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In order to fully understand the research progress of human factors and traffic safety in curve driving, from the perspective of driver-vehicle-road-environment dynamic traffic system, this paper explored the current research status and development trend of human factors of curve driving, and displayed the development process and structural relationship of human factors research of curve driving by using scientific knowledge map. Through the core collection database of Web of Science, 1408 English literatures related to human factors research of curve driving published from 2012 to 2022 (as of October 1, 2022) were obtained, and the literatures in this field were sorted and analyzed based on the VOSviewer visualization software. The results show that China, Tongji University and Accident Analysis and Prevention are the country, institution and journal with the largest contribution rate in the field of human factors research on curve driving. Co-citation analysis shows that the research contents in this field are divided into 5 clusters: driver's visual characteristics, risk of collision, vehicle dynamics characteristics, the influence of traffic engineering facilities on driving behavior, selection of driving speed. The co-occurrence analysis of keywords shows that the topics of curve geometry design and vehicle dynamics, driving behavior and risk, driving speed and safety, behavior prediction and intervention measures are the current research hotspots in the research field. It is found that the development trend of traffic safety improvement in curves is to construct a continuous, consistent, multi-level visual reference frame conforming to driving expectation through visual guiding technology, and summarizes the technical concept of linear visual guidance. This study can provide a reference for the study of human factors of curve driving.

Road traffic safety should be evaluated throughout the entire life-cycle of road design, operation, maintenance, and expansion construction. However, traditional methods for evaluating road traffic safety based on traffic accidents and conflict technology are limited by their inability to account for the complex environmental factors involved. To address this issue, a new road safety evaluation method has emerged that is based on driving behavior. Because drivers' behaviors may vary depending on the driving environment and their personal characteristics, evaluating road safety from the perspective of driver behavior has become a popular research topic. This paper analyzes current research trends and mainstream journals in the field of road safety evaluation of driving behavior. Additionally, it reviews the three most commonly used driving behavior data collection methods, and compares the advantages and disadvantages of each. The paper proposes the main application scenarios of road safety evaluation methods based on driving behavior, such as road design, evaluation of the effects of road appurtenances, and intelligent highways. Furthermore, the paper summarizes a driving behavior index system based on vehicle data, driver's physiological and psychological data, and driver's subjective questionnaire data. A comprehensive evaluation method based on the fusion of each index system is presented in detail. Finally, the paper points out current research problems and the future development direction of the road safety evaluation method based on driving behavior.

Accurately predicting lane-changing behaviors (lane keeping, left lane change and right lane change) in real-time is essential for ensuring traffic safety, particularly in mixed-traffic environments with both autonomous and manual vehicles. This paper proposes a fused model that predicts vehicle lane-changing behaviors based on the road traffic environment and vehicle motion parameters. The model combines the ensemble learning XGBoost algorithm with the deep learning Bi-GRU neural network. The XGBoost algorithm first checks whether the present environment is safe for the lane change and then evaluates the likelihood that the target vehicle will make a lane change. Subsequently, the Bi-GRU neural network is used to accurately forecast the lane-changing behaviors of nearby vehicles using the feasibility of lane-changing and the vehicle's motion status as input features. The highD trajectory dataset was utilized for training and testing the model. The model achieved an accuracy of 98.82%, accurately predicting lane changes with an accuracy exceeding 87% within a 2-second timeframe. By comparing with other methods and conducting experimental validation, we have demonstrated the superiority of the proposed model, thus, the research achievement is of utmost significance for the practical application of autonomous driving technology.

A historic urban district is a quarter built many years ago and contains valuable memories over a limited area with narrow streets for pedestrians, primitive transportation, and outdoor activities. Over the years, many districts have been growing randomly, and motorized transport has enlarged significantly and created severe problems. Improving the situation needs heritage protection and sustainable mobility development on its streets. The desired mobility focuses on managing travel demands, accessibility for all street users, and enhancing the environment. This research presents a proposed approach with an application framework for sustainable mobility development on historic roads according to its current conditions at the strategic level. It includes sequential phases to define desired goals, formulate and evaluate planning scenarios containing different action measures based on sustainability indicators and application of micro-simulation. In case of inappropriate evaluation results, approach phases allow the modification and re-evaluation of additional measures until deciding an acceptable solution. The application is limited to Ras El-Teen Palace Street, prominent in the ancient El Gomrok district, Alexandria and restricted to a Do-Nothing solution and a planning scenario. The application proves the approach's practicality and feasibility by improving traffic behaviour. Furthermore, the planning approach can indicate technical recommendations for further improvement.

Currently, the branch of air transport is one of the most dynamically developing branches of transport. Modern air transport systems provide relatively high quality of services in terms of satisfaction, preferences, and tastes of potential recipients, passengers, and freight operators. Air transport systems use highly advanced technologies, equipment, infrastructure, and appropriate rules and procedures in order to ensure the desired quality of services. The article presents an analysis of the volume of passenger and cargo transport in air transport on the example of the Warszawa Okęcie International Airport (Poland). The analysis covered the years from 2011 to 2021. In addition, the market of air services, and air connections with Warszawa Okęcie International Airport were characterized.

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]

The integrity of the nuclear envelope depends on the function of nuclear pore complexes (NPCs), transport channels that control macromolecular traffic between the nucleus and cytosol. The central importance of NPCs suggests the existence of quality control (QC) mechanisms that oversee their assembly and function. In this perspective, we emphasize the challenges associated with NPC assembly and the need for QC mechanisms that operate at various stages of an NPC's life. This includes cytosolic preassembly QC that helps enforce key nucleoporin–nucleoporin interactions and their ultimate stoichiometry in the NPC in addition to mechanisms that monitor aberrant fusion of the inner and outer nuclear membranes. Furthermore, we discuss whether and how these QC mechanisms may operate to sense faulty mature NPCs to facilitate their repair or removal. The so far uncovered mechanisms for NPC QC provide fertile ground for future research that not only benefits a better understanding of the vital role that NPCs play in cellular physiology but also how loss of NPC function and/or these QC mechanisms might be an input to aging and disease. [ABSTRACT FROM AUTHOR]

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles' energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow's impact on autonomous vehicles' energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles. [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]

Risk assessment in the design of control command and signaling devices (CCS) is one of the elements required by law. These analyses should be carried out at many stages of investment with the participation of various teams. This article presents a risk analysis method based on fuzzy sets, which can support and increase the safety of investment processes involving the railroad traffic control industry. The article analyzes hazards identified in CCS design. These risks were identified using a survey method based on a set of questions prepared by the authors and by conducting interviews among experts from design offices. As part of the survey, responses were obtained from 28 respondents who are specialists in the railway traffic control industry. Workshop meetings were held in six different design offices and at manufacturing plants of motion control systems. The identified risks were analyzed using the FMEA (failure mode and effect analysis) method and the fuzzy set method, as well as various methods of fuzzification and defuzzification. The results of all of the methods were compared with each other. The best solution from the analyzed ones was proposed. [ABSTRACT FROM AUTHOR]

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Several autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi‐scale dense nested deep CNN (MSDN‐DCNN) and regional search grasshopper optimization algorithm (RS‐GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre‐processed, which includes frame conversion, redundancy removal, and image enhancement. The pre‐processed frames are given as input to the MSDN‐DCNN for multi‐scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS‐GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN‐RS‐GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence. [ABSTRACT FROM AUTHOR]

An alternative approach for real-time network-wide traffic control in cities that has recently gained attention is perimeter flow control. Many studies have shown that this method is more efficient than state-of-the-art adaptive signal control strategies for heterogeneously congested urban networks. The basic concept of such an approach is to partition heterogeneous cities into a small number of homogeneous regions (zones) and apply perimeter control to the interregional flows along the boundaries between regions. The transferring flows are controlled at the traffic intersections located at the borders between regions so as to distribute the congestion in an optimal way and minimize the total delay of the system. The focus of current work is the mathematical formulation of the original nonlinear problem in a linear parameter-varying (LPV) form so that optimal control can be applied in a (rolling horizon) model predictive concept. This work presents the mathematical analysis of the optimal control problem as well as the approximations and simplifications that are assumed in order to derive the formulation of a linear optimization problem. Numerical simulation results for the case of a macroscopic environment (plant) are presented in order to demonstrate the efficiency of the proposed approach. Results for the closed-loop model predictive control scheme are presented for the nonlinear case, which is used as "benchmark," as well as the linear case. Furthermore, the developed scheme is applied to a large-scale microsimulation of a European city with more than 500 signalized intersections in order to better investigate its applicability to real-life conditions. The simulation experiments demonstrate the effectiveness of the scheme compared with fixed-time control because all of the performance indicators are significantly improved. Funding: This work was supported by Dit4Tram (Distributed Intelligence & Technology for Traffic & Mobility Management) project from the European Union's Horizon 2020 research and innovation programme under [Grant 953783]. [ABSTRACT FROM AUTHOR]

Intense vehicle traffic is one of the main disorders in large cities, and in Brazil, where the responsible authorities have not trained the road networks, overcrowding in traffic causes even more obstacles. The applications of computational intelligence techniques in traffic are very broad, with emphasis on smart traffic lights. For the design of intelligent traffic lights, this work proposes the use of Fuzzy Logic, and has as main objective the automatic generation of fuzzy systems using evolutionary fuzzy models for this purpose. To achieve this objective, the traffic simulation software SUMO is used, which allows the elaboration of scenarios of intersections controlled by traffic lights. In these scenarios, the traffic performance is evaluated based on different adjustments in the membership functions and in the set of rules of the fuzzy system that controls the traffic lights, and these adjustments are performed by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). When comparing the traffic performance with traffic lights controlled by fuzzy and fuzzy with optimized hyperparameters, there are important improvements in the analyzed traffic variables, such as waiting time and car queue size/length, in addition to reducing the emission of toxic gases and fuel consumption. Thus, this work highlights the importance of employing evolutionary fuzzy models in hyperparameters optimization. [ABSTRACT FROM AUTHOR]

Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios. In general, calibration is performed manually by traffic engineers who use their knowledge and experience to adjust the parameters of the simulator. Unfortunately, there is still no systematic and automatic process for calibrating traffic simulation networks, although some methods have been proposed in the literature. This study proposes a methodology that facilitates the calibration process, where an artificial neural network (ANN) is trained to learn the behavior of the transport network of interest. The ANN used is the Multi-Layer Perceptron (MLP), trained with back-propagation methods. Based on this learning procedure, the neural network can select the optimized values of the simulation parameters that best mimic the traffic conditions of interest. Experiments considered two microscopic models of traffic and two psychophysical models (Wiedemann 74 and Wiedemann 99). The microscopic traffic models are located in the metropolitan region of São Paulo, Brazil. Moreover, we tested the different configurations of the MLP (layers and numbers of neurons) as well as several variations of the backpropagation training method: Stochastic Gradient Descent (SGD), Adam, Adagrad, Adadelta, Adamax, and Nadam. The results of the experiments show that the proposed methodology is accurate and efficient, leading to calibration with a correlation coefficient greater than 0.8, when the calibrated parameters generate more visible effects on the road network, such as travel times, vehicle counts, and average speeds. For the psychophysical parameters, in the most simplified model (W74), the correlation coefficient was greater than 0.7. The advantage of using ANN for the automatic calibration of simulation parameters is that it allows traffic engineers to carry out comprehensive studies on a large number of future scenarios, such as at different times of the day, as well as on different days of the week and months of the year. [ABSTRACT FROM AUTHOR]

The bridge is the key node of expressway operation. The special structural characteristics of the bridge deck and pavement connection lead to the existence of a critical point of icing, so there is a greater operational risk. In order to study the safety state of vehicle operation under the critical conditions of the road bridge connection, it is of great significance to improve the level of expressway traffic safety. Aiming at the problem of expressway operation risk behavior under the critical state of bridge icing and subgrade section not icing, the friction coefficient of dry pavement, light snow pavement and thin ice pavement under the critical state was studied based on the meteorological environment simulation experiment system. Based on the friction coefficient of different road conditions and combined with the driving simulation experiment system, the driving behavior data of average speed, longitudinal acceleration, lateral acceleration and steering wheel angle were collected. The results show that the characteristics of driving behavior change at the road bridge junction in three different critical states are significantly different. The driving risk value under the road surface state in the road bridge junction 3 is calculated by the efficiency coefficient method. It is concluded that the critical state at the road bridge junction has a higher risk level. [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]

The information exchange among vehicles and road infrastructure with wide sensing range can be accomplished by roadside units (RSUs). Communication delays occur and are influenced by different approaches of deployment of RSUs, which increases the complexity of vehicle automatic control in a mixed traffic flow. Therefore, a speed control strategy was proposed for mixed traffic flow considering RSU deployment distance, interaction radius, and communication delay, aimed at enhancing traffic safety and efficiency. The characteristics of RSU deployment were modeled as functions, where an adaptive efficiency function was proposed to evaluate different RSU deployment plans. An improved speed control model was then developed based on the characteristics of the RSU in the mixed traffic flow. In addition, numerical analysis and simulation were carried out to evaluate vehicle stability and traffic throughput based on the improved speed control model. The analysis results show that the proposed model is stable under different updating frequencies, with the best stability achieved when the velocity update frequency is 500 and the velocity update time interval is 0.01 s. Moreover, it was found that the spatial occupancy rate increases with the increment of penetration rate of autonomous vehicles, which improves the rate by at least 20% compared to the no-control scenario. Additionally, the average speed is also increased by 30% while under the influence of communication delays. This paper investigated the relationship between characteristics of RSUs and traffic performances, which is essential for DOTs to develop an effective RSU plan to adapt to the mixed traffic flow. [ABSTRACT FROM AUTHOR]

This study proposes a new approach for measuring field pedestrian waiting delay and developed a new pedestrian waiting delay model for signalized midblock crosswalks under mixed traffic conditions. Eight signalized midblock crosswalks were selected in the southern part of India, and data were collected using videographic technique. Pedestrian characteristics, cycle time, and phase lengths were extracted using frame-by-frame extraction technique with a least count of 0.03 s. In the current study, field pedestrian waiting delay was measured by plotting queue length against cycle time. The proposed approach was validated using the conventional approach for measuring field waiting delay. A new pedestrian waiting delay model was developed for signalized midblock crosswalks under mixed traffic conditions. Two new coefficients, i.e., the coefficient of nonuniform arrival rate (Δ1) and the coefficient of noncompliance behavior (Δ2), were incorporated into the existing Webster delay model. The former is modeled using regression analysis, and the latter is calculated as the proportion of violators. The proposed pedestrian waiting delay model was validated with the field waiting delay. The proposed new approach for measuring field waiting delay had an error of 5% compared with the conventional approach for measuring waiting delay. Hence the proposed approach for measuring pedestrian waiting can replace the conventional method for measuring field waiting delay because the new method minimizes the data extraction time. The proposed pedestrian delay model was found to be accurate in estimating the pedestrian waiting delay at signalized midblock crosswalks. The mean absolute percentage error (MAPE) value of the new pedestrian waiting delay was found to be less than 12%. The proposed waiting delay model can be used to define the level of service (LOS) at signalized midblock crosswalks, and will aid traffic engineers and urban planners in improving pedestrian facilities and safety [ABSTRACT FROM AUTHOR]

Routing is the key issue for Vehicular Networks since each node in the network has mobility. The dynamic nature of vehicular networks increases with mobility and the consequence is reflected in controlling traffic overhead. As a result, the routing technique and the type of node mobility information are completely dependent on building dependable end-to-end communication links. To address these challenges, a Reliable Cluster-based Multi-Hop Cooperative Routing (RCCR) strategy was proposed by using velocity, distance, and link quality parameters. This algorithm obtains the tradeoff between Quality of Service (QoS) and mobility constraints over link parameters. It improves routing scalability by electing cluster heads and selecting Multi-Point Relays (MPRs) while considering mobility constraints and QoS needs. The proposed technique determines the link quality for every pair of nodes based on values of signal strength and distance parameters. The relay vehicles are chosen based on the highest possible QoS value, which is calculated to assure route stability, reliability, and durability. The heuristic limitations of the multi-point relay selection strategy are handled by considering the link quality, distance from the source vehicle, and cluster-head coverage area to enhance the performance of the multi-hop network with tolerable End-to-End transmission delay. Further, we obtained an optimal number of cooperative vehicles in every hop. Finally, simulation results demonstrate the effectiveness of the proposed algorithm compared to other state of art algorithms. [ABSTRACT FROM AUTHOR]

Congestion on expressways usually occurs during the peak hours of urban traffic flow, mainly in the bottleneck area of expressways with reduced capacity, the congestion leads to vehicle queuing, and the traffic flow forms a "stop and go" wave, resulting in frequent vehicle starts, which further reduces the traffic efficiency in the bottleneck area and forms a vicious circle. Aiming at the congestion in the bottleneck area of expressway during peak hours, based on the simulation of expressway traffic flow using the cellular transmission model, an optimization model of variable speed limit control based on the cellular transmission model is established. First, use python programming to complete the realization of the basic traffic flow simulation function and speed limit control function of the CTM model, and build the simulation model with the Chengdu Riyue Avenue Expressway as an example. Then, the closed-loop control method is used in the simulation model to realize the variable speed limit control of the traffic flow system. Finally, the two evaluation indexes of the total delay time and the total ideal vehicle speed difference are used to evaluate the implementation effect of the optimal control plan of the speed limit control strategy. The results show that the variable speed limit control has an obvious optimization effect on reducing the total delay time (TTD) and the total ideal speed difference (TVD) of the system during congestion: using the variable speed limit control optimization strategy and infinite speed control conditions in comparison, the total delay time has been reduced by 35.8%, and the total ideal speed difference has been reduced by 10.7%. When the traffic density is in the critical state of congestion, the implementation of variable speed limit control on the upstream of the bottleneck area can effectively alleviate the overall congestion of the system. [ABSTRACT FROM AUTHOR]

Radius of road curvature and the information of preceding vehicles have a significant influence on traffic flow in the vehicle-infrastructure cooperation environment. However, the existing car-following model is subjective in considering the type of preceding vehicles' information, and does not comprehensively consider the synergistic effect of road curvature radius and preceding vehicles' information. Therefore, in this study, an intelligent driver model for curved roads considering multi-preceding vehicles' information is established in the vehicle-infrastructure cooperation environment. Then, using the Lyapunov method to analyze the stability of the extended model, the linear stability condition is obtained, and the influence law of radius of road curvature and weight coefficient of the preceding vehicle on traffic flow is mastered. Finally, the theoretical analysis of the model is verified by numerical simulation experiments. The simulation results are consistent with the theoretical analysis, and both show that the stability of traffic flow increases with the increase of radius of road curvature and weight coefficient of the preceding vehicle. This shows that the extended model can not only accurately describe the car-following behavior of vehicles on a curved road in the vehicleinfrastructure cooperation environment but also stabilize the traffic flow and suppress traffic jams. [ABSTRACT FROM AUTHOR]

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible–Infected–Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks. [ABSTRACT FROM AUTHOR]

To alleviate the congestion caused by the tidal traffic and to improve the operational performance of both directions in arterials, this paper proposes a coordinated control model for arterials with asymmetric traffic demands in both oversaturated and unsaturated directions. The model utilizes a three-step architecture: (1) A mixed-integer linear programming model is built to maximize the throughput according to the relationship between traffic supply and demand as well as the balance between inflow and outflow. (2) A quadratic programming model is constructed to minimize the vehicle delay in the oversaturated direction based on Lighthill-Whitham-Richards theory. (3) A mixed-integer linear programming model is built to maximize the variable bandwidth in the unsaturated direction by introducing the relaxation variable. Furthermore, a three-step algorithm is used to solve the model and obtain the optimal arterial signal timing. Finally, a simulation model is put forward to verify the coordinated model. The results indicate that the model meet the asymmetric traffic demand through optimizing the arterial throughput, reducing delay in the oversaturated direction and maximizing the green wave bandwidth in the unsaturated direction. [ABSTRACT FROM AUTHOR]

With the emergence of the internet and cellular networks, interest in using computer communication networks and communication systems has skyrocketed. However, unusual occurrences, like cyber attacks, power outages, network congestion, equipment failures, etc. lead to abrupt changes in the state of the system and pose a significant risk to these systems. Consequently, some/all users can be promptly eliminated from the system. These type of scenarios can be well modeled by the multi-server catastrophic queueing model (MSCQ). This article elaborates a MSCQ with the consideration of retrial phenomenon and preemptive repeat priority (PRP) scheduling. For brevity, the working of system before and after the catastrophe phenomenon is referred to as the normal and catastrophic environment, respectively. This study identifies the incoming traffic as calls which are further categorized on the basis of the model operation scenarios. In normal operation scenario, the calls are classified as handoff ( H C ) and new calls ( N C ). This study provides priority to H C over N C using PRP. Whereas, in the catastrophic environment, when the calamity strikes, the whole system is rendered inoperable, and all types of busy/waiting calls are flushed out. To reinstate services in the concerned region, a set of backup/standby channels are quickly deployed. In response to the emergency circumstances in the area, the calls made to emergency personnel are referred as emergency calls ( E C ). Hence, in this case, the incoming calls are categorized as H C , N C , and E C . The E C are given priority using PRP over N C / H C due to the pressing need to save lives in such crucial situations. The system is modeled by a multi-dimensional Markov chain and by demonstrating that the Markov chain satisfies the requirements for asymptotically quasi-Toeplitz Markov chains, the chain's ergodicity conditions are established. Furthermore, a non-dominated sorting genetic algorithm-II method has been employed to define and address an optimization problem to achieve the optimal number of resources. [ABSTRACT FROM AUTHOR]

This paper concentrates on the issue of detecting abnormal flows in distributed monitoring systems, which has many network management applications such as anomaly detection and traffic engineering. Collecting massive network traffic in real‐time remains a large challenge due to the limited system resource. Most existing approaches perform abnormal flow detection at one measurement point, while they cause large computation and memory overhead for recovering abnormal flows. In this paper, we propose a novel data streaming method that supports accurate abnormal flow detection with a low memory requirement. The key idea of our method is that each monitor compresses flow information to summary data structure, sends the generated data structure to the controller; then the controller aggregates the received data structures, recovers candidates of abnormal flows and estimates their size and change to find abnormal flows on the basis of the aggregated data structure. The experimental results based on real network traffic show that the proposed approach can detect up to 97% of abnormal flows with low memory and update requirements in comparison with related approaches. [ABSTRACT FROM AUTHOR]

A widely used technique for locating the position of a train during its journey involves electrical detection: on a section of track, the train closes ("shunts") a circuit dedicated to its location via its wheels and axles. The quality of the wheel–rail contact is therefore particularly important for signalling management and traffic control. In this paper, we show that the current flowing through the track circuit induces a permanent modification and a nonlinear, frequency-dependent behaviour of the electrical contact. We propose an analytical expression that describes the evolution of the measured voltage as a function of current and frequency for these nonlinear behaviours. This behavioural law was obtained using experimental measurements on a real train and could therefore be integrated into a global model describing the track system. [ABSTRACT FROM AUTHOR]

Over the last two decades, many researchers have focused on providing new ideas and frameworks to help improve conventional bridge inspection planning approaches, however, little guidance is provided for implementing these new ideas in practice, resulting in limited change. Accordingly, this qualitative study aims to identify the factors that can help improve research products and accelerate research transfer to bridge inspection departments with the goal of enhancing bridge inspection practice. This study used semi-structured interviews, written interviews, and questionnaires for data collection to provide rich results. Responses from twenty-six bridge personnel from state Departments of Transportation (DOTs) across the United States (U.S.) were included in this study. The study found that most participants support a fixed inspection interval over a variable interval since fixed intervals are easier in scheduling and budget planning. Also, participants indicated that the barriers hindering the use of nondestructive techniques are the training required by inspectors, traffic control, and the required access equipment. The study presents the factors change leaders should focus on to facilitate organizational change in DOTs such as enhancing the capacity of DOT staff members and gaining support from the Federal Highway Administration (FHWA) [ABSTRACT FROM AUTHOR]

The PTV VISION VISUM software tool, which is the world's leading traffic analysis, forecasting and data management software that reliably models all road users and their interactions. The subject of analysis in this paper will be the planning of a sustainable urban transport system, in this case the public transport of the city of Prilep with the help of the software tool PTV VISION VISUM. In the paper that is dedicated to public transport, i.e. the steps that must be taken to obtain the sizes, from the creation of an operator, type of public transport, coordination group, placement of stands, lines of public transport, matrixes, uses, rewritings and similarly. The outputs will be shown as a combination of private and public transport and the application of the modal distribution of trips with the help of a legend and a detailed description of the elements of the pad. In addition to the VISUM software tool, the VISSIM tool will also be shown, with the help of which we will simulate the movement of the public city transport in the city of Prilep. [ABSTRACT FROM AUTHOR]

Many countries face the traffic related issues regardless of the wide effects adopted for developing and improving the traffic flow. Depending on the drawbacks of existing traffic management systems like inefficient time management, absence of priority options and lack of dynamic and adaptive services, the need for the incorporation of intelligent concepts is mandatory. This facilitates the replacing of traditional approaches with an array of sensors and artificial intelligence (AI) through vehicle traffic control system. In this paper, the intelligent traffic system (ITS) is proposed on the basis of techniques such as internet of vehicles (IOV) and vehicular adhoc network (VANET). A road side unit (RSU) controller connected with the traffic control system acts as the key component of ITS. The on board unit (OBU) is fixed with each vehicle, which exchanges the real time information with RSU (road side unit) and other OBUs. Additionally, an adaptive traffic management algorithm (ATMA) is provided to govern the ITS for the generation of high performance outputs. Furthermore effectiveness of ATMA in the proposed work is proved by simulation using Python. Eventually, the ATMA is compared with fixed-time (FT) algorithm and traffic signal control algorithm (TSCA) under various traffic conditions. The findings depicts that the proposed ATMA-based ITS outperforms the current traffic management systems in terms of average waiting time (AWT) obtained as 277.5s with respect to number of vehicles and 282.14s with respect to number of roads at an intersection. Added to this average number of serviced vehicles is given by 1731 with respect to the number of roads. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major, but neglected global public health problem, requiring intensive efforts for effective and sustainable prevention. Of all the systems that people must deal with daily, road transport is the most complex, and the most dangerous. Road safety is an issue of national concern, considering its magnitude, gravity and the consequent negative impacts on the economy, public health and the general welfare of the people in South Africa. Road traffic accidents are a collective responsibility and all major stakeholders need to play their role in road safety and ensure that road users are safe whenever they are on the road. One of the major challenges for road traffic authorities in preventing road traffic accidents in South Africa is the inability of road users to understand and interpret the rules of the road and this contributes to a high rate of fatalities on the road. This qualitative article adopted the non-empirical research design: Systematic review. This study is completely based on the secondary data. A systematic review was done in detail for the collected literature. The key findings indicated that road users do not obey the rules of the road: jaywalking, speeding, driving and walking while impaired contribute to a high rate of fatalities on the road. The findings further indicated that law enforcement officials are not enforcing the law consistently which compromises the road safety project. Based on the findings, the author provided possible recommendations such as providing road safety campaigns throughout the year; capacitating law enforcement officials with resources to prevent road traffic accidents. [ABSTRACT FROM AUTHOR]

As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%. [ABSTRACT FROM AUTHOR]

We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time series of rockfall events with volumes of 1 m 3 and greater since 2018 and serves as automatic traffic control for an important main road. In the period between January 2018 and October 2022, 6743 events were detected, which is 2 orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow area, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that, due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings. [ABSTRACT FROM AUTHOR]

The Smart Vehicle (SV) with multiple onboard sensors is one of the indispensable tools to collect dynamic traffic information in the Intelligent Transport System (ITS). In this paper, we aims at jointly relieving the cellular network load and the transmission energy consumption during the SVs' information uploading. A Green Cluster-based Traffic Information Acquisition Method named GCTIAM is proposed, where an edge clustering management architecture is designed to control the traffic information upload cycles of each SV. On this basis. We build an optimal clustering mechanism to further improve the energy utilization while relieve the cellular network load. The mechanism optimizes the cluster iteration radius and divides SVs into different competitive areas according to their movement characteristic. An adaptive multi-parameter iteration among the same competitive area will be executed to decide the data transmission mode and build optimal cluster structures by considering dynamic parameters such as link duration time, uploading data volume etc.. The simulation results show that the proposed scheme can reduce the transmission energy consumption by 81% while cut down the cellular access rate up to 70%. [ABSTRACT FROM AUTHOR]

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Lane-changing prewarning is an active management measure used to mitigate the impact of incidents on expressways. This measure affects the operational efficiency and safety of traffic flow upstream of the incident location. To evaluate the effectiveness of this measure in mitigating incident-induced congestion and enhancing traffic safety, it is essential to model and simulate traffic flow on expressways, followed by analyzing the simulation results. In this study, we use the lane-changing selection cell transmission model (ls-CTM) to investigate the impact of setting lane-changing prewarning information on expressway traffic flow. By enhancing the composition of flow within the diverging cell in CTM, traffic can determine its downstream direction based on lane-changing probability rather than the path. A case study was conducted to investigate the impact of prewarning distances of 50, 100, and 150 m on road operational efficiency and traffic safety under different traffic volumes. The effectiveness and applicability of three measures to improve traffic safety were also summarized. The research results can provide more rigorous and effective decision-making support for expressway traffic management and control. [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]

In autonomous vehicles (AVs), ensuring pedestrian safety within intricate and dynamic settings, particularly at crosswalks, has gained substantial attention. While AVs perform admirably in standard road conditions, their integration into unique environments like shared spaces devoid of traditional traffic infrastructure control presents complex challenges. These challenges involve issues of right-of-way negotiation and accessibility, particularly in "naked streets". This research delves into an innovative smart pole interaction unit (SPIU) with an external human–machine interface (eHMI). Utilizing virtual reality (VR) technology to evaluate the SPIU efficacy, this study investigates its capacity to enhance interactions between vehicles and pedestrians at crosswalks. The SPIU is designed to communicate the vehicles' real-time intentions well before arriving at the crosswalk. The study findings demonstrate that the SPIU significantly improves secure decision making for pedestrian passing and stops in shared spaces. Integrating an SPIU with an eHMI in vehicles leads to a substantial 21% reduction in response time, greatly enhancing the efficiency of pedestrian stops. Notable enhancements are observed in unidirectional (one-way) and bidirectional (two-way) scenarios, highlighting the positive impact of the SPIU on interaction dynamics. This work contributes to AV–pedestrian interaction and underscores the potential of fuzzy-logic-driven solutions in addressing complex and ambiguous pedestrian behaviors. [ABSTRACT FROM AUTHOR]

The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road. [ABSTRACT FROM AUTHOR]

Most road traffic crash reports published in Nepali media provide little information about their causes, impacts, or preventability. Three workshops involving 31 journalists from diverse media platforms were held using published World Health Organisation resources, to provide training in good road safety reporting. Participants were invited to complete a survey exploring their motivation for participation and knowledge gained. Thirteen (42%) participants responded, reporting the acquisition of new knowledge for content generation, finding facts, active application of learning, and purposeful communication, and an intention to apply the skills learned in their work. A before and after assessment of 94 media outputs from workshop participants found an improvement in the use of story angles and ideas recommended in WHO resources. The study suggests that journalists are interested in developing their road safety reporting skills and that running such workshops has the potential to improve responsible reporting and the promotion of road safety. [ABSTRACT FROM AUTHOR]

The main goal of this article is to present the possibility of using artificial intelligence in operational application of railway infrastructure manager. Railway 4.0 can be controlled fully digitally - operational applications based on the artificial intelligence can optimize the technology of railway traffic control. Thereafter, a wide range of equipment could be controlled by application through a secure interface. [ABSTRACT FROM AUTHOR]

The automatic driving vehicle has to enter the mixed traffic signal free intersection of people and vehicles. It is necessary to ensure the interaction safety between pedestrians. In order to solve this problem, the non self driving vehicle was taken as the research object, and its interaction with pedestrians at no traffic signal intersection was explored. Two intersections without traffic lights in Beijing were selected as the research scene for long-term shooting. Based on the video data, the individual attribute variables of pedestrians, pedestrian crossing behavior variables, vehicle crossing behavior variables and gap data were extracted, and the pedestrian crossing was divided into two stages before and during crossing. The results show that the crossing stage has a significant impact on the crossing time of pedestrians, and the crossing intersection has no significant impact on the crossing time of pedestrians. For the waiting time before crossing, the factors affecting it in the case of right turn lane are the number of vehicles rejected by pedestrians and the direction from which they come, and the factors affecting it in the case of no right turn lane are the number of vehicles rejected by pedestrians and the starting position. For the crossing time in crossing, the factors affecting it with or without right turn lane are the number of crossing people and the behavior of vehicle 1. In the future, when driving a vehicle to a traffic signal intersection, the result can be used to identify pedestrians and determine the travel time of traveller, so as to make corresponding measures in time. [ABSTRACT FROM AUTHOR]

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics. [ABSTRACT FROM AUTHOR]

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm. [ABSTRACT FROM AUTHOR]

There have been limited efforts to investigate the potential of using detailed trajectory data obtained from connected vehicles and/or other sensors in deriving metrics for use in real-time assessment of traffic safety. This study investigated the use of disturbance metrics for this purpose. The disturbance metrics considered were the number of oscillations (NoO) and a measure of disturbance durations in terms of the time exposed time-to-collision index (TETIndex). The time exposed time-to-collision (TET) has been widely used as a safety surrogate measure, but there is no study on the identification of its thresholds to justify activating plans to mitigate unsafe conditions in real-time operations. This study found a TETIndex of 0.03 and NoO of 10% can be used to recognise unsafe conditions in the case study of this research. Since estimation of the TETIndex requires the location and speed of both leading and following vehicles and therefore cannot be measured accurately with low sample sizes of vehicle trajectories, a regression model was derived to estimate the TETIndex based on speed parameters. It was also found that the investigated disturbance metrics and associated models and thresholds are significantly related to crash occurrence and can thus be used in the activation of transportation management strategies to reduce the probability of unsafe traffic and ease traffic disturbances that have an adverse impact on traffic safety. [ABSTRACT FROM AUTHOR]