivity enhancement, which means that a User Equipment (UE) can connect to a 4G LTE and a 5G NR base station at the same time. In such a dual connectivity network, the 4G LTE base station dispatches downlink traffic flows between the 4G LTE and 5G NR base stations for UEs. As investigated in previous works, traffic flow controls between 4G LTE and 5G NR base stations will affect network performance. In this work, we propose a downlink flow control algorithm based on the concept of Genetic Algorithm, with the objectives of maximizing throughput and fairness of throughput. We verify our designs through simulation programs and experiments on real platforms. The results indicate that the proposed solution effectively achieves the designed objectives. [ABSTRACT FROM AUTHOR]

The findings of this article expounds a multi-server priority queueing model by taking into account the preemptive resume priority scheduling and threshold based phase-type distribution ( P H D ) for retrial process. On the basis of priority, the incoming heterogeneous traffic is categorized as high priority traffic ( H P T ) and low priority traffic ( L P T ). When all the channels are busy, an arriving L P T will be denied for the service, and it will enter the orbit (virtual space) to retry after some time. The retrial process will follow P H D when the number of L P T is less than some threshold value otherwise the retrial process will follow exponential distribution. One of the following two instances may occur when all of the channels are occupied and a H P T enters the system. In the first instance, the arriving H P T will be discarded from the system if all the channels are packed with H P T solely. On the contrary, in the second instance, the approaching H P T will be provided service by employing preemptive priority when at least one L P T is receiving service, and that preempted L P T will enter a buffer of finite capacity. Whenever an idle channel is found, the preempted L P T will begin its service from the termination phase. The level dependent quasi-birth-death process is used for the modeling and analysis of the proposed framework. By establishing that the proposed Markov chain satisfies the asymptotically quasi-Toeplitz Markov chain classification, the ergodicity conditions for the chain are demonstrated. For the numerical illustration, the expressions of several performance measures have been developed. The non-dominated sorting genetic algorithm-II approach has been used to address an optimization problem for resource optimization and traffic control. [ABSTRACT FROM AUTHOR]

The paper presents a framework to realise "meaningful human control" over Automated Driving Systems. The framework is based on an original synthesis of the results of the multidisciplinary research project "Meaningful Human Control over Automated Driving Systems" lead by a team of engineers, philosophers, and psychologists at Delft University of the Technology from 2017 to 2021. Meaningful human control aims at protecting safety and reducing responsibility gaps. The framework is based on the core assumption that human persons and institutions, not hardware and software and their algorithms, should remain ultimately—though not necessarily directly—in control of, and thus morally responsible for, the potentially dangerous operation of driving in mixed traffic. We propose an Automated Driving System to be under meaningful human control if it behaves according to the relevant reasons of the relevant human actors (tracking), and that any potentially dangerous event can be related to a human actor (tracing). We operationalise the requirements for meaningful human control through multidisciplinary work in philosophy, behavioural psychology and traffic engineering. The tracking condition is operationalised via a proximal scale of reasons and the tracing condition via an evaluation cascade table. We review the implications and requirements for the behaviour and skills of human actors, in particular related to supervisory control and driver education. We show how the evaluation cascade table can be applied in concrete engineering use cases in combination with the definition of core components to expose deficiencies in traceability, thereby avoiding so-called responsibility gaps. Future research directions are proposed to expand the philosophical framework and use cases, supervisory control and driver education, real-world pilots and institutional embedding [ABSTRACT FROM AUTHOR]

Traffic congestion not only has a great impact on people's travel, but also increases energy consumption and air pollution. The control analysis of the macroscopic traffic flow model considering the vehicle braking effect is particularly important, reflecting the impact on the actual traffic flow density wave, so as to better solve the actual traffic problems. In this paper, based on a speed difference optimization speed model, the micro–macro-variables are transformed into a high-order continuous traffic flow model. Then, a random function considering the physical correlation of random components is added to the high-order continuous traffic flow model to establish a random traffic flow model that can reflect the uncertain behavior of traffic flow acceleration or deceleration. Based on this stochastic traffic model, the existence of Hopf bifurcation and bifurcation control of the traffic flow system model considering stochastic characteristics are derived by using Hopf bifurcation theorem. By Chebyshev polynomial approximation method, the stochastic problem of the system is transformed into the bifurcation control problem of its equivalent deterministic system. A feedback controller is designed to delay the occurrence of Hopf bifurcation and control the amplitude of the limit cycle. Without changing the equilibrium point of the system, the complete elimination of Hopf bifurcation can be achieved by controlling the amplitude of the limit cycle. That is, the feedback controller is used to modify the bifurcation characteristics of the system, such as the bifurcation appearing at the equilibrium point in the control system moves forward, moves backward or disappears, so as to achieve the effect of preventing or alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Moving aerial targets are tracked in new era of integrated computing with intelligent gadgets as well as systems. Traditional matched filter-based techniques estimation performance would suffer significantly when tracking multiple targets, as weak target is obscured by strong target or strong sidelobes. This exploration propose novel method in radar optical communication handling in light of flying objective examination by recurrence transfer speed with mess concealment. The conventional static radar-based Boundary Element mmwave signal bandwidth and frequency development is used here for the high-profile aerial target detection. Following that, adaptive processing based low rant spatio-temporal with MIMO (multiple input multiple output) is used to suppress radar clutter. Signal-to-clutter-plus-noise ratio (SCPNR), detection accuracy, traffic control, congestion control, and mAP (mean average precision) all play a role in the experimental analysis. Simulation results demonstrate proposed techniques efficacy. The proposed technique attained SCPNR of 85%, detection accuracy of 92%, traffic control of 53%, congestion controlof 55%, mAP of 55%. [ABSTRACT FROM AUTHOR]

A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

The rapid advancements in computer systems and wireless sensor networks (WSNs) motivate many intelligent transportation systems (ITSs) applications in smart cities, such as vehicular ad hoc networks (VANETs). The WSN supports data exchange between mobile vehicles and servers to provide effective traffic control and smooth service for VANET users. However, the vehicle environment still faces many challenges. One of the VANET concerns is the privacy and security of data flows from heterogeneous systems. Therefore, we suggest a Provably Online/Offline Signcryption (POOSC) protocol for vehicular communication in VANET to preserve data confidentiality and ciphertext unforgeability. In this architecture, the transmitter registered in the Identity-Based Cryptosystems (IBC) environment can safely and efficiently communicate with the receiver in the Public-Key Infrastructure (PKI) environment despite the limitations and challenges of heterogeneous systems. In a Random Oracle Model (ROM), we show that our protocol is safe against Indistinguishability-Adaptive Chosen-Ciphertext Attacks (IND-CCA2) under the Computational Diffie–Hellman (CDH) assumption and Existential Unforgeability-Adaptive Chosen Message Attacks (EUF-CMA) under the Discret-Logarithm (DL) assumption. Moreover, the POOSC protocol overcomes the certificate authority burden. Besides, we designed the POOSC protocol with a free pairing that achieves low computation cost and better scalability for VANET environments. The POOSC effectively meets non-repudiation, integrity, and authentication in a logically single step. [ABSTRACT FROM AUTHOR]

Traffic signal control (TSC) can be described as a multi-agent cooperative game. To realize cooperation, multi-agent reinforcement learning (MARL) is a significant approach, with communication being a core component. The large-scale traffic signals and the partially observable information in TSC pose a considerable challenge in finding the optimal joint control policy. This paper proposed a deep MARL model named attentional graph relations communications network (AGRCNet). Based on the Actor-Critic framework, AGRCNet designs a communication network to exchange observation information with agents to help obtain the optimal joint action, reducing the decision error caused by the partially observable condition. Specifically, through the communication network, the chain propagation of graph attention networks (GAT) and graph convolutional networks is used to expand the receptive domain of agents, improve communication efficiency and promote cooperative behavior. We simulate the traffic situation near the Nanjing Yangtze River Bridge in Simulation of Urban MObility. With a compound reward, our method performs best. Meanwhile, AGRCNet is applied to two abstract environments, and the results show that our approach can also adapt to dynamic agent relationships and is more efficient than comparison algorithms. [ABSTRACT FROM AUTHOR]

A new automata-based programming language built by extending the Event-B specification language is proposed. When developing models in Event-B, it becomes possible to use automata-based methods in addition to the popular refinement method. The automata-based software engineering based on Event-B is demonstrated by the example of traffic control on a bridge from the Event-B system manual. A simpler solution with verification in the Rodin tool is proposed. The effectiveness of Event-B verification methods is confirmed by finding three nontrivial bugs in our solution. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern in urban centers, as it can affect society, the environment, and the economy. There are many studies on the use of computational intelligence (CI) to improve mobility in urban centers. Some of these researches focus on developing strategies for traffic light programming, since traffic coordination is complex due to its many parameters, variables, and dynamic behavior, and also an inefficient traffic control plan can lead to increased delays and contribute to traffic congestion. Although there are many works in the literature on strategies for traffic control, there are still some contributions and gaps to be filled, especially because some studies do not consider the automatic optimization of traffic signals in real time, that is, according to the demand of vehicles on the roads, considering multiple objectives and the use of a network of intersections in their experiments. In addition, some of the proposed models are not independent of simulation to evaluate the solutions of CI algorithms, resulting in a more complex deployment in real situations. In this context, this paper presents a new method to optimize traffic light plan in a network of intersections and in real time, called Active Control of Traffic Signals (ACTS) associated with the Non-Dominated Sorting Genetic Algorithm, considering multiple objectives in the optimization process (minimizing the average delay time and the number of vehicles stops per cycle). To test the applicability of the model, a real dataset of vehicle demand collected by the Company of Transport and Traffic of Belo Horizonte (BHTrans) is loaded into the AIMSUN simulator, then the method is applied and compared with the current traffic control plan used by BHTrans. The results show that the ACTS method reduces the average vehicle delay by almost half compared to the results obtained with the current solution used by BHTrans. In real life, this means less time spent in traffic, which promotes faster traffic flow, reducing traffic congestions. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes an experiment to develop and test machine learning models, i.e., long short-term memory, gated recurrent unit recurrent networks, and stacked autoencoder networks. It compares their effectiveness with traffic prediction results generated using a widely recognized traffic simulator that analyzes traffic at the level of individual vehicles. [ABSTRACT FROM AUTHOR]

Rapid increase in the private and public vehicles fleet causes urban centers heavily populated with limited transport road infrastructure. To overcome this, in real-time scenarios, queue length-based traffic light controllers are being designed utilizing light-weighted S-Edge devices. This system suffers from starvation problems if a road lane at the intersection continuously receives vehicles during peak hours. With this, higher green phase duration can be allocated to the same-lane multiple times despite vehicles on the other lanes' longer waiting time. To tackle this problem, an efficient and smart edge computing (S-Edge)-driven traffic light controller is proposed by accounting the real-time heterogeneous vehicular dynamics at the fog computing node. The fog node executes the proposed fuzzy inference system to generate phase-cycle duration. Further, to allocate the phase duration effectively, a method for estimating the lane pressure is proposed for the edge controller utilizing average queue length and waiting time. S-Edge is a light-weighted actuated traffic light controller that generates traffic light control cycle duration and phase (red/yellow/green) duration. To validate the S-Edge controller, a prototype is developed on an Indian city OpenStreetMap utilizing the low-computing power IoT devices, i.e., Raspberry Pi, and a well-known open-source simulator, i.e., Simulation of Urban MObility. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

In this paper, first we introduce the notions of size, order, and degree of vertices and edges in a soft graph, and we give some examples for a better understanding of these concepts. Then, we define the fundamental concepts of the adjacency matrix of soft graphs, complement of soft graphs, and regularity based on the degree of vertices of soft graphs, and we investigate some related results. Moreover, we introduce the concept of soft multiset and soft multigraph and define the notions of union, intersection, complement, and sum, and then we investigate the relations among them. Finally, we give an application of soft graphs in controlling urban traffic flows. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN. [ABSTRACT FROM AUTHOR]

Load balancers enable efficient use of network resources by distributing traffic fairly across them. In software-defined networking (SDN), load balancing is most often realized by a controller application that solicits traffic load reports from network switches and enforces load balancing decisions through flow rules. This separation between the control and data planes in SDNs creates an opportunity for an adversary at a compromised switch to misreport traffic loads to influence load balancing. In this paper, we evaluate the ability of such an adversary to control the volume of traffic flowing through a compromised switch by misreporting traffic loads. We take a probabilistic approach to model the attack and develop algorithms for misreporting that allow an adversary to tune attack parameters toward specific adversarial goals. We validate the algorithms with a virtual network testbed, finding that through misreporting the adversary can control traffic flow to a high degree by drawing a target amount of load (e.g., + 200%) to within a 2% to 10% error of that target. This is yet another example of how depending on untrustworthy reporting in making control decisions can lead to fundamental security failures. [ABSTRACT FROM AUTHOR]

In general, dynamic traffic signal control or smooth driving benefits energy saving and CO2 emissions. However, most of studies in the past lack the integrated optimization of traffic signals control and instantaneous vehicle motion states simultaneously on energy saving and CO2 emission reduction. In this paper, a bi-level optimization model is proposed to minimize fuel consumption and CO2 emissions by considering real-time traffic signal control in road side unit (RSU) and instantaneous vehicle motion states optimization in on-board unit (OBU) synthetically. The RSU communicates with OBUs by vehicle wireless communication networks. Then, the traffic signal scheme in RSU is optimized with the received vehicle motion data sent from OBUs, and the system in vehicles optimize their speed and acceleration with the received traffic signal scheme sent from RSU. The simulation results indicate that the proposed model outperforms the existing Maximize Throughput Model (MaxTM) up to 10% in reducing fuel consumption and CO2 emissions especially when the traffic is heavy. [ABSTRACT FROM AUTHOR]

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people's travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Predicting future traffic state (e.g., traffic speed, volume, travel time, etc.) accurately is highly desirable for traffic management and control. However, network-wide traffic flow has complicated spatial-temporal dependencies, making it challenging to predict. This study proposes a multi-weighted graph 3D convolution network (MWG3D) to predict future network-wide traffic speed, considering the spatial-temporal heterogeneous effects of multiple external factors (i.e., points of interests (POIs), roadway physical characteristics and incidents). The network is composed of a Graph-3D convolution (G3D) module and an incident impact module. In G3D module, a weighted graph convolution is developed first, which extracts complex spatial dependencies of traffic flow considering heterogeneous effects of POIs and roadway physical characteristics. These external factors have great influence on the periodicity of human daily activities, which in turn cyclically affect traffic flow. The weighted graph convolution is further connected with 3D convolutions to extract temporal dependencies of traffic flow, accounting for temporal heterogeneous effects of these external factors. An incident impact module is separately developed to account for spatial-temporal heterogeneous effects of incidents. These external factors could lead to abrupt and temporary changes in traffic flow. The proposed network is evaluated on two real-world datasets. The results show that MWG3D outperforms a selection of the state-of-the-art models. Furthermore, the spatial-temporal heterogeneous effects of external factors are crucial to prediction accuracy. [ABSTRACT FROM AUTHOR]

Highway work zones are associated with significant adverse impacts on safety. Mitigating these impacts can be achieved by implementing several countermeasures. This paper uses controlled experiments in a driving simulator to investigate the effects of physical and digital temporary traffic control countermeasures on work zone safety. These include variable message signs, dynamic speed displays, rumble strips, and lane widths. 116 participants were recruited for the experiment. A linear mixed-effects model was developed to capture the effects of these countermeasures on average travel speed and standard deviation of speeds in the advanced warning area and within the work zone itself. The results reveal that rumble strips installed within the work zone cause the strongest reduction in speeds. Narrower lanes cause smaller speed reductions. In general, the effects of temporary traffic control countermeasures on speeds were local and were not retained beyond their proximity. [ABSTRACT FROM AUTHOR]

Signal optimisation is essential in traffic engineering. The traffic light control timings should be set as optimum. However, it is challenging because the traffic network is a non-polynomial problem. In this study, the problem is evaluated from a unique perspective. The primary idea is to remove the crossings of the intersections. A Hamiltonian cycle algorithm has been used to design the network. So, vehicles are only able to join or split. Thus, no control mechanism is needed that delays or interrupts the flow. The suggested algorithm and intersection design were tested on Allsop and Charlesworth's widely used sample network. Findings were compared with the literature in the form of delay calculated using the Highway Capacity Manual 2010 formula. The suggested network's delay is calculated to be 98.17% and 95.45% less than the original network and recently published study-based delay, respectively. As a result, Hamiltonian roads seem sustainable in time and fuel consumption and could be used for future designs. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic congestion. Recently, many decentralized multi-agent traffic light control algorithms are proposed. These researches mainly focus on improving reinforcement learning method and coordination method. But, as all the agents need to communicate while coordinating with each other, the communication details should be improved as well. To guarantee communication effectiveness, two aspect should be considered. Firstly, a traffic condition description method need to be designed. By using this method, traffic condition can be described simply and clearly. Secondly, synchronization should be considered. As different intersections have different cycle lengths and message sending event happens at the end of each traffic signal cycle, every agent will receive messages of other agents at different time. So it is hard for an agent to decide which message is the latest one and the most valuable. Apart from communication details, reinforcement learning algorithm used for traffic signal timing should also be improved. In the traditional reinforcement learning based ITLC algorithms, either queue length of congested cars or waiting time of these cars is considered while calculating reward value. But, both of them are very important. So a new reward calculation method is needed. To solve all these problems, in this paper, a new ITLC algorithm is proposed. To improve communication efficiency, this algorithm adopts a new message sending and processing method. Besides, to measure traffic congestion in a more reasonable way, a new reward calculation method is proposed and used. This method takes both waiting time and queue length into consideration. [ABSTRACT FROM AUTHOR]

The technology that can recognize the license plates of vehicles in real time and manage them automatically is a key element of building an intelligent transportation system. License plate recognition is the most important technique in vehicle image processing used to identify a vehicle. Object recognition using a camera is greatly influenced by environmental factors in which the camera is installed. When the vehicle image is acquired, the image is distorted due to the tilting of the license plate, reflection of light, lighting effects, rainy weather, and nighttime, so that it is difficult to accurately recognize the license plate. In addition, when the geometric distortion of the license plate image or the degradation of the image quality is intensified, it may be more difficult to automatically recognize the license plate image. Therefore, in this paper, we propose a deep learning–based vehicles' license plate recognition method to detect license plate and recognize characters accurately in complex and diverse environments. As a deep learning model, the YOLO model can be used to detect robust license plates in a variety of environments and to recognize characters quickly and accurately. It can also be seen that the license plate accurately recognizes the license plate with geometric distortion. [ABSTRACT FROM AUTHOR]

With the development of information technology, the high-speed private data stream encryption transmission system is more and more widely used in the field of education. The high-speed private data stream encryption transmission system can realize high-speed and safe data transmission and protect the privacy and security of user data. To protect the security of private data in the online education of specialty "traffic engineering", an encryption transmission system for high-speed private data stream is designed. After the online education system and core modules of traffic engineering are analyzed, the system framework is designed through the codec module and the high-speed privacy data stream processing control and transmission module. The sliding window segmentation method is used to extract the privacy data in the education system and send it to the coding sub-module. Based on a homomorphic encryption algorithm, a private data stream encryption and decryption model is established. The key encoding is designed by the vector quantization coding method. The private data is authenticated according to the characteristics of the key, and the data encryption transmission is completed through the microcontroller. The experimental results show that the encrypted data of the designed system has only 0.01 Gbit loss, and the encryption time is only 93ms. The data dimension displayed after encryption is relatively scattered, and the distribution probability of encrypted data is between 1500–2000, which can effectively encrypt the private data in the online education system of transportation engineering specialty, and improve the security of high-speed private data flow in the online education system of transportation engineering specialty. [ABSTRACT FROM AUTHOR]

With the rapid development of internet technology and mobile devices, massive streaming data of spatiotemporal information is available for real-time data mining. Outlier detection is playing as one of the most important analysis tasks for trajectory stream processing, such as traffic control and urban planning. Existing work about this issue can be divided into two main categories: individual outlier detection (IOD) and group outlier detection (GOD). IOD focuses on detecting an individual trajectory outlier generated by a single moving object (e.g., an over-speeding car), while GOD aims to detect a group outlier generated by various moving objects (e.g., a group of cars influenced by a traffic jam). However, existing studies only support one of them and cannot comprehensively understand the traffic conditions by combining both of them. To this end, we propose a novel framework for real-time urban traffic outlier detection (RUTOD) in this paper. RUTOD contains IOD based on current traffic conditions and GOD according to historical data. In addition, we adopt a street-based trajectory division to accelerate investigation. Experimental results show that our proposal is not only effective but also efficient for detecting both individual outliers and group outliers in real-time scenario. [ABSTRACT FROM AUTHOR]

The construction and operation of linear infrastructure has major impacts on biodiversity through loss of habitat, increased mortality and loss of connectivity. In particular, minimising the impact of roads which pass through ecologically sensitive areas on surrounding species at the construction and operational phases is critical for conservation. However, potential impacts are rarely known perfectly at the construction phase and early in the operational phase. To address this problem, a company could build flexibility into road operation so that it can respond rapidly to future ecological impacts if necessary. In this paper we analyse the value of this flexibility using stochastic dynamic programming and use the results to guide a global search algorithm to find high value roads in the region. We consider flexibility in terms of the proportion of traffic volume routed along the road, with the remainder passing along an existing higher-cost, lower-impact road. We applied this to an example scenario where a road must be routed through a region with a vulnerable species present. By incorporating flexibility, the proposed model was able to find a road that met a desired ending population of animals and was more valuable than roads found under existing design alternatives. [ABSTRACT FROM AUTHOR]

Bifurcation of traffic flow involves complex dynamic characteristics of the system. In order to understand the complex traffic phenomenon, this work designed a macro traffic model considering the driver's memory which plays an important role in the traffic flow. Based on this model, we investigate the effects of the driver's memory and wave velocity on the stability of the traffic flow. By means of one and two parameter bifurcation analysis, we explore how these parameters affect the bifurcation structure of the system, and further investigate the dynamic mechanisms of traffic flow. We explain various traffic phenomena related to the different types of equilibrium points and limit cycles by phase plane analysis. We also study how the initial density and bifurcation structure affect the energy consumption in the system. The results show that the driver's memory and wave velocity play an important role in the stability of the traffic flow. By considering the change of bifurcation structure, we can better understand the source of traffic congestion, and further predict and control the possible traffic congestion. [ABSTRACT FROM AUTHOR]

The paper is devoted to scheduling in multiagent systems in the framework of the Flatland 3 competition. The main aim of this competition is to develop an algorithm for the effective control of dense traffic in complex railroad networks according to a given schedule. The proposed solution is based on reinforcement learning. To adapt this method to the particular scheduling problem, a novel approach based on structuring the reward function that stimulates an agent to adhere to its schedule was developed. The architecture of the proposed model is based on a multiagent version of centralized critic with proximal policy optimization (PPO) learning. In addition, a curriculum learning strategy was developed and implemented. This allowed the agent to cope with each level of complexity on time and train the model in more difficult conditions. The proposed solution won first place in the Flatland 3 competition in the reinforcement learning track. [ABSTRACT FROM AUTHOR]

Although importance of innovation has been widely recognized, there is a lack of the effective method to evaluate the design innovation. Most of the existing methods for innovation evaluations are based on the qualitative analysis and domain experts. As the lack of objective indicators and rules, evaluation results from different designers are easy to differ. In order to reduce obstacles of the innovative design, this paper proposes a quantitative method to evaluate the innovation of conceptual schemes. From the perspective of product physical attributes, length-time dimensions are formed to represent the scheme type and innovation level. The degree of technical differences between existing products and new solutions is assigned as a measurable independent variable. Using a multiple regression model, sufficient case data are analyzed to obtain the evaluation formula with the statistical and engineering significance. The proposed method is applied to evaluate design of a series of practical products. Design of a tire breaker for the traffic control illustrates the method in detail, which proves the method potential to guide designers for searching innovative solutions. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is an incurable B cell malignancy characterized by the accumulation of monoclonal abnormal plasma cells in the bone marrow (BM). It has been a significant challenge to study the spatiotemporal interactions of MM cancer cells with the embedded microenvironments of BM. Here we report a microfluidic device which was designed to mimic several physiological features of the BM niche: (1) sinusoidal circulation, (2) sinusoidal endothelium, and (3) stroma. The endothelial and stromal compartments were constructed and used to demonstrate the device's utility by spatiotemporally characterizing the CXCL12-mediated egression of MM cells from the BM stroma and its effects on the barrier function of endothelial cells (ECs). We found that the egression of MM cells resulted in less organized and loosely connected ECs, the widening of EC junction pores, and increased permeability through ECs, but without significantly affecting the number density of viable ECs. The results suggest that the device can be used to study the physical and secreted factors determining the trafficking of cancer cells through BM. The sinusoidal flow feature of the device provides an integral element for further creating systemic models of cancers that reside or metastasize to the BM niche. [ABSTRACT FROM AUTHOR]

This study investigates how adaptable Machine Learning Traffic Signal control methods are to topological variability. We ask how well can these methods generalize to non-Manhattan-like networks with non-uniform distances between intersections. A Machine Learning method that is highly reliable in various topologies is proposed and compared with state-of-the-art alternatives. Lastly, we analyze the sustainability of different traffic signal control methods based on computational efforts required to achieve convergence and perform training and testing. We show that our method achieves an approximately seven-fold improvement in terms of CO 2 emitted in training over the second-best method. [ABSTRACT FROM AUTHOR]

Next-generation networks are expected to combine advanced physical and digital technologies in super-high-speed connected system infrastructures, gaining critical operation competitiveness of improved efficiency, productivity and quality of services. Towards a fully digital and connected world, these platforms will enable infrastructure virtualization and support of edge processing, making emerging sectors, such as Industry 4.0, ready to exploit its full potentials. Nevertheless, the fast growth of data-centric and automated systems may exceed the capabilities of the overall infrastructure beyond the radio access networks, becoming unable to fulfil the demands of vertical sectors and representing a bottleneck. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for network providers to deploy highly scalable and prioritisable in-network optimisation schemes to meet industry expectations in next-generation networks. To this end, this work presents a novel framework that leverages extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) to offload network functions to reduce unnecessary overhead in the backbone infrastructure. The proposed solution is envisioned to be implemented as a Network Application (NetApp) service, which will greatly benefit the compatibility with next-generation networking ecosystem empowered by Artificial Intelligence (AI), advanced automation, multi-domain network slicing, and other related technologies. The achieved results demonstrate key performance improvements in terms of packet processing capacity as high as about 18 million packets per second (Mpps), system throughput up to 6.1 Mpps with 0% of packet loss, and illustrate the flexibility of the framework to adapt to multiple network policy rules dynamically on demand. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban areas poses several challenges to municipal authorities including pollution, productivity loss, reckless driving, and delays in dealing with emergencies. Smart cities can use modern IoT infrastructure to solve the congestion problem and reduce pollution and delays. In this article, we focus on congestion mapping and mitigation for emergency vehicles in smart cities. We use a novel traffic light control technique to change the flow of cars on lights of interest thereby making way for emergency vehicles. We use a simulation model for a selected area of Manhattan to implement congestion mapping and to help find the fastest path for routing emergency vehicles based on the congestion metrics. The system controls traffic lights to block off the roads feeding into congestion and allows flow away from the congested path. This helps in clearing the preferred route to help emergency vehicles reach the destination faster. We show that the proposed algorithm can map congestion on city roads with accuracy thus helping to improve the response time of the emergency services and saving precious lives. [ABSTRACT FROM AUTHOR]