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This paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short‐term traffic prediction with limited data. To address the challenges posed by limited data and the complex and diverse traffic patterns observed in traffic networks, we propose a profile model based on few‐shot learning to extract each detector's unique profiles. These profiles are then used to cluster detectors with similar patterns into distinct clusters, facilitating effective learning with limited data. A Convolutional Neural Network ‐ Long Short‐Term Memory (CNN‐LSTM)‐based predictive model is proposed to learn and predict traffic volumes for each detector within a cluster. The proposed method demonstrates resilience to detector failures and has been validated using the Performance Measurement System dataset. In scenarios with less than 2 months of training data and 10% failed detectors, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) for station‐level traffic volume prediction increase from 12.7 vehs/5 min, 20.9 vehs/5 min, and 10.5% to 13.9 vehs/5 min, 24.2 vehs/5 min, and 11.7%, respectively. For lane‐level traffic volume prediction, the average MAE, RMSE, and MAPE increase from 4.7 vehs/5 min, 7.7 vehs/5 min, and 15% to 5.6 vehs/5 min, 9.6 vehs/5 min, and 16.8%. Furthermore, the proposed method extends its applicability to traffic speed and occupancy prediction tasks. TL is integrated to improve speed/occupancy prediction accuracy by leveraging knowledge obtained from traffic volume, considering the correlation between traffic flow, speed, and occupancy. When less than 1 month of traffic speed/occupancy data is available for learning, the proposed method achieves an MAE, RMSE, and MAPE of 0.7 km/h, 1.3 km/h, and 1.3% for station‐level traffic speed prediction and 0.5%, 1.1%, and 11% for station‐level traffic occupancy. [ABSTRACT FROM AUTHOR]

The Pavement Management System (PMS) includes constant assessments of the structural and, mainly, functional conditions of the pavement. An automated collection, performed at traffic speeds, in which human error can be minimized, presents perspectives for PMS optimization. To contribute to the functional pavement assessment process, this study investigated the applicability of the functional inventory process for rigid pavements using equipment with laser crack measurement system (LCMS) technology. Test sections were defined following the traditional method of assessing the surface of the pavement proposed by the Brazilian National Department of Transport Infrastructure (DNIT). Additionally, pavement surface macrotexture was evaluated to serve as a basis for comparison with the results obtained with the use of LCMS technology. The analysis of the repeatability and reproducibility of each technique demonstrated that the use of the LCMS equipment and the procedure of analysis is feasible, and could provide equations to correlate the results obtained via the mechanized and traditional procedures. [ABSTRACT FROM AUTHOR]

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections. [ABSTRACT FROM AUTHOR]

Spatial causality in urban traffic networks explores how events or conditions in one location affect those in another. Unveiling congestion spatial causality is crucial for identifying congestion‐inducing bottlenecks in traffic networks and offering valuable insights for traffic network management and control. This study introduces the traffic‐convergent‐cross‐mapping (T‐CCM) method, a state‐space‐reconstruction approach from the dynamic system perspective, to identify causality among roads within urban traffic networks using time series data. Simultaneously, it effectively addresses the intricate challenges of uncertainty and interdependency among sensors caused by traffic flow dynamics. Empirical findings from real‐world (PeMS‐Bay area) traffic speed data validate the effectiveness of the T‐CCM method in detecting causality. This study reveals bidirectional causal effects between downstream and upstream roads in short‐term congestion generation and dissipation periods, which can pinpoint congestion origins and inform quick traffic management response. Furthermore, it elucidates the long‐term causality impacts between distant roads, particularly with regard to traveler choices and road land use attributes, guiding infrastructure investment and public transit improvements. [ABSTRACT FROM AUTHOR]

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model's adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems. [ABSTRACT FROM AUTHOR]

AbstractThere is a lack of research that investigates the risk factors of cycling in low- and middle-income countries. We present descriptive epidemiology of crashes that involved at least one fatal cyclist, over a three-year period (2016–2018) in Delhi, India. Next, we used an unmatched case-control approach to identify road design risk factors of cycle fatalities. Cases were road segments with at least one cycle fatality, and controls were those with none. We developed logistic regression models with cases and controls as binary outcomes to estimate the odds ratio of site characteristics. There were 167 crashes involving at least one cyclist fatality over the study period. Fatal cyclists were almost all males. They were less likely to be children or young adults and less likely to be residents of high-income localities, compared to the general population. One in ten crashes included more than one occupant on a cycle and 5% of fatal victims were pillion riders. Seventy percent crashes occurred at midblock, and majority were backend collisions. Regression shows that road width, traffic speed, and volume of heavy vehicles have strong positive effects on the fatality risk of cyclists. These results can inform strategies to design interventions for safety of cyclists. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the influence of flexural deformation of the body of a freight wagon on the indicators of the interaction of rail fleet with rails. The considered indicators depend both on the design of the rail fleet, its condition and speed, and on the design and condition of the railroad track. A theoretical study was carried out using a model of spatial vibrations of a freight wagon as part of a homogeneous train. When carrying out calculations, the wagon body was considered as an absolutely rigid body and as a discrete multi-mass system with elastic connections between the masses. When choosing a design scheme, it was assumed that the wagon body is a deformable body and, when bending, has finite rigidity in the vertical and horizontal planes. As a result of the research, the dependences of the dynamic indicators of a freight wagon on the flexural deformation of the body and the speed of movement were obtained. Based on theoretical calculations, the influence of the deformability of the body on the interaction of rail fleet with the railroad track on a tangent level track and curved section with irregularities was assessed. [ABSTRACT FROM AUTHOR]

We consider situations in which the clustering of some multivariate data is desired, which establishes an ordering of the clusters with respect to an underlying latent variable. As our motivating example for a situation where such a technique is desirable, we consider scatterplots of traffic flow and speed, where a pattern of consecutive clusters can be thought to be linked by a latent variable, which is interpretable as traffic density. We focus on latent structures of linear or quadratic shapes, and present an estimation methodology based on expectation–maximization, which estimates both the latent subspace and the clusters along it. The directed clustering approach is summarized in two algorithms and applied to the traffic example outlined. Connections to related methodology, including principal curves, are briefly drawn. [ABSTRACT FROM AUTHOR]

Traffic forecasting using deep learning represents a crucial aspect of intelligent transportation systems, carrying substantial implications for congestion reduction and efficient route planning. Despite its significance, accurately predicting traffic states remains a challenge. Existing methodologies focus on capturing the temporal trends of traffic states and the spatial dependencies between roads to enhance prediction accuracy. However, two noteworthy limitations persist in these approaches: (1) Many models neglect the interaction between spatiotemporal features across varying time spans, hindering their ability to utilize traffic state information effectively for predicting future conditions. (2) Genuine correlations between roads are time-varying, making it inadequate to rely on static graphs or static pre-trained node embeddings to model dynamic correlations between roads. To address these challenges, we propose the Multiple Time-Scale Graph Attention Network (MTS-GATN), which comprises two key modules: the Multiple Time-Scale Spatiotemporal Features Extraction Module and the Feature Augmentation Module. The first module involves stacking multiple spatiotemporal extraction layers to discern traffic state information at different time scales. In the second module, we employ dynamic spatial semantic embedding for feature augmentation, providing nodes with dynamic representations over time. Subsequently, we leverage a multi-head spatiotemporal attention mechanism to comprehensively consider location information and real-time semantic data, facilitating the interaction of traffic state information across multiple time scales. Experimental results on two distinct traffic datasets validate the superior performance of MTS-GATN in medium-term and long-term forecasting scenarios. [ABSTRACT FROM AUTHOR]

Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network's ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm's loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have gained significant attention in diverse domains, ranging from urban planning to pandemic management. Ensuring both accuracy and robustness in GNNs remains a challenge due to insufficient quality data that contains sufficient features. With sufficient training data where all spatiotemporal patterns are well-represented, existing GNN models can make reasonably accurate predictions. However, existing methods fail when the training data are drawn from different circumstances (e.g., traffic patterns on regular days) than test data (e.g., traffic patterns after a natural disaster). Such challenges are usually classified under domain generalization. In this work, we show that one way to address this challenge in the context of spatiotemporal prediction is by incorporating domain differential equations into graph convolutional networks (GCNs). We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models. To support our theory, we propose two domain-differential-equationinformed networks: Reaction-Diffusion Graph Convolutional Network (RDGCN), which incorporates differential equations for traffic speed evolution, and the Susceptible-Infectious-Recovered Graph Convolutional Network (SIRGCN), which incorporates a disease propagation model. Both RDGCN and SIRGCN are based on reliable and interpretable domain differential equations that allow the models to generalize to unseen patterns. We experimentally show that RDGCN and SIRGCN are more robust with mismatched testing data than state-of-the-art deep learning methods. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model. The MFSTBiSGAT model leverages graph attention networks to extract dynamic spatial features from complex road networks, and utilizes bidirectional long short-term memory networks to capture temporal correlations from both past and future time perspectives. Additionally, spatial and temporal information enhancement layers are employed to comprehensively capture traffic flow patterns. The model aims to directly extract original temporal features from traffic flow data, and utilizes the Spearman function to extract hidden spatial matrices of road networks for deeper insights into spatiotemporal characteristics. Historical traffic speed and lane occupancy data are integrated into the prediction model to reduce forecasting errors and enhance robustness. Experimental results on two real-world traffic datasets demonstrate that MFSTBiSGAT successfully extracts and captures spatiotemporal correlations in traffic networks, significantly improving prediction accuracy. [ABSTRACT FROM AUTHOR]

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver's intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

Internet traffic (IT) is a measure of data transfer across devices. In this paper, an analogy is made between data transfer and soliton propagation in optical fibers. This is achieved by employing the concatenation model (CM) that describes soliton propagation in optical fibers, which is presented recently in the literature. The CM contains nonlinear space-time dispersion effect, that may lead to bottleneck soliton shape (BNSS). Thus, in view of this model, BNSS effect of soliton propagation may occur, which is analogous to a possible BN in IT. So, the prediction of the characteristics of internet traffic can be depicted via the CM, which is studied here with Caputo-q time derivative. Also, a variety of exact solutions of the CM are derived. These solutions are represented graphically and they show multiple shapes of concatenated solitons. Among them, bottleneck, M-shaped, hybrid M shaped, chirped solitons and vector of dromian patterns. On the other side, the speed of IT and chips heating are estimated. It is found that the speed of IT is constant with time and the effects of distributed time delay (recent memory (RM)) is to slow the traffic speed. This is done via varying the fractional order. Also, it is observed, when accounting for RM, that the chip heating is too small. We think that the results for the speed of IT and chip heat are, qualitatively, realistic. The stability of a steady state solution is analyzed and the controlled parameters for stability is determined. [ABSTRACT FROM AUTHOR]

Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance ( D i s i n t e r ). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and D i s i n t e r . First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the D i s i n t e r level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in large cities worldwide. This project uses high-frequency data from the Los Angeles metropolitan area combined with an instrument that varies spatially and temporally to estimate the causal impact of an additional vehicle mile traveled on travel times. Specifically, the research team exploits the network structure of the Los Angeles highway system and uses crashes on close alternative routes as exogenous shocks to traffic demand. To do so, the team relies on Google Maps to determine the ideal route and alternatives for over 19,000 real-world commutes. The researchers estimate that at peak times an additional trip reduces speed by, on average, 0.22%. They find the optimal toll at peak times is 33 cents per mile, with the toll being lower, even zero, off-peak. The researchers show how this toll varies over space and time, as well as report on its distributional effects. This toll would more than double highway speeds during peak times and only requires reducing vehicle miles traveled (VMT) at the peak by 10%. The resulting social welfare gains are over two billion dollars per year.View the NCST Project Webpage

Traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), offering applications across diverse domains. However, current deep learning models face significant challenges. Real-world traffic conditions, especially during peak hours, exhibit complex spatio-temporal dynamics and intricate nonlinear relationships. Existing studies often overlook variations in traffic flow across different time periods, locations, and scenarios, resulting in prediction models lacking robustness and accuracy across diverse contexts. Furthermore, simplistic models struggle to accurately forecast traffic flow during peak periods, as they typically focus on isolated features such as traffic speed, flow rate, or occupancy rate, neglecting crucial interdependencies with other relevant factors. This paper introduces a novel approach, the peak hour embedding-based multi-feature spatio-temporal coupled traffic flow prediction model (PE-MFSTC), to address these challenges. The PE-MFSTC model incorporates peak time embedding within a multirelational synchronization graph attention network structure. The peak time-based embedding involves mapping daily, weekly, and morning/evening peak periods into low-dimensional time representations, facilitating the extraction of nonlinear spatio-temporal features. The network framework employs a multirelational synchronized graph attention network, integrating multiple traffic features and spatio-temporal sequences for learning. Additionally, a spatio-temporal dynamic fusion module (STDFM) is introduced to model correlations and dynamically adjust node weights, enhancing the model's sensitivity. Experimental evaluations on four real-world public datasets consistently demonstrate the superior performance of the PE-MFSTC model over seven state-of-the-art deep learning models. These results highlight the efficacy of the proposed model in addressing the complexities of traffic flow prediction across various scenarios. [ABSTRACT FROM AUTHOR]

The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN. [ABSTRACT FROM AUTHOR]

Logistics and transportation industry is not only a major energy consumer, but also a major carbon emitter. Developing green logistics is the only way for the sustainable development of the logistics industry. One of the main factors of environmental pollution is caused by carbon emissions in the process of vehicle transportation, and carbon emissions of vehicle transportation are closely related to routing, road conditions, vehicle speed, and speed fluctuations. The low-carbon vehicle routing problem with high granularity time-dependent speeds, speed fluctuations, road conditions, and time windows is proposed and formally described. In order to finely evaluate the effects of vehicle speed and speed fluctuations on carbon emissions, a graph convolutional network (GCN) is used to predict the high granularity time-dependent traffic speeds. To solve this complicated low-carbon vehicle routing problem, a hybrid genetic algorithm with adaptive variable neighborhood search is proposed to obtain vehicle routing with low carbon emissions. Finally, this method is validated using a case study with the logistics and traffic data in Jingzhou, China, and also the results show the effectiveness of this proposed method. [ABSTRACT FROM AUTHOR]

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks. [ABSTRACT FROM AUTHOR]

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising. [ABSTRACT FROM AUTHOR]

Traffic replay can effectively improve the fidelity of network emulation and support emerging network verification and network security evaluation. A distributed traffic replay system based on cloud platform is designed for large-scale, high-speed and highly concurrent network traffic emulation, named DTRS. Aiming at the diverse network traffic, a scalable multi-stream replay method is proposed to realize the flexible reproduction of multiple protocol traffic. A traffic replay strategy based on DPDK is proposed to achieve accurate control of the replay process and improve the timing fidelity of replay. To solve the problem of insufficient traffic replay speed, a vector packet processing strategy based on VPP is proposed to improve the throughput. The experimental results show that DTRS can realize the scalable loading of diverse network traffic, and support the flexible, realistic and controllable reproduction of massive heterogeneous network scenarios. In high- speed traffic emulation, DTRS can ensure the timing fidelity of replay. Compared with traditional methods, the traffic replay throughput of DTRS has increased by 3.71 times. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis. [ABSTRACT FROM AUTHOR]

The Time Space Diagram (TSD) can abstractly represent multiple data sources and the macroscopic state of road traffic. However, the TSDs may be incomplete due to missing data, which seriously affects traffic management. Therefore, this paper proposed a Multi-Directional Recurrent Graph Convolutional Network (MDRGCN) for reconstructing TSDs and estimating missing traffic speeds given sparse data. We designed multi-directional RNN layers for scanning the TSDs from horizontal and vertical directions, which can fully exploit the contextual dependencies of the traffic information. In addition, our model includes graph convolution layers for mining potential spatial correlations in the TSDs. The performance of the model reconstructed from TSDs is validated on the NGSIM dataset. We also provided a comparison with other advanced methods, and the experimental results show that our method can perform well at both low and high missing rates, significantly outperforming the baseline methods. [ABSTRACT FROM AUTHOR]

Influenced by external factors, the speed of vehicles in the traffic network is changing all the time, which makes the traditional static shortest route unable to meet the real logistics distribution needs. Considering that the existing research on time-dependent shortest path problems (TDSPP) do not include the topological information of the traffic network, it is unable to reflect the spatial and temporal dynamic characteristics of the traffic network during the vehicle travelling process and is unable to update to the changes of the vehicle speed in real time, and poor scalability. Therefore, we used the structural RNN (SRNN) model containing topological information of the road network is used to predict time-varying speeds in the traffic road network. We proposed an SRNN-RSA framework for solving the TDSPP problem, which achieves a synergistic evolution between the real-time vehicle speed change process and the RSA solving process, and the scalability of the proposed SRNN-RSA is demonstrated and validated using different real data. Compared with other algorithms, the results show that SRNN-RSA has the lowest error with the actual situation, which can balance the solution accuracy and calculation speed and is more consistent with the real traffic road network, with better stability and expandability. [ABSTRACT FROM AUTHOR]

Vacant cruising is an inevitable part of taxi services caused by spontaneous demand, and the efficiency of cruising strategies has purported impact on the profit of individual drivers. Extensive studies have been conducted to analyze taxi cruising patterns and propose effective cruising strategies. However, existing studies mainly focused on the collective behavior of certain driver groups and failed to capture cruising behavior patterns at the individual driver or trip level. Also, prior studies considered different types of factors affecting taxi cruising, but we still lack an integrated model to compare their relative importance. In this study, we analyze trip-level cruising time and the associated external and internal factors using a taxi trajectory dataset in Shanghai, China. A trajectory annotation technique is introduced to segment taxi trajectories into different phases. Various external (supply and demand, traffic condition and built environment) and internal (cruising strategies and historical driver performance) factors are derived from taxi trajectories and other data sources. A spatiotemporal embedding method is devised to capture unobserved effects over time and space. The impacts of external and internal factors on taxi cruising time are examined using regression and XGBoost—a machine learning model. The results show external and internal factors are both important in determining taxi cruising time. Cruising strategies contribute 49.0% in taxi cruising time, which implies effective cruising strategies can greatly reduce vacant cruising time. Additionally, nonlinear associations of some variables (e.g., supply–demand patterns, traffic speed) with taxi cruising time are discussed. [ABSTRACT FROM AUTHOR]

In this paper is present the procedure for introducing the coordination of light signals at five intersections on the Boulevard "St. Kliment Ohridski" in Skopje. The primary purpose of a traffic signal is to assign the right-of-way to intersecting traffic streams for the purpose of ensuring that all streams are served safely and without excessive delay. A properly designed and timed signalized intersection will minimize fuel consumption, delay, and stops without having an adverse effect on safety. Travelers will realize one or more of the following benefits at intersections where the traffic signal is needed, properly designed, and well timed: orderly movement of traffic, increase in the traffic-carrying capacity of the intersection, reduction in the frequency and severity of certain types of crashes, progressed traffic when traveling in a coordinated signal system, and interruption of heavy traffic flow to provide safe opportunities for minor movements. Coordinated signal timing synchronizes traffic movements and manages the progression speed of specific modes where uninterrupted flow is desired along a corridor. While traditionally applied to increase vehicular traffic flow and reduce peak-hour delay, coordinated signal timing can also be optimized for slower speeds, creating an uninterrupted flow for bicyclists or low vehicle progression speeds for a pedestrian-friendly downtown. Signals may also be timed to coordinate transit headways along routes where regular transit service is consistent and has low variability. Coordination of traffic signals can reduce the number of stops along a corridor and provide for a continuous flow of traffic at the target speed. Progression speeds should be set at or below the target speed, rather than existing 85th percentile speeds. [ABSTRACT FROM AUTHOR]

Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction. [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]

Due to the complex routes and the dynamic changing factors in transportation, precise traffic speed prediction is very difficult. Traditional prediction methods only focus on a single monitoring site, without establishing a relationship between different sites, so the precision is poor. The deep learning method can model traffic networks well, but suffers from information loss and the disadvantage of single input data. A multisource spatio-temporal hybrid dilated graph convolutional network (GCN) for forecasting traffic speed is proposed in this paper. A GCN based on hybrid dilated convolution can extract the influence of adjacent information and capture dynamic spatial and non-linear temporal correlations. Considering multisource data will increase the forecasting precision and improve the generalisation ability. Using a real-world data set, the performance of the proposed model was validated against other baselines (a fully connected neural network, convolutional neural network and spatio-temporal GCN). The proposed model was found to be superior to other models as it considers proximity information, which is often overlooked, and multifactorial influence. [ABSTRACT FROM AUTHOR]

Given the role of road tunnels in a road network, it is relevant to quantitatively assess their resilience due to disruptive events. In this respect, the travel speed of traffic flow, as a metric of resilience, in the event of traffic accidents in road tunnels has been scarcely used. This represents a gap of knowledge that this paper intends to fill. For the purpose, the research method applied involves the development of a traffic macro-simulation model, and the analysis of results using the average travel speed and its spatial profile as a resilience metric. Particularly, we have evaluated the resilience of a twin-tube motorway tunnel when a traffic accident occurs in a tube. The findings showed how the best functionality level of the system, expressed in terms of average vehicles' speed, with its related greatest resilience index, is associated with the partial closure of the disrupted tube rather than the complete one. Further benefits might be obtained by activating Variable Message Signs (VMSs) that alert only the Heavy Goods Vehicles (HGVs) to exit the motorway before entering the tunnel and to use an alternative itinerary identified in the nearby transportation network. In this respect, we found that by means of the activation of VMSs as a traffic control strategy, the resilience index increased by about 7–17% when the traffic accident caused the partial closure of the disrupted tube for 1–3 h. Improvements might also be achieved by rapidly opening the two traffic by-passes at the tunnel portals that allow for the use of the adjacent undisrupted tube for two-way traffic. The unavailability of an alternative itinerary in the nearby transportation network along which to re-route the HGVs is also examined, finding a reduction in the functionality conditions of the system. The results obtained might serve as a support tool in the choice of functional recovery strategies in the case of the temporary partial or complete block of a tunnel tube due to a traffic accident. [ABSTRACT FROM AUTHOR]

The presented research is focused on the design of a new driving cycle of the Bus Rapid Transit (BRT) for the Kyiv city. This cycle consists of a complex of two types of sections. Each of them includes various phases of the traffic. A mathematical model was specified, which serves for defining the efficiency properties indices and for regimes of bus transport. The BRT are used within the bus lines. It was found, that two articles articulated buses are the most suitable choice, the MAZ-215 among them, for exploitation. At the same time, they provide the smallest fuel consumption together with the shorter operation time on the bus line as well as higher average speed at full and half loading. The optimal driving speeds of the steady traffic are the values of 50 km/h for section up to 1 km and 60 km/h or 70 km/h for section over 1 km. It was evaluated, that depending on the steady motion speed and the load level, the driving time of the BRT on the line varies from 20.1 min to 23.4 min, which is much lower than the driving time of a trolleybus on the same line according to the traffic schedule. [ABSTRACT FROM AUTHOR]

Abstract Influenced by external factors, the speed of vehicles in the traffic network is changing all the time, which makes the traditional static shortest route unable to meet the real logistics distribution needs. Considering that the existing research on time-dependent shortest path problems (TDSPP) do not include the topological information of the traffic network, it is unable to reflect the spatial and temporal dynamic characteristics of the traffic network during the vehicle travelling process and is unable to update to the changes of the vehicle speed in real time, and poor scalability. Therefore, we used the structural RNN (SRNN) model containing topological information of the road network is used to predict time-varying speeds in the traffic road network. We proposed an SRNN-RSA framework for solving the TDSPP problem, which achieves a synergistic evolution between the real-time vehicle speed change process and the RSA solving process, and the scalability of the proposed SRNN-RSA is demonstrated and validated using different real data. Compared with other algorithms, the results show that SRNN-RSA has the lowest error with the actual situation, which can balance the solution accuracy and calculation speed and is more consistent with the real traffic road network, with better stability and expandability.

ABSTRACTThe focus of this study is to identify potential road links suitable for implementing a variable speed limit (VSL) sign by analyzing real-world traffic speed data collected over one year in Charlotte, North Carolina, United States. Exploratory and bivariate analyses were conducted to examine variations in traffic speed patterns during weekdays and weekends across eight specific timespans. The results revealed that road links with lower posted speed limits consistently experienced traffic speeds exceeding the posted speed limits. The mean traffic speeds are generally close to the posted speed limits for road links with higher posted speed limits while the 85th percentile speeds exceeded the posted speed limits, indicating a potential need for VSL sign implementation. The road links with 40 mph or 50 mph posted speed limits have a unique pattern compared to road links of other posted speed limit clusters. The mean traffic speed on these road links decreased as the standard deviation increased. The findings contribute to an improved understanding of traffic speed patterns and provide valuable insights for implementing a VSL sign.

Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR]

Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an undirected static adjacency matrix to map the correlation of nodes, which ignores the dynamic symmetry change of correlation over time and faces the challenge of oversmoothing during training iterations, making it difficult to learn the spatial structure and temporal trend of the traffic network. To overcome the above challenges, we propose a novel multi-head self-attention gated spatiotemporal graph convolutional network (MSGSGCN) for traffic speed prediction. The MSGSGCN model mainly consists of the Node Correlation Estimator (NCE) module, the Time Residual Learner (TRL) module, and the Gated Graph Convolutional Fusion (GGCF) module. Specifically, the NCE module aims to capture the dynamic spatiotemporal correlations between nodes. The TRL module utilizes a residual structure to learn the long-term temporal features of traffic data. The GGCF module relies on adaptive diffusion graph convolution and gated recurrent units to learn the key spatial features of traffic data. Experimental analysis on a pair of real-world datasets indicates that the proposed MSGSGCN model enhances prediction accuracy by more than 4% when contrasted with state-of-the-art models. [ABSTRACT FROM AUTHOR]

Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curbside utilization and disturb the mainline traffic flow, evidently leading to significant negative societal externalities. However, there is a lack of an analytical framework that rigorously quantifies and mitigates the congestion effect of PUDOs in the system view, particularly with little data support and involvement of confounding effects. To bridge this research gap, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general regional networks. A causal graph is set to represent the spatiotemporal relationship between PUDOs and traffic speed, and a double and separated machine learning (DSML) method is proposed to quantify how PUDOs affect traffic congestion. Additionally, a rerouting formulation is developed and solved to encourage passenger walking and traffic flow rerouting to achieve system optimization. Numerical experiments are conducted using real-world data in the Manhattan area. On average, 100 additional units of PUDOs in a region could reduce the traffic speed by 3.70 and 4.54 miles/hour (mph) on weekdays and weekends, respectively. Rerouting trips with PUDOs on curb space could respectively reduce the system-wide total travel time (TTT) by 2.44% and 2.12% in Midtown and Central Park on weekdays. A sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework. Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant 52102385], grants from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grants PolyU/25209221 and PolyU/15206322], a grant from the Otto Poon Charitable Foundation Smart Cities Research Institute (SCRI) at the Hong Kong Polytechnic University [Grant P0043552], and a grant from Hong Kong Polytechnic University [Grant P0033933]. S. Qian was supported by a National Science Foundation Grant [Grant CMMI-1931827]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0195. [ABSTRACT FROM AUTHOR]

This paper aims to offer novel empirical evidence as to the identification of the most effective Trigger Speeds (TS) for Vehicle-Activated Signs (VAS) in rural areas. To achieve this, an experimental study was carried out in the area of Scottish Borders, UK, in settlements with 20 mph speed limits. To determine the effective trigger speeds for VAS, in terms of reducing vehicle speeds, a comparative quantitative analysis was conducted using traffic and speed data collected during different waves of a traffic survey, for which various TS settings were deployed (24 mph, 28 mph and 35 mph). The descriptive analysis showed that the 24 mph TS threshold seems to be more effective compared to the other TS settings considered, mainly in terms of reducing the 85th percentile speeds. The 28 mph threshold was identified to yield slightly lower mean speeds compared to the other TS settings. Non-parametric and parametric statistical tests were conducted on the basis of approximately 2.8 million speed observations to identify any statistically significant speed differences under various TS settings. Overall, the findings of this study show that the application of VAS in addition to 20 mph speed limits helps reduce vehicle speeds. In particular, setting a TS to remind drivers, especially those driving between 21–24 mph, that they should slow down has led to statistically significant reductions in speeds driven. This provides road safety benefits both from the reduction in kinetic energy in the transport system, any consequent crash-related outcomes, and also by improving perceived safety for all who use and live close to roads where 20 mph speed limits augmented with VAS have been implemented. [ABSTRACT FROM AUTHOR]

• The number and mean duration of glances away from the non-driving related tasks (NDRTs) increased significantly when the standard deviation (SD) of speed was high. • The mean speed had a significant effect on the mean glance duration, with longer glances away from NDRTs when mean speed was low, compared to that in high speed. • There was a significant effect of age on NDRT engagement, with older drivers less likely to engage in NDRTs, while female drivers were more engaged in NDRTs than males. Previous simulator and real-world studies with SAE Level 2 automated vehicles (AVs) have shown that, when compared to manual driving, drivers are more inattentive to the driving environment when automation is engaged, as reflected by fewer glances towards the forward roadway and side/rear view mirrors, and more focus on non-driving related tasks (NDRTs). Manual driving studies also suggest that drivers are more likely to engage in NDRTs during slow-moving or stationary traffic conditions. The aim of the current study was to understand whether NDRT engagement and visual attention patterns are impacted by the driving environment while drivers experienced a ride in a real-world SAE Level 3 AV. Forty-six video clips, from 32 drivers interacting with NDRTs during L3 motorway driving were analysed for this study. Due to the absence of externally facing cameras, the mean and standard deviation (SD) of driving speed were used as a proxy for assessing the surrounding traffic volume. The number of glances, and mean glance duration away from NDRTs per minute, were used as proxy measures for NDRT engagement. A generalised linear mixed model (GLMM) was used to investigate the effect of surrounding traffic on NDRT engagement. Results showed that the number and mean duration of glances away from the NDRT increased significantly when the SD of speed was high. The mean speed had a significant effect on the mean glance duration, with longer glances away from NDRTs when mean speed was low, compared to that in high speed. There was a significant effect of age on NDRT engagement, with older drivers less likely to engage in another task, while female drivers were more engaged in NDRTs than males. Overall, the results indicate that drivers' propensity to engage in NDRTs is impacted by the AV's speed, which is influenced by the volume of surrounding traffic. These results are useful for understanding the implications of surrounding traffic on drivers' self-regulated engagement in NDRTs in the real world during SAE Level 3 driving. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic had a significant impact on mobility worldwide, specifically through stay-at-home orders. There is a general consensus in the literature regarding the reduction of traffic during that period, and a trend toward increased speeds. However, the literature is still scarce regarding the pandemic's long-term and site-specific effects on traffic volumes and speed behavior. In this context, the present study looks at speed and traffic volume data, using several temporal and spatial filters to isolate the effect of the pandemic from other elements that can influence speeds, such as congestion, construction, weather, and faulty detector data. Data from Wisconsin for the stay-at-home period in 2020 and corresponding time periods in 2019 and 2021 were used. Traffic volume and speed were analyzed using descriptive and inferential (Kolmogorov–Smirnov Test and Mann Whitney U Test) statistics. While the results for traffic showed the expected reduction in 2020 in relation to other years, speeds also showed a reduction in 2020 for eight of the twelve analyzed locations, most of these rural areas. Furthermore, the results show a return of speeds to pre-pandemic levels in 2021, associated with the partial or complete recovery of traffic volumes. • Evaluation of speeds and traffic volumes before, during, and after Covid-19 lockdown in Wisconsin. • Speed and traffic data quality control from several data sources. • Reduction of traffic volumes in 2020 and partial or complete recover in 2021. • Reduction of speeds in 2020 and a return to pre-pandemic levels in 2021. [ABSTRACT FROM AUTHOR]