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This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow smoothing technology may improve the user experience and operations of managed lanes or bridges, however it may require external incentives such as reduced tolls to entice the traveling public to use it. This must be matched with other goals such as verifying vehicle occupancy. It might be possible for some hybrid solution that addresses both challenges to provide a way forward. A concept of operations needs to be developed specifically for a target road geometry and a California partner. This concept should benefit from lessons learned from previous pilot projects and will need to be defined so as to achieve both (1) a penetration rate sufficient to achieve measurable effects; and (2) sufficient quality and quantity of data to confirm benefits.

This project is to investigate how accessibility of city blocks is quantified through the transport systems and real traffic flow datafrom the Los Angeles Metropolitan Area. The authors investigate the reachability problem and provide a solution with a functional system that is capable of visualizing the reachability map (isochrone). Unlike other studies, this approach is data-driven and does not depend on mathematical graph-theory to compute the isochrone which requires intensive computation. Instead, it focuses on directly processing the large amount of traffic flow data that the Integrated Media Systems Center at USC has collected from the Regional Integration of Intelligent Transportation Systems (RIITS) for more than 10 years under the Center’s existing Archived Traffic Data Management System (ADMS) project. The reachability map construction is based on vehicle trajectories so the researchers devised the Data-Driven Trajectory Generator (DDTG), a data-driven, model-free, and parameter-less algorithm for generating realistic vehicle trajectory datasets from ADMS data. Since real world traffic is incomplete with lots of temporal and spatial missing data, the researchers studied imputation and interpolation methods to complete the dataset. Their experiments with real-world trajectory and traffic data show that the datasets generated by DDTG follow distributions that are very close to the distributions of a real trajectory dataset. Furthermore, to demonstrate the resultsfrom the proposed research, a web application was developed in which users can select a location, travel time, and the time of year to see the evaluated accessibility info in the form of an isochrone map. The outcomes of this project—synthetic vehicle trajectory dataset and reachability map construction—will be helpful in evaluating accessibility of city blocks for transport systems over a large area, essential for policymakers for effective city planning as well as to improve the well-being of citizens.View the NCST Project Webpage

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology's efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads. [ABSTRACT FROM AUTHOR]

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a "teacher-student" distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the "parallel-fusion" structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns. [ABSTRACT FROM AUTHOR]

Now-a-days since number of vehicles are growing day by day on the roads in urban and metropolitan area, hence traffic jam is becoming very common problem which everyone is facing time to time. So it becomes very crucial to forecast traffic to avoid the regular traffic congestion situation in daily life. In today's intelligent traffic system, reliable and precise traffic forecasting in metropolitan road networks is critical. Many models based on classical methods have been presented to address this; however, they lag in certain circumstances, failing to incorporate spatial and temporal dependency in the environment. As a result, this research aims to solve the geographical and temporal dependency problem that plagues most traffic forecasting models. Graph Convolution Network (GCN) and Gated Recurrent Unit (GRU) are employed in conjunction with an attention-based model for more accurate traffic forecasts in cities. A combined model called Spatio-Temporal Attention-based Model with GCN and GRU (ST AGG) is employed to anticipate traffic. ST-AGG model mainly focuses on extracting the relevant information from all the input given to the attention-based model. The spatial dependency is determined by using GCN and the temporal dependency is determined by using GRU, used in the ST-AGG model. It assists in short-term traffic forecasting as well as long-term traffic forecasting. The results demonstrate that the suggested ST AGG has properly predicted the volume of traffic on city roadways in real time with greater accuracy. The model depicts how traffic is influenced by geography and time. The Shen Zhen (SZ) car dataset from China and the Los Angeles (LAS) dataset from California are used to test the suggested model. And the result shows that Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) have reduced by up to 2.7% less, and accuracy has increased by 2.7% more compared to the previous model ST GCN for the SZ vehicle dataset. Similarly, although the accuracy has not changed significantly for the Loss Angeles data set, RMSE and MAE have reduced by 3.71% less as compared to the previous ST GCN model. The proposed model is more effective than the prior models like GCN, GRU, and ST GCN. [ABSTRACT FROM AUTHOR]

The paper aims to investigate the trust-attachment mechanism and understand how viewers build trust relationships in the live-streaming context. We build a theoretical model by incorporating the characteristics of the brand, live streaming, and streamer to understand the trust-attachment mechanism in live streaming. 359 useful survey responses were collected. The results identified antecedents of viewers' trust perceptions. Trust transfer from live streaming to brand and streamer, the positive effects of trust in brands and streamer on attachment to brands, and the positive association between trust in streamers and attachment to streamers were also confirmed. This study contributed to the live streaming studies by developing a theoretical model to uncover the trust-attachment mechanism and understand the trust transfer mechanism. The high flow of customer traffic brought by the streamers does not necessarily transfer to the fixed customers of the brand. The current work has originality in investigating (1) different categories of viewers' attachment (2) the influence of diverse types of trust on attachment and (3) the attachment transfer phenomenon in live streaming. [ABSTRACT FROM AUTHOR]

Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications. [ABSTRACT FROM AUTHOR]

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called "progressive pretraining (PPT)." We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular. [ABSTRACT FROM AUTHOR]

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, N O x , P M 2.5 , and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution. [ABSTRACT FROM AUTHOR]

The present study aims at investigating the main and interaction effects of contributing factors from multiple risk sources (i.e. traffic flow characteristics, roadway design and weather conditions) on the real-time crash risk for urban roads. A mixed logit model is used to characterise heterogeneity induced by unobservable factors, and result shows that the mixed logit model achieves better goodness-of-fit than the binary logit model. It is found that the multiple risk sources such as roadway design (i.e. length of road segment, road hierarchy), traffic flow characteristics (i.e. average speed, average travel time, congestion level), and weather conditions (i.e. wind speed) have significant effects on the real-time crash risk. Moreover, interactions between the multiple risk sources such as roadway design and traffic flow characteristics (i.e. SD of speed and length of road segment, road hierarchy and average travel time), traffic flow characteristics and weather conditions (i.e. average travel time, temperature and wind speed), and roadway design and weather conditions (i.e. length of road segment and wind speed) are found to be significantly correlated to the real-time crash risk. Such findings can be potentially used for the development of real-time management measures that could mitigate the high-risk situations of urban roads. [ABSTRACT FROM AUTHOR]

The weaving section on the freeway will be an active bottleneck due to vehicles' mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix. [ABSTRACT FROM AUTHOR]

Connected vehicles (CVs) are becoming increasingly prevalent in today's transportation systems, and understanding their behavior in mixed traffic flow is crucial for enhancing traffic efficiency and safety. This paper presents a comprehensive study investigating the impact of CV drivers' compliance and aggressiveness on mixed traffic flow through simulation experiments. The unique contribution of this research lies in the adoption of a clustering method to classify CV drivers' compliance and aggressiveness based on trajectory data captured by Unmanned Aerial Vehicles (UAVs). This approach allows for the accurate calibration of car-following and lane-changing models, surpassing previous methodologies. The study outlines two primary methods: the intelligent driver model (IDM) with driver compliance (CVs-IDM) and the lane-change 2013 model with drivers' style. These methods are applied to simulate various scenarios of mixed traffic flow, considering different CV penetration rates and driver types. The pivotal findings reveal that higher CV penetration rates lead to reduced traffic flow disturbance, improved safety, and enhanced efficiency. Specifically, CV drivers exhibiting high compliance and normal aggressiveness demonstrate optimal performance in terms of disturbance reduction, safety, and overall efficiency. This research offers valuable insights for policymakers and practitioners. It recommends increasing the CV penetration rate in mixed traffic flow to enhance overall efficiency. Moreover, selecting the appropriate CV driver type based on the penetration rate can further optimize traffic flow, positively impacting transportation systems and promoting safer and more efficient mixed traffic environments. [ABSTRACT FROM AUTHOR]

When a work zone is located at an intersection, it greatly reduces the capacity and change the traffic flow characteristics. However, the impacts of work zones have not attracted much attention, and the signal control method of the intersection with a work zone has not been investigated yet. This paper focuses on a specific type of work zone, which is located within the area of an intersection. The saturation flow rate model, traffic wave model, traffic delay model, and emptying time model are proposed to capture the traffic flow characteristics of the intersection with an island work zone, and a signal control model is further put forward. The real data is collected to calibrate, validate, and evaluate the proposed models. The results indicate that the proposed signal control model can reduce about 15.6% of queue length and 17.2% of traffic delay for the intersection with an island work zone. [ABSTRACT FROM AUTHOR]

Many enclosed public spaces with multiple destinations often feature obstacles, such as columns within the building space and dense crowds, which adversely affect the recognition and guidance of pedestrian sign. To mitigate this issue, enhancing traffic efficiency necessitates the implementation of auxiliary guidance signs. However, experience-based schemes for positioning these signs often require iterative adjustments to achieve the desired effect. Thus, this paper endeavors to address this challenge by analyzing the relationship between obstacles and pedestrians' visual blind areas. It establishes a mathematical model to describe pedestrian visual perception and its correlation with crowd density and individual visual field function. The model employs the effective aggregation area of the population exceeding a certain density threshold as its primary variable and enhances this variable's function. Subsequently, it formulates an effect function that yields a substantial improvement in various combinations and permutations of auxiliary guidance signs. To exemplify this research, a large subway transfer hall with multiple destinations serves as a simulation model. The study calculates the optimal combination of auxiliary guidance sign placements that align with the enhancement effect function. Based on this combination, an optimized layout plan is derived, resulting in a significant improvement. The results demonstrate that the auxiliary guidance sign layout scheme proposed in this study markedly enhances traffic efficiency in enclosed public spaces. This research serves as a valuable reference for decision-makers seeking to optimize traffic flow in such spaces. [ABSTRACT FROM AUTHOR]

With the continuous acceleration of urbanization, traffic congestion, traffic accidents, and urban environmental problems are becoming increasingly serious, which negatively impacts the lives of city residents. With today's urbanization trend, traffic management is a pressing issue, and the safety and smoothness of highways profoundly affect a city's economy and quality of life. As a result, the intelligent inspection robot has entered the public view. It has the advantages of stability and efficiency, can continue to work in a high-intensity state, and helps reduce a lot of human workloads. Firstly, an intelligent transport monitoring system based on the Internet of Things (IoT) is proposed. This system integrates deep learning and artificial intelligence technology, which can quickly query traffic parameters, environmental parameters, and violations that may cause traffic accidents. Secondly, an intelligent inspection robot is introduced to monitor road traffic flow and violation records in real-time, which provides technical support for further scientific management of road traffic. Finally, the intelligent monitoring system's sensitivity and improvement measures are analyzed using the Simultaneous Localization and Mapping (SLAM) algorithm results, making intelligent traffic monitoring more popular. A section of closed safety road is selected for the inspection robot test. The results reveal that (1) the urban transportation model based on the IoT can meet the architecture of intelligent urban transportation. (2) Considering the performance of the inspection robot, the SLAM algorithm is more suitable for road intelligent traffic monitoring. (3) When the number of particles in the improved SLAM algorithm is small, the accuracy and real-time performance of the algorithm can also be guaranteed. The calculation efficiency is improved to 80%, and the modeling accuracy is improved by 23.3%. Traditional traffic monitoring methods typically rely on static sensors and limited data sources. However, the proposed system leverages IoT technology's and inspection robots' real-time data collection capabilities, achieving a more comprehensive, accurate, and flexible acquisition of traffic data. Through this exploration, the overall ideas and objectives of the construction of intelligent highways are clarified, which will lay a solid foundation for the follow-up construction of intelligent highways and provide comprehensive design and practical ideas. The improved SLAM algorithm can more stably complete the positioning and mapping of the tunnel inspection robot in the road environment. In the SLAM algorithm, an Extended Kalman Filter is introduced to ensure the accuracy and real-time of the improved algorithm, which can be applied to the modeling and positioning of unknown environments. Consequently, using the SLAM algorithm in the road detection robot system can stably realize environment awareness and autonomous path planning. [ABSTRACT FROM AUTHOR]

The increasing number of network attacks has led to the development of intrusion detection systems. However, these methods often face limitations such as high traffic flow data dimensions, which can reduce attack detection rates and noise sensitivity, affecting anomaly detection performance. This paper introduces a new model based on recurrent deep learning and instance-level horizontal reduction to detect anomalies and network attacks. The model uses nested sliding windows, which move with a specific step in the data and generate a different number of histogram outputs based on the type of anomaly in the data. Evaluation results on five databases show that the proposed model achieves a high accuracy of 99% in detecting different attacks, demonstrating the success of this new approach combined with deep recurrent neural networks in detecting anomalies. [ABSTRACT FROM AUTHOR]

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management. [ABSTRACT FROM AUTHOR]

Alternative intersection designs can provide cost-effective solutions to overcome the proven inadequacy of conventional approaches. Several studies have assessed the performance of alternative designs against a range of traffic volumes and geometric design aspects, each in isolation, but a model which can factor in multiple variables into the analysis is the identified research gap. The displaced left-turn – DLT intersection design was found to be the most versatile, efficient, and transferable to locations elsewhere in the world. In this paper, a displaced right-turn intersection – a variant of DLT, was modelled for a range of traffic flows and design conditions. Regression models were developed for Practical Reserve Capacity and Delay as dependent variables with traffic flow, proportion of right-turning traffic, signal cycle time and length of displaced turn as explanatory variables. These models can provide relatively quick preliminary estimates of the performance indicators before committing to resource-consuming junction remodelling works. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

In the world of transportation, where computer technology is on the rise, connected vehicles (CAVs) are sharing real-time driving data to improve traffic flow stability. Also, the passing maneuvers of vehicles are primarily influenced by the traffic density in their surroundings. In this study, an extended car-following model incorporating a passing effect is employed to simulate and examine the potential outcomes of cyberattacks on vehicles. The linear stability analysis investigates the neutral stability conditions for different parameters. Also, nonlinear analysis uses the reductive perturbation method to derive equations (Burgers, KdV and mKdV) that describe different wave patterns, reflecting stable, metastable and unstable areas of autonomous vehicle flow. It is found that a cyberattack tends to worsen traffic flow stability during passing. The interaction between passing maneuvers and the attack disrupts the traffic patterns, potentially leading to more chaotic conditions and increased instability in the flow. The theoretical findings indicate that the proposed model effectively captures the behavior of connected vehicles under cyberattacks. Furthermore, the validity of the theoretical findings is confirmed through numerical simulations and found that if the rate of passing is small, it means that there are relatively fewer vehicles on the road and a phase transition occurs from the no-jam region to the kink-jam region. However, as the rate of passing increases, the transition moves from kink jams to a more chaotic traffic pattern for different levels of cyberattacks. The simulation results provide further evidence that the proposed model can help prevent collisions and alleviate traffic congestion in the presence of cybersecurity threats. The combination of the cyberattack and the passing behaviors of drivers creates a compounding effect that disrupts the usual traffic patterns. [ABSTRACT FROM AUTHOR]

Multi-object tracking (MOT) technology is widely applied to traffic flow monitoring, human flow- monitoring, pedestrian tracking, or tactical analysis of players on the courts. It associates the detection boxes with tracklets for each frame in the video. The challenges of MOT include long-term occlusions, missing detections, and complex scenes. Although many trackers have proposed to solve these problems, the tracking results still have room for improvement. In this paper. we propose a solution named MEDIATrack (Matching Embedding Distance & IOU Association Track), a two-stage online multi-object tracking method based on ByteTrack. We replace the Kalman Filter with the NSA Kalman Filter, introduce appearance features for track association, and design a punishment mechanism to alleviate errors in complex scenes. In addition, we remove the nonactivated strategy, and the high-score unmatched detection boxes are directly added to the tracklets. On MOI 17, we achieve 79.3 MOTA, 76.5 IDFI,and state-of-the-art performance. [ABSTRACT FROM AUTHOR]

As a critical characteristic curve of bridge structures, the influence line (IL) is effective in evaluating the bridge damage and bearing capacity. Contrary to conventional methods using static loading to obtain IL, this study proposes a methodology to determine the rotation influence line (RIL) based on the vehicle–bridge interaction (VBI) under random traffic loads. A key feature is the employment of the machine learning algorithm to determine the RIL from the upper and lower bridge response data induced by traffic flow, which is simulated using the cellular automata (CA) approach. Subsequently, by establishing a relationship between the RIL difference and the structural damage coefficient, this study identifies the bridge damage location and degree effectively. The numerical example results validate the accuracy in identifying both single- and multi-damage cases. The parameter analysis indicates that larger vehicle speed induces higher errors in identifying the RIL and the bridge damage. [ABSTRACT FROM AUTHOR]

Traffic flow prediction based on spatial–temporal data plays a vital role in traffic management. However, it still faces serious challenges due to the complex spatial–temporal correlation in nonlinear spatial–temporal data. Some previous methods have limited ability to capture spatial–temporal correlation, and ignore the quadratic complexity problem in the traditional attention mechanism. To this end, we propose a novel spatial–temporal combination and multi-head flow-attention network (STCMFA) to model the spatial–temporal correlation in road networks. Firstly, we design a temporal sequence multi-head flow attention (TS-MFA), in which the unique source competition mechanism and sink allocation mechanism make the model avoid attention degradation without being affected by inductive biases. Secondly, we use GRU instead of the linear layer in traditional attention to map the input sequence, which further enhances the temporal modeling ability of the model. Finally, we combine the GCN with the TS-MFA module to capture the spatial–temporal correlation, and introduce residual mechanism and feature aggregation strategy to further improve the performance of STCMFA. Extensive experiments on four real-world traffic datasets show that our model has excellent performance and is always significantly better than other baselines. [ABSTRACT FROM AUTHOR]

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%. [ABSTRACT FROM AUTHOR]

For efficient and stable large-scale water electrolysis, practical industrial electrodes should meet the following characteristics: high activity, structural stability, and fast bubble removal. Here we report a structured electrode (NiS–Co–NiP) prepared via 3D printing of nickel-based substrate and subsequent electrodeposition for the hydrogen evolution reaction (HER). With unique 3D nanosheets, the NiS–Co–NiP electrode exhibits large electrochemical surface area (ECSA), strong hydrophilicity, high mechanical strength and electrical conductivity, delivering outstanding HER activity. It requires low overpotentials of 134 mV and 169 mV to achieve the current densities of 100 mA cm−2 and 300 mA cm−2, respectively. Moreover, rational design of the NiS–Co–NiP electrode in pyramidal geometry facilitates directional bubble transport and electrolyte flow, resulting in superior stability of 350 h at a current density of 150 mA cm−2. This work demonstrates a new approach for manufacturing binder-free structured electrode of excellent HER performance at high current densities. [Display omitted] • Preparing a structured NiS–Co–NiP electrode by 3D printing and electrodeposition. • The superwetting NiS–Co–NiP electrode shows outstanding HER activity. • 3D pyramidal geometry enables directional bubble traffic and electrolyte flow. • 3D pyramidal geometry favors stable performance at large current hydrogen evolution. [ABSTRACT FROM AUTHOR]

The utilization of autonomous vehicles (AVs) has emerged as a pivotal factor in addressing the rising costs and safety concerns associated with modern travel. As technology advances and traffic challenges intensify, enhancing accessibility stands out as a critical goal for transportation experts. Accessibility, constrained by factors like travel time, underscores the increasing need for AVs to mitigate these limitations. This study aimed to model the influence of AVs on the accessibility index within transportation networks and discuss system optimization based on user equilibrium (UE) and system optimum (SO) outcomes. The research conducted numerical analysis employing the Hearn network as a fundamental system to validate a mixed assignment model and ascertain baseline accessibility. Additionally, the Sioux Falls network, a medium-sized network, was employed for analysis. A hybrid heuristic assignment algorithm was introduced, concurrently assigning different percentages of AV presence alongside the remaining non-AV percentage in three distinct scenarios. These scenarios ranged from 0 to 100% AV presence: the first scenario maintained constant network capacity, the second scenario adjusted network capacity based on AV presence, and the third scenario incorporated capacity adjustments in the assignment stage. In all three scenarios, network accessibility was evaluated using gravity and accessibility index methods derived from the hybrid assignment model output. The findings demonstrated that as the percentage of AVs increased, accessibility improved in both Hearn and Sioux Falls networks across all scenarios. The second and third scenarios exhibited higher accessibility increases compared to the first, attributable to augmented capacity resulting from increased AV presence. In the Sioux Falls network, the first scenario showed enhanced SO and UE due to increased AV presence and enhanced system operator management. Conversely, the second and third scenarios, with increased AVs and subsequent capacity increments, displayed reduced UE and SO results. Despite the decline in UE and SO, traffic flow assignment and overall network accessibility improved. These findings highlight the positive correlation between AV presence, network capacity, and enhanced accessibility. The study underscores the potential benefits of AV integration in optimizing transportation networks and improving overall accessibility, albeit with nuances in capacity adjustments impacting traffic flow dynamics. Further research avenues could explore complex traffic flow scenarios and delve into more specific optimization strategies. [ABSTRACT FROM AUTHOR]

Traffic congestion poses a persistent and escalating problem for major cities in both developed and developing countries, exerting a direct impact on the economic growth and development of these urban areas. Quantifying the extent of traffic congestion is the fundamental initial step in comprehending the severity of traffic congestion in order to devise effective methods for alleviation. The city of Addis Ababa is currently experiencing significant traffic congestion at its main intersections. The primary aim of this research is to assess the current level of traffic congestion at specific intersections. The assessment of traffic congestion was conducted using the travel time method. Data on travel time, traffic volume, and travel speed were gathered at three blocks and two intersections using a combination of quantitative and qualitative data collection methods. The travel rate, delay rate, and total travel delay (in vehicle-minutes) were computed. The total vehicle-minute delay for the selected three segments is estimated to be approximately 12,708 vehicle-minutes (or 212 vehicle-hours). The text reveals the significance of measuring the various components of traffic congestion in order to ensure a sustainable traffic system. It also highlights the importance of maintaining a satisfactory level of service for the future sustainability of City. [ABSTRACT FROM AUTHOR]

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that integrates temporal and spatial features. This paper proposes an attention mechanism-based model to address these issues and model complex spatial–temporal dependencies in road networks. The self-attention mechanism enables the model to achieve long-term dependency modeling and global representation of time series data. Regarding temporal features, we incorporate a time embedding layer and a time transformer to learn temporal dependencies. This capability contributes to a more comprehensive and accurate understanding of spatial–temporal dynamic patterns throughout the entire time series. As for spatial features, we utilize DGCN and spatial transformers to capture both global and local spatial dependencies, respectively. Additionally, we propose two fusion gate mechanisms to effectively accommodate to the complex correlation and heterogeneity of spatial–temporal information, resulting in a more accurate reflection of the actual traffic flow. Our experiments on three real-world datasets illustrate the superior performance of our approach. [ABSTRACT FROM AUTHOR]

To address urban traffic congestion, researchers have made various efforts to mitigate issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts primarily involve microscopic route selection from the vehicle perspective, multi-vehicle route optimization based on traffic flow information and historical data, and coordinated route optimization that models vehicle interaction as a game behavior. However, existing route selection algorithms suffer from limitations such as a lack of heuristic, low dynamicity, lengthy learning cycles, and vulnerability to multi-vehicle route conflicts. To further alleviate traffic congestion, this paper presents a Period-Stage-Round Route Selection Model (PSRRSM), which utilizes a population game between vehicles at each intersection to solve the Nash equilibrium. Additionally, a Period Learning Algorithm for Route Selection (PLA-RS) is proposed, which is based on a multi-agent deep deterministic policy gradient. The algorithm allows the agents to learn from the population game and eventually transition into autonomous learning, adapting to different decision-making roles in different periods. The PSRRSM is experimentally validated using the traffic simulation platform SUMO (Simulation of Urban Mobility) in both artificial and real road networks. The experimental results demonstrate that PSRRSM outperforms several comparative algorithms in terms of network throughput and average travel cost. This is achieved through the coordination of multi vehicle route optimization, facilitated by inter-vehicle population games and communication among road agents during training, enabling the vehicle strategies to reach a Nash equilibrium. [ABSTRACT FROM AUTHOR]

Large enterprise networks typically rely on expensive, high-speed backbone links to connect multiple campuses across diverse regions. As the volume of traffic traversing these backbone links increases, traffic engineering techniques are employed to filter or redirect traffic flows. Nevertheless, simple rerouting strategies can introduce business disruptions such as packet reordering, which significantly impact the user experience. To address this issue, we introduce an enhanced traffic scheduling algorithm named Critical Flow Rerouting with Weight- Reinforcement Learning(CFRW-RL), which builds upon the critical flow rerouting-reinforcement learning (CFR-RL) algorithm. CFRW-RL incorporates the principles of reinforcement learning, accounting for both the weights and classifications of data flows. This approach enables the algorithm to prioritize flows with lower weights for rerouting. The simulation results demonstrate that CFRW-RL significantly minimizes the rerouting of high-priority business flows and reduces business interference compared with the CFR-RL algorithm and that it maintains a similar computational complexity. [ABSTRACT FROM AUTHOR]