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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]

Forecasting traffic flow is crucial for Intelligent Traffic Systems (ITS), traffic control, and traffic management systems. Complex spatial and temporal interactions of traffic networks make traffic forecasting tasks challenging. Recently, Graph Convolutional Network (GCN) has attracted researchers' attention as it can better represent graph-shaped road networks and extract spatial features of traffic. However, traditional GCN has some drawbacks since it uses a static adjacency matrix which is unable to capture the time-varying features of traffic propagation. To overcome this, we represent the traffic road network as a dynamic graph and use a probabilistic spatiotemporal adjacency matrix to identify the time-varying impacts of adjacent roads on target roads in GCN. In addition, to find the similarity among the nonadjacent nodes, we have employed node-specific learning in GCN rather than sharing parameters in traditional GCN. This node-specific learning helps our model to learn detailed characteristics of road networks. For temporal feature extractions, we used a Gated Recurrent Unit (GRU) that captures the local trend of traffic flow and an attention mechanism to capture the global trend of traffic flow. We compared the performance of our model with baseline models using two real-world datasets. Experimental results show that our model is effective in forecasting both short and long-term traffic flow. Source code of our model is available at https://github.com/atkia/PSTGCN [ABSTRACT FROM AUTHOR]

Automated Vehicles (AVs) are designed and programmed to follow traffic rules. However, there is no separate and comprehensive regulatory framework dedicated to AVs. The current Queensland traffic rules were designed for humans. These rules often contain open texture expressions, exceptions, and potential conflicts (conflict arises when exceptions cannot be handled in rules), which makes it hard for AVs to follow. This paper presents an automatic compliance checking framework to assess AVs behaviour against current traffic rules by addressing these issues. Specifically, it proposes a framework to determine which traffic rules and open texture expressions need some additional interpretation. Essentially this enables AVs to have a suitable and executable formalization of the traffic rules. Defeasible Deontic Logic (DDL) is used to formalize traffic rules and reasoning with AV information (behaviour and environment). The representation of rules in DDL helps effectively in handling and resolving exceptions, potential conflicts, and open textures in rules. 40 experiments were conducted on eight realistic traffic scenarios to evaluate the framework. The evaluation was undertaken both quantitatively and qualitatively. The evaluation result shows that the proposed framework is a promising system for checking Automated Vehicle interpretation and compliance with current traffic rules. [ABSTRACT FROM AUTHOR]

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world. Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops. [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 alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes. [ABSTRACT FROM AUTHOR]

Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. [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]

In urban areas, utilizing traffic lights to prioritize vehicles at the intersection is a solution to control traffic. Among the smart traffic light methods, the methods based on machine learning are particularly important due to their simplicity and performance. In this paper, Q-learning with deep neural network are combined and used in two different intersection models. The first one is an individual intersection, and the second one is two intersections that are connected and shared their actions. By using this method, the traffic light can make an intelligent decision at the intersection to reduce vehicle consumption time by managing the allocation of phases. The proposed smart traffic light is studied and simulated via SUMO. The results illustrated that compared to fixed-time traffic lights, the average queue time of each vehicle in different traffic scenarios has been reduced by 34% in the individual intersection. In the case of two intersections, awareness and communication between agents led to a 24% reduction in the queue time of all cars in the heavy traffic scenario. [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]

Accurately predicting network traffic is helpful for improving a variety of spatial-temporal data mining applications, such as intelligent traffic control, network planning and anomaly detection. The mainstream graph-based methods are limited by the node-level message passing mechanism and require transforming dimensions to generate edge representations. Accordingly, some researchers propose using edge convolution to directly learn edge representations for network traffic prediction. However, node-level and edge-level information aggregation are two different perspectives, and their combination of them can achieve better performance. This paper proposes a novel model for network traffic prediction named the Enhanced Edge Convolution-based Spatial-Temporal Network (EESTN). Armed with a Graph Neural Network and Hypergraph Neural Network, EESTN employs the edge convolutions defined on the graph and hypergraph to effectively extract spatial features. EESTN further combines node convolution to capture the complex correlations among nodes and utilizes an attention mechanism to generate the edge convolution kernel for the decoder. Moreover, a 3D convolution-based multihead self-attention mechanism and a hierarchical loss function are proposed to capture the long-term temporal dependence and make full use of the model's represent ability. Finally, we conduct extensive experiments to validate the effectiveness of EESTN, and the related results demonstrate that EESTN outperforms the state-of-the-art methods. [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]

Using reinforcement learning to control traffic signal systems has been discussed in recent years, but most works focused on simple scenarios such as a single crossroads, and the methods aiming at large-scale traffic scenarios face long-time training and suboptimal results. In this work, we develop a new multi-agent reinforcement model for large-scale traffic signal control tasks, and a curriculum transfer learning method is developed to optimize the joint policy step by step. The policies for different intersections are trained in a partially observable Markov decision process with centralized training and decentralized execution mechanism, and we design transformer modules for both the policy and evaluation networks by attention mechanism. We first train policies in a simple traffic scenario, and then these policies are transferred to the next curriculum by policy reloading, while the experiences of the source task are reused selectively. With the number of agents increasing, our method can achieve satisfactory performances quickly by reusing the knowledge from previous curriculums. We conduct several experiments on the Cityflow testbed. In the case of more than 10 crossroads, our model improve the mean reward from 3.0 to 5.0. [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]

Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is mismatch between the training and testing environments. The main reason for this mismatch is the presence of noise during the testing phase of an ASR system. Various techniques have been used by different researchers in front and backend phases of ASR, to detect and handle the noise. However, a very few review papers have considered noise as a criterion to present the comparison among the existing research works. Hence, the objective of this survey is to analyze and review all the effective methods proposed by different scientists and researchers to boost the noise robustness of an ASR system. Initially, the paper discusses the basic architecture of an ASR system, the factors affecting the its performance, and noise problem formulation. Secondly, the work analysis existing state of the art noise robust ASR methods in terms of front end feature extraction techniques and backend classification model. Then, a detailed review in terms of various speech databases, that are used by these methods, is given. Finally, an analysis in terms of performance metrics of all these noise-resistant ASR techniques is presented. Also, the paper discusses various feature extraction techniques, backend classification methods, different speech databases and performance metrics in detail, while presenting the analysis. The paper also discusses the existing challenges, and describes future research directions in the area of building noise-resistant ASR systems. [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]

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]

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

In contemporary urban, traffic signal control is still enormously difficult. Multi-agent reinforcement learning (MARL) is a promising ways to solve this problem. However, most MARL algorithms can not effectively transfer learning strategies when the agents increase or decrease. This paper proposes a new MARL algorithm called cooperative dynamic delay updating twin delayed deep deterministic policy gradient based on the exponentially weighted moving average (CoTD3-EWMA) to solve the problem. By introducing mean-field theory, the algorithm implicitly models the interaction between agents and environment. It reduces the dimension of action space and improves the scalability of the algorithm. In addition, we propose a dynamic delay updating method based on the exponentially weighted moving average (EWMA), which improves the Q value overestimation problem of the traditional TD3 algorithm. Moreover, a joint reward allocation mechanism and state sharing mechanism are proposed to improve the global strategy learning ability and robustness of the agent. The simulation results show that the performance of the new algorithm is better than the current state-of-the-art algorithms, which effectively reduces the delay time of vehicles and improves the traffic efficiency of the traffic network. [ABSTRACT FROM AUTHOR]

Most traffic control systems are centralized, where all the collected data can be analyzed to make a decision. However, there are problems with computational complexity and, more seriously, real-time decision-making. This paper proposes a decentralized traffic routing system based on a new pheromone model of ant colony optimization algorithm and an automated negotiation technique in a connected vehicle environment. In particular, connected vehicles utilize a new pheromone model, namely the inverted pheromone model, which generates a repulsive force between vehicles and gives negative feedback to the congested roads. They also perform a collective learning-based negotiation process for distributing traffic flows throughout the road networks, reducing traffic congestion. Via extensive simulations based on the Simulation of Urban Mobility, the proposed system shows that it can significantly reduce travel time and fuel consumption compared to existing systems. [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]

Traffic volume data at fixed detectors are of great importance to track the time-varying states of urban traffic, and the volume of each turning movement (i.e., continuing straight, turning left, turning right) in an intersection plays a significant role in traffic control. It is a difficult issue to determine the complete road network volume due to the limited coverage of detector deployment. Existing traffic volume imputation methods seem to be hardly tractable to address this persistent missing data problem of fixed detectors because they mainly function by utilizing spatiotemporal patterns of historical data to address the problem of intermittent missing traffic data collected from probe vehicles. Considering the unattainable temporal information of the target movement, this paper aims to develop an end-to-end graph neural network-based imputation approach to impute traffic volume data with persistent missing patterns. We regard the road network as a directed graph with each turning movement as a node and employ a meta-learning-based graph attention mechanism with graph embedding in multiple spatial granularities to fully extract the relationship between the target node and its neighbours. In addition, a multitask learning mechanism is designed to improve the accuracy of the imputation results by setting auxiliary tasks according to the volume conservation constraints. The proposed approach has been validated with extensive simulation experiments and applied to field tests for Shenzhen and Shanghai, China, with considerable accuracy and reliability, satisfying the demand for traffic control. To the best of our knowledge, this is the first time that traffic volume has been imputed for persistent missing patterns with a graph neural network-based method. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction plays a significant role in urban traffic management, including traffic congestion control and public travel route planning. Recently, several approaches have been put forward to learn the patterns from historical traffic data. However, there exist some limitations resulting from the use of the static learning method to explore the dynamical characteristics of the road network. Besides, the dynamic global temporal and spatial properties are not considered in these models. These drawbacks lead to a low prediction performance and make applying to a more extensive road network challenging. To address these issues, from the inspiration of Chebyshev polynomial, we proposed an improved dynamic Chebyshev graph convolution neural network model called iDCGCN. In the proposed approach, a novel updating method for the Laplacian matrix, which approximately constructs features from different period data, is proposed based on the attention mechanism. In addition, a novel feature construction method is proposed to integrate long-short temporal and local-global spatial features for complex traffic flow representation. Experimental results have shown that iDCGCN outperforms the state-of-the-art GCN-based methods on four real-world highway traffic datasets. [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]

Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established. Measurement(s) Ambulance Siren and Road Noises. (Recordings) Technology Type(s) Microphone Sample Characteristic - Environment Roads Sample Characteristic - Location Pakistan [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN), a novel network paradigm, separates the control plane and data plane into different network equipment to realize the flexible control of network traffic. Its excellent programmability and global view present many new opportunities. DDoS detection under the SDN context is an important and challenging research field. Some previous works attempted to collect and analyze statistics related to flows, usually recorded in switches, to address DDoS threats. In contrast, other works applied machine learning-based solutions to identify DDoS and achieved promising results. Generally, most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions. Nevertheless, the request for flow rules is very time-consuming and CPU-consuming; moreover may congest the communication channel between the controller and the switches. Therefore, we present FORT, a lightweight DDoS detection scheme, which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state. A time-series algorithm, ARIMA, is utilized to determine the port statistics adaptively, and an SVM algorithm is applied to detect whether a DDoS attack does occur. Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy. Referring to the false alarm rate of 1.24% in the comparison scheme, the false alarm rate of this scheme is only 0.039%, which significantly reduces the probability of false alarm. Besides, by introducing the alarm mechanism, this scheme can reduce the load of the southbound channel by more than 60% in the normal state. [ABSTRACT FROM AUTHOR]

Crowd flow prediction is one of the most remarkable issues in a wide range of areas, from traffic control to public safety, and aims to forecast the inflow and outflow of crowds in each region of a city. Most existing studies adopt CNN and its variants to discover the spatial patterns of grid maps (each grid represents a region) while ignoring the correlation between distant regions that may share similar temporal patterns. In this paper, we propose a gnn-based prediction method, called STGs, for crowd flow prediction, which jointly constructs spatial and temporal graphs from grid maps and then implements graph neural networks to directly capture the relationship between regions. Additionally, we introduce a gated fusion mechanism to combine spatial and temporal embedding from the corresponding graph, which further improves the performance of our STGs. We conduct numerical experiments to compare STGs with other baseline models using two real-world datasets, BikeNYC and TLC. Experimental results demonstrate the superiority of our STGs model; specifically, our model reduces the mean absolute error (MAE) of crowd flow prediction by approximately 7-8% compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

The use of the Internet of things (IoT) devices is growing rapidly in the current industrial market. Integrating these devices into the network system has subjected the information to Cybersecurity attacks putting the market of IoT technology at risk. As a result, accurate protection algorithms and efficient monitoring systems need to be developed. This paper proposes a new framework design relays on a stochastic measurement of determining parameters based on a novel adaptive deep learning (ADL) algorithm for industrial manufacturing such as transportation, energy. The new framework will be designed to incorporate an element of randomness as opposed to deterministic frameworks. The implemented method considered the network forensic systems and intrusion detection (ID); therefore, the prediction model is set up to alert the administrator about the system status. The framework is proposed based on five prioritized protection stages. The developed model tested on the Bot-IoT dataset, which is well-structured and valid for training, as confirmed by literature. The result has shown the ability to simulate the IoT network traffic along with risk assessment against different types of attacks by using the ADL algorithm. The new framework will be designed to improve computational efficiency by being able to adjust their structure based on the inputs. The research contributions are that it will help in the development of an algorithm which can minimize traffic accidents, improve traffic flow, minimize energy wastes, and save costs of controlling traffic flow. Findings show that using this new algorithm has 100% accuracy, which makes it an excellent choice for the detection of threats and errors. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is of great significance to urban traffic control and public safety applications. The key challenge of traffic flow forecasting is how to capture the complex correlation of different time levels and learn time dependence. Some external information is closely related to traffic flow, such as accidental traffic accidents, weather, and Point of Interests (PoI) information. This paper proposes a deep learning-based model, called AttDeepSTN+, which is used to predict the inflow and outflow of each area of the entire city. Specifically, AttDeepSTN+ uses the structure of interactive attention and convolution to model the temporal closeness, trend, and periodicity of crowd flow, in the interactive attention layer, learn the importance of closeness to periodicity and trend respectively to model the long-term dependence of time, and then use feature fusion to capture complex correlations at different levels, thereby reducing model prediction accuracy. In addition, PoI information are combined with time factors to express the influence of location attributes on crowd flow, to learn prior knowledge of crowd flow. Finally, a new fusion mechanism is used to fuse the attention layer modules and PoI information and other information together into the module to capture the complex correlation between different levels of features, to predict the final crowd flow in each area, and further improve the prediction accuracy of the model. The New York City crowd flow experiment shows that the model is better than the current state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

The newly-built single-level rail-cum-road bridge brings the issue of the aerodynamic impact of train-induced wind on road automobiles. This research introduced a validated computational fluid dynamics (CFD) model regarding this concern. Such an aerodynamic impact mechanism was explored; a relationship between the transverse distance between train and motor-van (hereinfafter referred to as van) and the aerodynamic effects on the van was explored to help the optimization of bridge decks, and the relationship between the automobile speed and aerodynamic variations of a van was fitted to help traffic control. The fitting results are accurate enough for further research. It is noted that the relative speed of the two automobiles is not the only factor that influences the aerodynamic variations of the van, even at a confirmed relative velocity, the aerodynamic variations of the van vary a lot as the velocity proportion changes, and the most unfavorable case shows an increase of over 40% on the aerodynamic variations compared to the standard case. The decay of the aerodynamic effects shows that not all the velocity terms would enhance the aerodynamic variations; the coupled velocity term constrains the variation amplitude of moments and decreases the total amplitude by 20%–40%. [ABSTRACT FROM AUTHOR]

Background: Biosecurity plays an irreplaceable role in preventing diseases, increasing productivity of dairy herd and welfare on the farm, prevention of drug resistance and public health safety. Assessment of biosecurity measures were carried out both by observations and questionnaires from December 2019 to April 2020 in dairy cows' farms. In addition to assessment of biosecurity, the present study also intended to identify factor associated to biosecurity and its specific component implementation. Results: Out of 125 biosecurity practices considered to evaluate the overall biosecurity status of dairy farms, the scores ranged from 34 to 75. The overall percentage of the biosecurity score varied from 27.2 to 60%. Out of 20 dairy farms included in the present study, nine farms gained a percentage score of greater than or equal to 50% (ranging from 50.4 to 60%), thus their biosecurity status was evaluated as "Good". The remaining 11 farms attained a percentage score lower than 50% (varying from 27.2 to 46.4%) and therefore graded as "Poor". In this study biosecurity categorized in to four components: traffic control (21.5 ± 4.3), isolation (14.45 ± 2.2), sanitation (16.65 ± 6.83) and health management (3.25 ± 1.07) with their respective mean ± standard deviation of each component. Out of four biosecurity components: isolation and traffic control were implemented better, but sanitation and animal health management were poorly implemented. Fisher's exact test analysis of the obtained results showed that among all factors assumed to affect biosecurity adoption, however only a significant association (P < 0.05) between biosecurity status and location of farms was appreciated. Conclusions: In conclusion, the overall adoption of biosecurity measures in the dairy farms was evaluated to be poor. Thus, raising awareness of herd owners, facilitating dairy husbandry training by the concerned body, pushing herd owners to develop a biosecurity plan and practicing it. And, especially improvement of sanitation and animal health management systems should be prioritized by policy makers or the concerned organizations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) refer to a group of battery-operated tiny sensor nodes having vast application areas in daily use. These are spatially dispersed and dedicated wireless sensor nodes for observing and recording the different parameter and physical conditions of the surroundings. There is a recent advancement in the field of network connectivity and computations in WSNs. The key functions of WSNs are a data extraction and to transmit the extracted data to the server placed at an isolated location. Various types of WSNs like underground underwater, terrestrial, and multimedia networks get applications domains such as in industrial automation, traffic monitoring and control medical device monitoring, and many other areas. Despite the thriving market, there are several challenges like energy efficiency, limited storage and computation, low bandwidth, high error rates, scalability, and survivability in harsh environment; hence, network lifespan expanding is a critical demanding issues. So many researchers have earlier focused towards finding the optimal path in between member node and sink node, so that energy depletion can be reduced to improve the network lifespan. There are different challenges in WSNs but one of the most challenging issues is how to minimize the energy consumption; numerous bio-inspired techniques have been proposed previously to obtain an optimal path between the member node and the sink node. In this manuscript, we are presenting a comprehensive survey on optimization technique-based routing and clustering. The study of this comprehensive survey offers in-depth summary of the past researches in the area of WSNs. [ABSTRACT FROM AUTHOR]

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions. [ABSTRACT FROM AUTHOR]

Traffic lights are an important controlling factor in traffic flows, and good policies will facilitate traffic congestion. A car's waiting time is highly related to the period in which the traffic lights are green or red; thus, a proper controlling policy reduces the waiting time and speeds up the car movement. Emergency vehicles always have a higher priority than other cars and need to reach their destination faster. In the last decade, a lot of research has been done to solve the traffic light scheduling issue using artificial intelligence techniques. Reinforcement learning with simulating human learning has the outstanding performance to solve complex problems, such as traffic lights control. In this study, a deep reinforcement learning traffic light control was implemented at a crossroad using real-time traffic information with an emphasis on emergency vehicles. The agent learns to adjust its policies to prioritize the emergency cars over the other cars. The results show less waiting time and speed up the cars' movement. It also indicates the emergency vehicles will cross the intersection with the least delay. With 200 to 600 cars and one emergency vehicle, the average delay decreases 2–16% in total using reinforcement learning. Considering more emergency vehicles, there is a 27–40% decrease in average delay for emergency vehicles. [ABSTRACT FROM AUTHOR]

Cell polarity is a fundamental feature underlying cell morphogenesis and organismal development. In the Arabidopsis stomatal lineage, the polarity protein BASL controls stomatal asymmetric cell division. However, the cellular machinery by which this intrinsic polarity site is established remains unknown. Here, we identify the PRAF/RLD proteins as BASL physical partners and mutating four PRAF members leads to defects in BASL polarization. Members of PRAF proteins are polarized in stomatal lineage cells in a BASL-dependent manner. Developmental defects of the praf mutants phenocopy those of the gnom mutants. GNOM is an activator of the conserved Arf GTPases and plays important roles in membrane trafficking. We further find PRAF physically interacts with GNOM in vitro and in vivo. Thus, we propose that the positive feedback of BASL and PRAF at the plasma membrane and the connected function of PRAF and GNOM in endosomal trafficking establish intrinsic cell polarity in the Arabidopsis stomatal lineage. The polarly localized BASL protein controls asymmetric cell division during stomatal lineage development in Arabidopsis. Here, the authors show that BASL polarization depends on PRAF/RLD proteins that interact with GNOM and suggest a role for endosomal trafficking in establishing polarity. [ABSTRACT FROM AUTHOR]

This paper is devoted to the mathematical modeling of traffic flow in a large automobile network. A statistical model of traffic flow proposed by the authors and designed for full-scale modeling of the operation of large-scale transport systems on long time intervals is used. A model has been constructed for one of the sides of the Moscow Ring Road and experiments have been carried out for two ways of jamming on the highway. The functionality of the naive entry control model has been tested in both types of the experiments. It has been shown that even the simplest method of limiting the incoming flow is efficient in terms of highway travel time losses. [ABSTRACT FROM AUTHOR]

In contemporary urban areas, the construction speed of urban roads lags far behind the growth rate of the number of vehicles. The traffic delay caused by excessive vehicles is a major challenge for modern transportation systems. In this paper, we design an adaptive coordination traffic signal control system based on a three-level framework. In the framework, we propose a hierarchical strategy, which is helpful in avoiding possible offset conflicts and configuring the offsets reasonably. In addition, we establish a multi-intersection traffic signal control model with the goal of minimizing traffic delays. To solve the proposed model, we propose a new algorithm, called the Immune-Fireworks algorithm (IM-FWA), on the basis of the artificial immune and fireworks algorithms. Inspired by the antibody maintenance mechanism, diversity mechanism and communication mechanism of the artificial immune algorithm, IM-FWA can effectively overcome the shortcomings of the fireworks algorithm, such as its limited search range and lack of interaction among fireworks. The experiments show that the proposed model and algorithm have good practicability and that our control system can obtain a better signal timing schedule to effectively reduce traffic delays. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting or prediction plays an important role in the traffic control and management of a city. Existing works mostly train a model using the traffic flow data of a city and then test the trained model using the data of the same city. It may not be truly intelligent as there are many cities around us and there should be some shared knowledge among different cities. The data of a city and its knowledge can be used to help improve the traffic flow forecasting of other cities. To address this motivation, we study building a universal deep learning model for multi-city traffic flow forecasting. In this paper, we exploit spatial-temporal correlations among different cities with multi-task learning to approach the traffic flow forecasting tasks of multiple cities. As a result, we propose a M ulti-city T raffic flow forecasting N etwork (MTN) via multi-task learning to extract the spatial dependency and temporal regularity among multiple cities later used to improve the performance of each individual city traffic flow forecasting collaboratively. In brief, the proposed model is a quartet of methods: (1) It integrates three temporal intervals and formulates a multi-interval component for each city to extract temporal features of each city; (2) A spatial-temporal attention layer with 3D Convolutional kernels is plugged into the neural networks to learn spatial-temporal relationship; (3) As traffic peak distributions of different cities are often similar, it proposes to use a peak zoom network to learn the peak effect of multiple cities and enhance the prediction performance on important time steps in different cities; (4) It uses a fusion layer to merge the outputs from distinct temporal intervals for the final forecasting results. Experimental results using real-world datasets from DIDI show the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Cities world-wide have taken the opportunity presented by the COVID-19 pandemic to improve and expand pedestrian infrastructure, providing residents with a sense of relief and pursuing long-standing goals to decrease automobile dependence and increase walkability. So far, due to a scarcity of data and methodological shortcomings, these efforts have lacked the system-level view of treating sidewalks as a network. Here, we leverage sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and find that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians. Next, we connect these geometries to build a sidewalk network –adjacent, but irreducible to the road network. Finally, we compare a no-intervention scenario with a shared-effort heuristic, in relation to the performance of sidewalk infrastructures to guarantee physical distancing. The heuristic prevents the sidewalk connectivity breakdown, while preserving the road network's functionality. While public space is traditionally shared by pedestrian and cars, the distancing measures imposed by the Covid-19 pandemic have underlined the need for wider pedestrian spaces. Here, the authors take a complex network approach to analyze sidewalk data from cities across the world to evidence a strong unbalance in the space distribution between cars and pedestrian, and propose a strategy to improve urban walkability within a socially distancing context and without compromising road traffic. [ABSTRACT FROM AUTHOR]

The development of domain-independent planners within the AI planning community is leading to "off-the-shelf" technology that can be used in a wide range of applications. Moreover, it allows a modular approach—in which planners and domain knowledge are modules of larger software applications—that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this article, we investigate how the performance of domain-independent planners is affected by domain model configuration, i.e. the order in which elements are ordered in the model, particularly in the light of planner comparisons. We then introduce techniques for the online and offline configuration of domain models, and we analyse the impact of domain model configuration on other reformulation approaches, such as macros. [ABSTRACT FROM AUTHOR]

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice. [ABSTRACT FROM AUTHOR]

Unforeseeable developments will accompany progressive COVID-19 recovery globally. Similarly, science will inform changes amidst its own progress. Social isolation and distancing imposed by the pandemic are likely to result in changed habits, behavior, and thinking paradigms. Inevitably, this should affect the tremendous confusion inhibiting automated urban mobility's evolution. While mobility often seems magnanimously resistant to change, using international data, this analysis shows road traffic, the largest net contributor to global warming, is responsible for even greater damages. The core claim justifies replacing private cars (PCs) by existing and future public transit (PT) vehicles. In testing 17 major cities globally, 94% of the scenarios proved PT superior or equivalent to PCs for reducing travel time. As a result, a foreseeable, future scenario shows potential reduction in car traffic by approximately two-thirds compared with the current situation. In two arenas, proactive government can promote such sustainable urban mobility: (1) developing autonomous vehicles for PT only; (2) coordinating standardization for seamless urban mobility. These global decisions for improving our lives in the future are likely to be better received and understood subsequent to COVID-19, as the focus of our concerns changes from what preoccupied us under the circumstances prior to the pandemic. [ABSTRACT FROM AUTHOR]

With the recent significant growth of virtualization and cloud services, the data center network (DCN) as the underlying infrastructure is more important. The increasing and changing volume of workloads highlights critical issues such as load balancing and energy efficiency in data centers. Large path diversity in DCNs introduces multipath forwarding as a promising approach to improve load distribution. However, the over-provisioned DCNs consume large amounts of power while the network is under full capacity most of the time. Accordingly, this paper proposes BiTE, a dynamic bi-level traffic engineering (TE) scheme in a hierarchical Software Defined Networking (SDN)-based DCN to strike a balance between load balancing and energy efficiency objectives. BiTE consists of decision-making problems at two levels modeled as a multi-period bi-level optimization problem, where each decision maker optimizes one of objectives. According to the inherent complexity of bi-level programming, a co-evolutionary metaheuristic algorithm is proposed for solving BiTE. BiTE performance is evaluated in comparison to NSGA-II algorithm and four previously proposed TE schemes in terms of several load balancing and energy saving metrics under different scenarios. The results show that BiTE performs well in traffic load balancing while preserves the energy efficiency. We apply the Analytic Hierarchy Process (AHP) method to multi-criteria analyze and rank the performance of studied TE mechanisms. AHP results for different scenarios indicate that BiTE is in first or second place in terms of the overall performance score among six studied approaches. [ABSTRACT FROM AUTHOR]

COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for "Hispanic" and "Male" groups; (2) the "hot spots" of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]