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Real-world traffic flow data often contain missing values, which can limit its usability. Although existing deep learning-based imputation methods have shown promising results by reconstructing observed values, they often overlook certain missing patterns in the dataset and perform worse on filling real missing values. This paper addresses this issue and proposes a novel masking method called PatternwiseMissingMix (M-Mix) for masked modeling-based traffic flow data imputation. M-Mix generates masks by mixing existing missing values in the target datasets to preserve the missing pattern information, thereby enhancing the performance of imputing real missing values. Additionally, a dual-objective loss function is proposed for model optimization, which predicts masked values for higher robustness and reconstructs observed values to maintain semantic correctness. Through extensive experiments on real-world datasets, M-Mix consistently demonstrates superior performance compared to other masking methods. [ABSTRACT FROM AUTHOR]

Unlike lane-based traffic where each driver has a distinct leader, the subject driver in disorderly traffic may interact with multiple vehicles in-front. The existence of lateral interactions among the vehicles in-front adds even more complexity to modeling the human-driving process. Utilizing trajectory data extracted from an instrumented vehicle study, this research attempts to propose a gated recurrent unit neural network model to predict responses of vehicles interacting with two leading vehicles simultaneously. The recurrent neural network model can illustrate realistic human-like following behavior of drivers, much better than the classical optimal velocity-based models in terms of trajectory reproducing accuracy. The model can also explain the closing-in, shying-away behavior and local stability properties. Results of the study provide insights into the driving behavioral phenomena of disorderly traffic flows and can contribute to the development of a realistic microsimulation model, smarter autonomous systems, and in-traffic safety evaluation as well. [ABSTRACT FROM AUTHOR]

In order to unravel the physical and mathematical mystery of synchronized-flow mechanism and to reveal the fundamental mechanism and origin of synchronized flow produced by nonlinear stochastic processes, we have produced simple stochastic traffic flow models (the gradual-NaSch, Phoenix and mPhoenix models) with nonlinear safe speeds. In the mNaSch model and our gradual-NaSch model, the i th vehicle's speed is the same as or different from the j th vehicle's speed because they are discrete values. In the mNaSch and gradual-NaSch models, the same discrete values of the i th and j th states make it easy to identify synchronized flow with speed-synchronized phase of the i th and j th states. On the other hand, when we deal with synchronized flow in continuous traffic flow models, we face a problem. Continuous values cause the difficulty in identification of synchronization. In order to definitely clarify whether or not synchronized flow occurs in continuous models, we have established a novel idea of Exchange approach to recognizing synchronized flow as speed-synchronized phase. Our idea is generated from the analogical image that the whole system of synchronized metronomes does not change even if the i th and j th synchronized metronomes are exchanged. The Exchange approach (exchanging the i th vehicle's state for any j th vehicle's state in the whole system of traffic flow), which causes distortion such as a collision if non-synchronized vehicle's states are exchanged and makes it possible to definitely clarify whether or not synchronized flow occurs in continuous models, is applied to our simple stochastic continuous model (the mPhoenix model). On the basis of the Exchange approach, we can recognize that the mPhoenix model surely reproduces synchronized flow. In addition, we have proposed mathematical approach to deriving nonlinear safe speeds which guarantee collision free driving and reproduce synchronized flow. [ABSTRACT FROM AUTHOR]

This study explored whether a policy with maximum velocity limitation prior to each jam point in a traffic flow system can mitigate jam situations in a highway. To quantify this question, a new cellular automata traffic model based on the Revised S-NFS model was established. We perused two specific scenarios: a jam naturally brought by a high traffic density, and one brought by a lane-closed section placed as an explicit bottleneck. The result of multi-agent simulation (MAS) reveals that such maximum velocity limitation policy can certainly mitigate jam situations when each jam is naturally bought up not by a bottleneck, as proved by several statistics, and cannot improve the traffic flux. [ABSTRACT FROM AUTHOR]

Starting from a nonlocal version of a classical kinetic traffic model, we derive a class of second-order macroscopic traffic flow models using appropriate moment closure approaches. Under mild assumptions on the closure, we prove that the resulting macroscopic equations fulfill a set of conditions including hyperbolicity, physically reasonable invariant domains and physically reasonable bounds on the speed with which the waves propagate. Finally, numerical results for various situations are presented, illustrating the analytical findings and comparing kinetic and macroscopic solutions. [ABSTRACT FROM AUTHOR]

Because of the high nonlinearity and complexity, it is still a challenge to forecast traffic flow accurately. Most of the existing methods, which lack the ability to model the dynamic spatio-temporal correlations of traffic flows, rely on a priori knowledge of the graph structure to capture static spatial correlations. However, spatial correlation can be dynamic and the physical structure does not reflect the true relationship between roads. To better capture complex spatio-temporal dependencies and predict traffic conditions on road networks, a new forecasting model based on Transformer and Diffusion Graph Attention Network (T-DGAN) is proposed. This model adopts the Transformer encoder-decoder architecture, where the encoder and decoder contain multiple Spatio-Temporal Convolutional Network Blocks (ST-CB) and Diffusion Graph Attention Blocks (DGA-B), which are utilized to capture the spatio-temporal dependency and the dynamic spatial correlation of traffic flow separately. In addition, an information auxiliary module is added to the decoder to summarize the traffic flow information between the encoder and the decoder. Experiments on different datasets verify that the T-DGAN model has achieved higher forecasting performance than the baseline methods. In PEMS03, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of T-DGAN to 60 min are improved by 4.28% and 3.66%, compared with the state-of-the-art model, respectively. [ABSTRACT FROM AUTHOR]

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called 'two-step cluster analysis' is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. [ABSTRACT FROM AUTHOR]

In the backdrop of an ever-expanding urban transportation road network, the dramatic changes in traffic flow make traffic flow forecasting become a challenge. Which encompass intricate spatial correlations and intricate non-linear temporal relationships. Given a complex time series of traffic flow, accurately predicting traffic flow under such circumstances becomes an arduous task, particularly for long-term sequences. To tackle this conundrum, we put together a traffic prediction framework called the multi-scale attention-based graph convolutional recurrent network (MAGRN). Based on GCN and RNN, this framework skilfully extracts dynamic spatial and temporal features from node attributes. By incorporating multi-scale feature extraction and two attention mechanisms within the attention structure, MAGRN adeptly captures features at varying granularities. This enables MAGRN to diligently capture both the temporal interdependencies and spatial correlations inherent in traffic sequences, thereby minimizing errors. Experimental validation on three real-world traffic datasets unequivocally attests to the superior performance of the MAGRN model. [ABSTRACT FROM AUTHOR]

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

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]

In urban Vehicular Ad-hoc Networks (VANETs), disseminating traffic data efficiently is challenging due to the dynamic and complex nature of the network. Multi-hop-based broadcasting approaches are commonly used to address this issue. However, selecting the optimal relay nodes poses a challenge and directly impacts network performance. Existing relay selection strategies, such as beacon-based methods, have limitations in scaling under different traffic conditions. This paper proposes a multi-attribute relay selection strategy for urban multi-hop VANETs to overcome these challenges. The strategy evaluates the rebroadcast capability of each receiving node based on its real-time status. It utilizes a fuzzy-BCM-based weight estimation strategy to determine the contribution of each attribute to the node's capability. The node with the highest broadcasting capability is given priority to access the channel and broadcast the data packet. The proposed scheme is evaluated through simulation tests in VANET simulation environments, considering various traffic flow and speed variations. Performance comparison with four benchmark methods is conducted. The results show that the proposed scheme improves the overall dissemination efficiency by 51.1% compared to the benchmarked methods. [ABSTRACT FROM AUTHOR]

Real-time traffic prediction uses past data to anticipate traffic volume. The volume of traffic in the region may be estimated using interpolation and extrapolation from library data by the trend structure. It is based on a prediction model with a linear function. From this, the distance-relevant procedures are used to conduct the vehicle traffic flow system. To improve forecast accuracy for managing traffic flow and representing the traffic pattern in the trip route, an adaptive model of optimum learning was presented for missing traffic flow predictions. To categorize and forecast the traffic flow from the database for this model, Adaptive Wildfire Optimization (AWO) with the AI method is suggested. It chooses the best features from the database's overall properties to outperform the conventional classification model in making predictions. Spatial Pattern Super Learning (SPSL), a paradigm for enhancing pattern learning, is presented to increase learning accuracy. By comparing the suggested model's overall outcomes with those of other cutting-edge techniques using statistical factors, the findings may be confirmed. [ABSTRACT FROM AUTHOR]

The short-term traffic flow prediction can help to reduce flight delays and optimize resource allocation. Using chaos dynamics theory to analyze the chaotic characteristics of en-route traffic flow is the basis of short-term en-route traffic flow prediction and ensuring the orderly and smooth state of the en-route. This paper takes the time series of en-route traffic flow extracted from Automatic-Dependent Surveillance Broadcast (ADS-B) measured data as the research object, uses the improved C–C method to reconstruct the phase space, and uses the improved small data volume method to calculate the Lyapunov index to identify the chaos phenomenon of en-route traffic flow. In order to avoid the interference of chaos phenomenon on traffic prediction, the Wavelet Neural Network (WNN) model is established to predict the traffic flow at en-route points. The experimental shows that when the number of iterations is 10,000, the average accuracy of WNN prediction is 0.87173, and the average running time is 6.9335334 s. According to the experimental results, it can be seen that the smaller number of iterations has more advantages in running time, which greatly reduces the overall running time. At the same time, it indicates that appropriately increasing or reducing the number of iterations in this experiment has little effect on the results. [ABSTRACT FROM AUTHOR]

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations. [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]

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]

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]

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]

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]

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]

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]

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]

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]

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation. [ABSTRACT FROM AUTHOR]

Although roads are central to human society, they have many negative environmental impacts and create risk for traveling motorists. Our aim was to evaluate the spatiotemporal evolution of ungulate-vehicle collision (UVC) hotspots in response to major road construction. We examined two different locations and scales in the province of Alberta, Canada: (1) a highway bypass adjacent to a large city with 4.5 km of wildlife mitigation measures (wildlife fencing and two underpasses) and (2) 55 km of rural highway that was converted from a two-lane to a four-lane divided highway. Using government police collision and carcass data (2000-2021), beforeafter and control-impact analyses were used to assess changes in UVC rates. Our approach is novel in that we tested the paired use of a clustering method known as kernel density estimation plus and a spatiotemporal stepwise modification of this method to monitor UVC hotspots. By monitoring UVCs over space and time, we could identify stable vs. ephemeral UVC hotspots, a fence-end effect, and a barrier effect due to traffic volume, and we could explore hotspot stability before and after construction. The wildlife mitigation measures along the highway bypass resulted in 86% fewer UVCs compared to an unmitigated highway. At a larger scale, however, net benefits were affected by road density. The construction of a four-lane divided highway with no wildlife mitigation measures and an increase in the posted speed limit resulted in a slight increase in UVCs and the reemergence of the majority of historical UVC hotspots. Our analysis highlighted the need to incorporate wildlife considerations at a variety of scales throughout the transportation planning and mitigation evaluation process. [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]

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]

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]

Researchers widely use the two-fluid model (TFM) to evaluate the performance of urban networks. However, the TFM is deterministic and does not capture the stochastic relation between speed and density. The present study develops a modified two-fluid model (MTFM). The variance function or the distribution of speed or travel time for a given density is incorporated using a percentile-based indicator, travel time uncertainty (TTU). The percentile-based indicators for the speed distribution are more robust than the variance or other moment-based indicators. The effect of TTU is incorporated using two parameters, $$\alpha ,$$ α , and $$ \beta $$ β. The applicability of the proposed MTFM is demonstrated using empirical data collected at the corridor and network levels. The TFM and MTFM were calibrated by formulating a nonlinear optimization problem. Based on the investigation using the corridor and network-level data, it was concluded that the MTFM showed better performance than the existing model.. [ABSTRACT FROM AUTHOR]

The damage of the frog rail significantly affects the wear of the crossing rail and restricts the passing speed of the train. A geometric 3D modeling of the vehicle passing through the crossing center is particularly concerned with the cumulative wheel-rail contact of the traffic volume. The frog rail wear is simulated to obtain the dynamic change of the impact force of the wheel on the frog rail as the rail wears. By summarizing the existing experimental results of other scholars, it is clear that the important factors, that cause the damage of the frog rail, are vehicle load, friction coefficient, slip roll ratio and shear stress. This paper combines the theoretical analysis of mechanics and 3D simulation to obtain the position change of the wheel-rail contact point with the wear of the frog rail, and finally compares it with the actual measurement results. It can more accurately predict the area where the maximum damage occurs after a certain amount of traffic for a certain fixed model, the change of wheel-rail contact point at frog rail is simulated with the wear of each component. Through theoretical analysis, the main factors determining frog rail damage were determined. Then evaluate the possible damage area of the frog track and control the prediction range to 5-10 cm, which reduces the detection time and cost. The worst state of distraction will be detected in time to facilitate replacement or polishing. Through further research in this area, the service life of the frog rail can be predicted. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts. [ABSTRACT FROM AUTHOR]

In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyons by developing and applying a prediction model based on the mathematical modeling of physical processes. The prediction model considered factors such as the influence of traffic wind, natural wind, traffic flow, and other relevant variables influencing particle mass concentrations in street canyons. Field measurements were conducted in Dalian, China, to verify the feasibility of the model. Particle mass concentrations, traffic flow, temperature, relative humidity, and wind speed were measured on Shichang Street (a two-lane one-way road), Tangshan Street (a four-lane two-way road), and Shengli Road (a six-lane two-way road). The results indicated that the majority of traffic peaks occurred around 19:00 on all road types. The PM1.0 mass concentration was well diluted on the four-lane two-way road, with the least dilution observed on the two-lane one-way road. A strong correlation between the particle mass concentrations and traffic flow was discovered. Furthermore, a prediction model was established, accurately predicting the particle mass concentrations when the prediction step was from 5 to 15 s. The coefficient of determination (R2) between the predicted and measured values on the two-lane one-way road, four-lane two-way road, and six-lane two-way road was 0.9319, 0.6582, and 0.9238, respectively. Additionally, the prediction model allowed for a detailed analysis of traffic flow limitations, corresponding to the recommended World Health Organization (WHO) PM2.5 values. Overall, the findings of this study offer valuable insights for forecasting particle exposure levels in street canyons. [ABSTRACT FROM AUTHOR]

We extend a multimodal transport model to simulate an increase of the market share of electric vehicles. The model, which is described in detail in Kilani et al. (Sustainability 14(3):1535, 2022), covers the north of France and includes both urban and intercity trips. It is a multi-agents simulation based on the MATsim framework and calibrated on observed traffic flows. We find that the emissions of pollutant gases decrease in comparable proportion to the market share of the electric vehicles. When only users with shorter trips switch to electric vehicles, the impact is limited and demand for charging stations is small since most users will charge by night at home. When the government is able to target users with longer trips, the impact can be higher by more than a factor of two. But, in this case, our model shows that it is important to increase the number of charging stations with an optimized deployment for their accessibility. [ABSTRACT FROM AUTHOR]

This paper implements a two-staged ensemble learning model for traffic forecasting, focusing on the interpretability of predictions. The stacking model leverages the advantages of its diverse component learning models. Experiments on high-dimensional and sparse data validate the stacking model's superior predictive accuracy compared to baseline models, including LightGBM and XGBoost. In addition to validating the stacking model's outstanding predictive performance, this paper emphasizes the interpretability of its predictions by proposing an innovative explanation model based on feature contributions. This explanation model addresses the high dimension and sparsity in data prevalent in transportation engineering with its integration of resampling and consensus clustering, offering a scalable, stable, and computationally efficient solution ideal for real-time and large-scale applications. The paper presents theoretical justification, experimental results, and empirical validation of the interpretation model. Extensive experiments demonstrate the model's enhanced stability compared to traditional shapley additive explanations (SHAP) implementations such as kernel SHAP. Investigating trade-offs between stability and computational efficiency of resampling provides insights for optimal configuration choices. This paper contributes to traffic flow prediction with broad applicability in real-time and large-scale traffic management scenarios, underscoring the vital role of ensemble learning and interpretable machine learning in contemporary data-driven decision making processes. [ABSTRACT FROM AUTHOR]

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered. [ABSTRACT FROM AUTHOR]

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model–NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source–receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models' application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors. [ABSTRACT FROM AUTHOR]

The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format. Existing protocols possess inherent challenges to achieving this prioritized low-latency communication, ranging from rigidly scheduled time division transmissions, scalability/jitter of carrier-sense multiple access (CSMA) protocols, and encryption-induced latency. This paper presents a hardware-validated low-latency technique built upon receiver-assigned code division multiple access (RA-CDMA) techniques to implement a secure wireless TSN-like extension suitable for the IIoT. Results from our hardware prototype, constructed on the IntelFPGA Arria 10 platform, show that (sub-)millisecond single-hop latencies can be achieved for each of the available message types, ranging from 12 bits up to 224 bits of payload. By achieving one-way transmission of under 1 ms, a reliable wireless TSN extension with comparable timelines to 802.1Q and/or 5G is achievable and proven in concept through our hardware prototype. [ABSTRACT FROM AUTHOR]

• Drivers on slip roads want vehicles on the target lane to decelerate. • Drivers in target lanes rate the behaviour of vehicles on slip roads ambiguously. • AVs are rated identically or even more positively than MVs with identical behaviour. • No different safety distance is kept from automated vehicles. • Results show that cross-border traffic between Germany and UK with AVs is feasible. • Intercultural aspects must still be considered in the development of AVs. To ensure safe and uninterrupted traffic flow, (semi-)automated vehicles must be capable of providing comprehensible and agreeable implicit communication cues to human drivers. This driving simulator study investigated the assessment of implicit communication at a motorway slip road through longitudinal driving dynamics (acceleration, deceleration, and maintaining speed). The second aim of the study was to determine whether expectations of automated vehicles are different from those of human drivers. And thirdly, we investigated whether these findings are country-specific or can be (partially) generalised to other countries. The perception of three means of communication in connection with the presence of a labelling as an automated vehicle (eHMI) was examined in two samples in Germany and England. 27 participants drove from a slip road onto the motorway and interacted with another vehicle. After a stretch on the motorway, they passed a second slip road on which there was a vehicle merging onto the participants lane. This was repeated six times to test all variables. After each situation, the perceived cooperativity and criticality was recorded, as well as the time headway (THW) to the other vehicle. This paper presents the findings from the UK sample and compares them with the German results, which were previously published. Results show, that when the cooperating vehicles are on the slip road, participants from both countries prefer this vehicle to decelerate. However, when participants themselves are on the slip road, expectations for vehicles on the target lane are ambiguous in the UK sample. Except for one aspect (perceived cooperativity of decelerating vehicles on the slip road), the perception of automated vehicles is similar to those of manual drivers. Also, UK participants do not maintain a different safety distance from these vehicles, while this is the case in the German sample. This paper contributes valuable insights into the cross-cultural evaluation of driving dynamics, shedding light on implications for the development and acceptance of automated vehicles. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) continue to evolve and approach widespread adoption in the near future, the touted benefits of improved fuel efficiency at an individual level come under scrutiny when considering the overall impact on fuel consumption. This research delves into the paradoxical relationship between the promising technology of AVs, their impact on traffic capacities, travel demand, and the subsequent influence on aggregate fuel consumption. While AVs have demonstrated enhanced fuel efficiency when considered as a singular mode of transportation, our study reveals a contrasting trend when scaled to a broader societal context. Through comprehensive analysis of the literature, we discovered that, at lower limits of energy savings achievable by a single AV, the overall fuel consumption increases by a staggering 42% compared to conventional human-driven vehicles. This counterintuitive outcome is a result of the aggregate effect of increased AV usage, leading to higher traffic volumes and travel demands. Conversely, at higher thresholds of energy savings by individual AVs, the percentage of fuel consumption increment diminishes, but remains notable. Even with advanced energy-saving features, the overall fuel quantity still experiences a substantial 30% increase compared to conventional vehicles when scaled up to widespread AV use. Our findings emphasize the importance of considering the holistic impact of AVs on transportation systems and energy consumption. As society transitions towards AV-dominated traffic, policymakers and stakeholders must address the challenges associated with increased travel demand, potential traffic congestion, and the resultant implications on fuel consumption. [ABSTRACT FROM AUTHOR]

Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas. [ABSTRACT FROM AUTHOR]

Wildlife mortality due to collisions with vehicles (roadkill) is one of the predominant negative effects exerted by roads on many wildlife species. Reducing roadkill is therefore a major component of wildlife conservation. Roadkill is affected by various factors, including road attributes and traffic volume. It is theorized that the effect of traffic volume on roadkill probability should be unimodal. However, empirical evidence for this theory is lacking. Using a large‐scale roadkill database of 18 wildlife species in Israel, encompassing 2846 km of roads over 10 years, we explored the effects of traffic volume and road attributes (e.g., road lighting, verge vegetation) on roadkill probability with a multivariate generalized linear mixed model. A unimodal effect of traffic volume was identified for the striped hyena (Hyaena hyaena), whereas 5 species demonstrated a novel quadratic U‐shaped effect (e.g., golden jackal [Canis aureus]). Four species showed a negative linear effect (e.g., wild boar [Sus scrofa]). We also identified varying effects of road attributes on roadkill. For instance, road lighting and roadside trees decreased roadkill for several species, whereas bus stops and concrete guardrails led to increased roadkill. The theorized unimodal effect of traffic volume may only apply to large, agile species, and the U‐shaped effect could be related to intraspecies variability in traffic avoidance behavior. In general, we found that both high‐traffic and low‐traffic roads can pose a high mortality risk for wildlife. It is therefore important to monitor roadkill on low‐traffic roads and adapt road attributes to mitigate roadkill. Road design for effective roadkill mitigation includes reducing the use of concrete guardrails and median barriers where possible and avoiding dense bushes in verge landscaping. These measures are complemented by employing wildlife detection systems, driver warnings, and seasonal speed reduction measures on low‐traffic roads identified as roadkill hotspots. [ABSTRACT FROM AUTHOR]

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