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Current research often formalizes traffic prediction tasks as spatio-temporal graph modeling problems. Despite some progress, this approach still has the following limitations. First, space can be divided into intrinsic and latent spaces. Static graphs in intrinsic space lack flexibility when facing changing prediction tasks, while dynamic relationships in latent space are influenced by multiple factors. A deep understanding of specific traffic patterns in different spaces is crucial for accurately modeling spatial dependencies. Second, most studies focus on correlations in sequential time periods, neglecting both reverse and global temporal correlations. This oversight leads to incomplete temporal representations in models. In this work, we propose a Space-Specific Graph Convolutional Recurrent Transformer Network (SSGCRTN) to address these limitations simultaneously. For the spatial aspect, we propose a space-specific graph convolution operation to identify patterns unique to each space. For the temporal aspect, we introduce a spatio-temporal interaction module that integrates spatial and temporal domain knowledge of nodes at multiple granularities. This module learns and utilizes parallel spatio-temporal relationships between different time points from both forward and backward perspectives, revealing latent patterns in spatio-temporal associations. Additionally, we use a transformer-based global temporal fusion module to capture global spatio-temporal correlations. We conduct experiments on four real-world traffic flow datasets (PeMS03/04/07/08) and two traffic speed datasets (PeMSD7(M)/(L)), achieving better performance than existing technologies. Notably, on the PeMS08 dataset, our model improves the MAE by 6.41% compared to DGCRN. The code of SSGCRTN is available at https://github.com/OvOYu/SSGCRTN. [ABSTRACT FROM AUTHOR]

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]

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments. [ABSTRACT FROM AUTHOR]

Stream speed pattern of traffic plays a crucial role in urban transportation. The presence of on-street parking significantly reduces this speed by narrowing the carriageway width available for traffic movement, impeding the traffic flow. Therefore, understanding the traffic flow behavior in the context of on-street parking could be challenging. In this context, the present study explores the influence of on-street parking on the stream speed of regular traffic. To achieve this, data were collected at various ideal and parking sections. The stream speed ranges from 39.28 km/h to 51.47 km/h for ideal sections with significant percentage reduction observed at parking sections, ranging from 39.09% to 81.30%. This speed reduction was then modelled considering the most significant parking parameters conceptualized in this study. The findings of the study offer valuable insights that can be implemented for developing the on-street parking norms. [ABSTRACT FROM AUTHOR]

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as 'ground truth', the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes. [ABSTRACT FROM AUTHOR]

In the context of intelligent connected environments, this study explores methods to guide the speed of mixed traffic flow to improve intersection efficiency. First, the composition of traffic flow is analyzed, and a car-following model for mixed traffic flow is established, considering reaction time and the psychology of human drivers. Secondly, considering the uncertainty factors of the leading vehicle, we establish a speed guidance model for mixed traffic flow platoons. Finally, a simulation environment is built using Python and SUMO, evaluating the speed guidance effect from the perspectives of different traffic volumes and CAV penetration rates based on average stop times and average delays. The research findings indicate that the speed guidance algorithm proposed in this paper can reduce the number of parking times and delays at intersections. When the mixed traffic flow remains constant, the higher the penetration rate of CAV, the more effective the guidance becomes. However, when the traffic flow reaches a certain level, congestion intensifies, and the effectiveness of the guidance gradually diminishes. Therefore, this study is more applicable to long-distance intersections or key intersections on interconnected roads outside urban areas. [ABSTRACT FROM AUTHOR]

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v85 profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v85, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends. [ABSTRACT FROM AUTHOR]

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]

Pavement service life is an important factor that affects both the whole life cost and carbon footprint of a pavement. The service life of a pavement is affected by several different parameters which can be broadly classified into climate conditions, binder and mixture properties, pavement design, workmanship, and maintenance strategies. The current practice for determining service life of pavements involves the use of pavement design tools, which are used while constructing a new pavement or performing a reconstruction/resurfacing or pavement maintenance. In addition, field measurements using ground penetration radar, falling weight deflectometer, traffic speed deflectometer, and other techniques are also used to assess the condition of an existing pavement. The information from these measurements is then combined with pavement design software to predict potential pavement service life. The accuracy of the predicted pavement service life is affected by the associated uncertainties in the parameters that affect pavement life. The following paper presents various approaches that could be potentially used to determine the associated uncertainties in the estimation of pavement service life. The various uncertainty quantification techniques have been applied to a specific design, and the outcomes are discussed in this paper. The Monte Carlo simulation method, a system-level uncertainty quantification technique, can estimate a probabilistic pavement service life. The other uncertainty quantification schemes are software specific and provide probabilistic life factors by assumed statistical distributions. Hence, the Monte Carlo simulation technique could be one potential method that can be used for estimating a generalized pavement service utilizing predictions from various design software. [ABSTRACT FROM AUTHOR]

With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting. [ABSTRACT FROM AUTHOR]

The problem of increasing passenger traffic remains acute for municipal public transport. The value of this indicator is determined by the interest of citizens in this way of making their trips and determines the feasibility of the carrier's operation. The authors conducted a study of the problems of public transport services in large- and medium-sized cities, which found that the population's interest in public urban passenger transport has generally been significantly lost. More than 40% of the city population refuses to travel on public transport, half of the population has questions about the reliability of tariff formation, and the same number of people are not satisfied with the regular route network and schedule. City residents increasingly prefer personal vehicles or taxis for their trips, which negatively affects the revenue side of carriers, as well as the level of social comfort and the quality of life of citizens. Efforts to reduce the operating costs of the carrier are aimed at correcting the current situation with urban transport so that tariffs for transportation are more acceptable for passengers. The formation of tariffs for passenger transportation for transport companies is an urgent and complex task. It is necessary to formulate the tariff in such a way as to cover your own transportation costs in the near future and, at the same time, not exceed the psychological threshold for passengers so as not to cause their negative reaction. In addition, since the transportation of passengers by urban public transport is regulated by the authorities, it is also necessary to provide an economic justification for transportation tariffs. This is difficult in the absence of substantiated indicators of consumption rates of material resources in the transport process. To solve this problem, it is necessary to carefully analyze the current costs of operating the bus fleet, as well as forecast costs for future periods. At different periods, researchers have proposed various approaches for planning the cost of operating a bus fleet. The approach we propose is to use standardization of the consumption of material resources, considering the individual operating conditions of the bus fleet and the influence of various factors. [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

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]

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]

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]

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]

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]

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]

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.

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So> the present study aims to identify the structural characteristics of road netwoiks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the netwoik. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata. [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]

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]

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]

Inappropriate or unsafe speed is one of the main factors that affects the number of road crashes as well as the severity of the consequences. Research shows that speed is an influential factor in the occurrence of road crashes in more than 30% of road crashes with fatal outcomes and in over 12% of all road crashes. With an increase in speed, the risk of road crashes increases as well as the severity of the consequences. The perception of the vehicle speed in the traffic lane is one of the basic prerequisites for the safe functioning of traffic, that is, for the successful and timely interaction of all road users. Therefore, the challenge of this paper is to examine how the assessment of the speed of a passenger vehicle in different environments affects the prediction of the respondent's participation in road crashes. Bearing the above in mind, an experimental study was carried out, in real traffic conditions (RTC) as well as in a virtual environment using a driving simulator (DS), at different passenger vehicle speeds (30, 50 and 70 km/h), and at different perspectives of observing the oncoming vehicle (observing the vehicle from the front, from the back, from the side and from the driver's seat) by the respondents. The respondents had the task of evaluating the passenger vehicle speed, in all tested conditions and at all tested speeds. Standard statistical models and fuzzy logic were used to analyze the obtained results. The results show statistically significant differences for all tested situations and all tested speeds as well as statistically significant differences depending on the gender of the respondents, the driver's license category, the driver's experience, frequency of driving and depending on whether respondents wear glasses. Bearing in mind the results of the developed model, by applying fuzzy logic, it can be concluded that the proposed model can be used to assess the propensity of respondents to participate in road crashes, based on perception of vehicle speeds in two tested environments. [ABSTRACT FROM AUTHOR]

Traffic speed forecasting is a crucial aspect of traffic management that requires an accurate multi spatiotemporal time series forecasting technique. Previous studies typically employ graph neural network (GNN)-based methods for this task, but they are limited by their focus on spatial dependence based on real geographic distance in road networks. These structures are often inadequate for accurately describing spatial dependencies in the real world. Recently, multigraph neural networks (MGNNs) have shown considerable promise for improving forecasting performance by modelling graph structures from different spatial relationships. However, these kinds of methods do not account for complex relationships between aspects and latent dependence that cannot be known beforehand. To address these shortcomings, we propose a novel traffic speed forecasting method called adaptive multigraph convolutional networks (AMGCN), where we first introduce five predefined graphs based on spatial distance, accessibility, pattern similarity, distribution similarity and KL divergence. We fuse these graphs into a complex prior graph using a method based on spatial attention and graph relation attention. In this process, the spatial dependence in the road network is modelled comprehensively from multiple perspectives. In addition, we introduce the adaptive graph to calculate the similarity between learnable node embeddings to assist the forecasting. In this process, spatial dependencies that still cannot be captured by predefined graphs can be obtained by the way of data-driven. We utilize a mix-hop graph convolution with a residual connection to capture spatial dependencies in prior graphs and adaptive graphs. Time dependencies are also captured through causal convolution based on equidistance downsampling to prevent overfitting and redundancy in capturing spatiotemporal interactions. Extensive experiments on four real-world datasets demonstrate that our proposed method achieves superior performance compared to other baselines and effectively captures the spatiotemporal dependencies of the road network. Source codes are available at https://github.com/hfimmortal/AMGCN. [ABSTRACT FROM AUTHOR]

A smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving unwrinkled traffic flow. However, due to its spatial and temporal interdependence, traffic forecasting might be more difficult. Graph neural networks (GNN) are widely used to obtain traffic forecasting due to their capability to operate in non-Euclidean data. The topological connection of the traffic network plays a crucial role in graph structure learning. Therefore, the importance of the adjacency matrix in graph construction might be significant for effective traffic forecasting. As a result, for efficient graph construction, a Deep Spatial-Temporal Graph Neural Network (DSTGNN) is proposed for the construction of the weighted adjacency matrix and traffic speed prediction accurately by recording topological and temporal information. Three different weighted adjacency matrices are suggested depending on three different proposed algorithms for different variations of traffic's spatial condition. The new adjacency matrix is used in a novel DSTGNN for predicting the traffic state. The novel model comprised multiple layers with skip connections to adequately extract the variation of temporal and spatial information. DSTGNN outperforms the standard and other graph neural network models on four traffic speed datasets, SZ-taxi, Los-loop, PeMSD7, and METR-LA. [ABSTRACT FROM AUTHOR]

The degradation of bus system attractiveness is primarily caused by low‐level service quality and reliability. As an essential technology for bus operation management, online bus speed control has proven to be a flexible and effective solution to mitigate bus bunching and enhance the service level of bus operation systems. In this study, we propose a robust nonlinear decision mapping (RNDM) approach that uses real‐time key bus system states to control bus speeds and accounts for uncertainties associated with passenger demands at stations and traffic speeds of interstation links. We develop this approach through a design process that involves learning the input–output mapping relation of a nonlinear programming simulation‐based optimization (NLPSO) method using regression tree with AdaBoost. Critical parameters of the fitted regression tree with AdaBoost are then optimized offline using a distributionally robust simulation‐based optimization (DRSO) model that is solved by a simulation‐based optimization (SO) algorithm. The resulting RNDM method effectively handles two types of uncertainties, expressed by two ambiguity sets of probability distributions, and ensures good bus operation performance even under worst‐case uncertainty levels. Numerical experiments reveal that the RNDM, NLPSO, and integer programming SO (IPSO) methods successfully mitigate bus bunching and improve service efficiency and robustness, compared to the no‐control scenario. Furthermore, the RNDM method outperforms NLPSO and IPSO in terms of comprehensive performance under uncertainties and demonstrates practical operability. In conclusion, this study presents an innovative general framework that uses a nonlinear decision mapping optimized offline by an SO approach to address online simulation‐based optimal decision‐making problems under uncertainties, which can be applied to solve similar problems. [ABSTRACT FROM AUTHOR]

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into subcategories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA). [ABSTRACT FROM AUTHOR]

The aim of this article is to perform a comprehensive analysis of the data obtained during flow speed measurements on Latvian national roads. An algorithm for data selection was developed and implemented allowing for an analysis of comparable and unbiased traffic flow measurement data. In the course of the work, more than 150 000 000 traffic flow data records were received from the State Limited Liability Company Latvian State Roads for the period from autumn 2011 to the end of 2022. Above 30 000 000 records from 15 road sites were selected and processed. The results obtained during the analysis were visualized by depicting flow characteristics – average daily traffic, car proportion in traffic flow, average speed, speed histograms, speed rates V25, V50, V75, V90, V95, V99 and proportions of vehicles driven below various speed levels and speeding data. The data characterising the situation on Latvian national roads are intended to be used in the future traffic safety research. [ABSTRACT FROM AUTHOR]

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Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region's emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu's online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation. [ABSTRACT FROM AUTHOR]

Low and medium speed magnetic levitation traffic with short power supply distance and complex grounding network structure, prone to power supply rail grounding faults. However, the existing fault location methods do not accurately locate the fault point, making it difficult for the protective device to act to cut off the fault. To address the above problems, this paper builds a dynamic simulation model of the low and medium speed magnetic levitation power supply rails to study the distribution characteristics of the fault traveling waves after a ground fault occurs in the power supply rails, and analyses the generation mechanism of the traveling wave spectrum through formula calculation. First, the difference in current between the positive and negative bus bars is analyzed to determine whether a ground fault has occurred. Second, the direction of the current difference between stations is compared to locate the faulty section. Finally, the fault distance is calculated from the frequency difference of the fault voltage at the double‐ended station. Through simulation, the method is validated to be unaffected by fault location, fault transition resistance, noise interference, and is applicable to short circuit faults caused by lightning strikes, and the ranging error always remains within 20 m. The method has strong robustness, can effectively solve the problem of protection misoperation and accurately locate the fault point. It is suitable for low and medium speed magnetic levitation transportation power supply rail ground fault. [ABSTRACT FROM AUTHOR]

Between April and October 2018, we studied the wildlife road mortality on two roads from the western region of the Southern Carpathians, Romania. One of these roads follows the course of the Jiu River and is situated in the Jiu Gorge National Park. The other road ascends to the Vulcan Pass. On both roads, wildlife road mortality was studied with monthly frequency on four road sectors, each with a length of 500 meters. We identified 2766 road-killed animals, which belonged to 72 taxa, on the surveyed roads. On the road from the Jiu River Gorge, the number of corpses was double compared to the road from the Vulcan Pass, but the number of taxa was slightly higher. The wildlife road mortality on the two studied roads reflects the composition and peculiarities of the fauna from the neighboring regions, but also the human impact from the past. Thus, the sectors with the lowest road mortality were those affected by other types of human impact besides the road (constructed areas, touristic resources, open areas, etc.). Moreover, the climatic peculiarities of the studied year had a noticeable imprint on the wildlife road mortality. Thus, the cold spring of 2018 made field activity impossible in March (when it was still very cold and snowy). Also, there was a severe cooling in June, as the study day was very cold and windy, which triggered an unusual reduction in road mortality intensity. Local differences in road mortality can be noticed in the case of both studied roads, although the differences between them were not significant. Thus, in the higher areas from Vulcan Pass, we found corpses of animals related to a colder mountain climate. At the same time, in the Jiu River Gorge, animals related directly or indirectly to water were killed (as the road parallels the Jiu River). In the southern areas of the Gorge, we found road-killed animals related to a warmer climate. The differences in wildlife road mortality between the two studied roads could be considered consequences of different road traffic and speeds. Still, they were also caused by differences in habitats, altitude, or fauna composition between the two areas. Because of the high number of road-killed protected animals on roads situated in a national park and in its vicinity, the road network from the protected region should be carefully monitored in the future as the protected area administration should search for appropriate mitigation measures and avoid any construction or modernization of roads. [ABSTRACT FROM AUTHOR]

Pavement performance prediction is widely considered as a significant element of road infrastructure asset-management systems or Pavement Management Systems (PMS) by pavement researchers and practitioners. Predicting pavement performance significantly reduces the huge costs of constructing roads, especially in the case of countries that made incredible investments in road construction. This study mainly focuses on the implementation of the mechanistic-empirical (M-E) analysis method using the AASHTOWare Pavement ME Design (AASHTOWare PMED) software for flexible pavement distress prediction-models generation. To achieve that four steps were followed. First, the most accurate assessment that shows the combined impact of the most important parameters that affect flexible pavement performance was used to perform the AASHTOWare runs. In which, 378 design combinations of (3 traffic speed levels × 3 traffic load levels ×3 climatic zones ×7 Surface HMA mixes widely used in Egypt) at two input levels of the AASHTOWare PMED hierarchy (levels 1 &2) that typically are required for binders and hot-mix-asphalt (HMA) were used. Second, a sensitivity analysis to study the combined effect and impact of the investigated parameters on AASHTOWare PMED-predicted performance (cracking, rutting, and roughness) was conducted at the two input levels. Third, a Multiple Linear Regression (MLR) was implemented as a modeling approach to develop five performance prediction models for flexible pavements based on the AASHTOWare PMED software results. The proposed MLR models predicted each distress as a function of climatic factors, the surface HMA properties, different regions' speed levels, and traffic volume levels. Finally, a validation process of the proposed MLR prediction models was conducted. Results indicated that the proposed models yield an overall good prediction, asserting the robustness of the proposed process. Proposed MLR prediction models can be perceived as a function of Average Annual Daily Truck Traffic, Traffic speed, mean annual air temperature, and the percentage of air voids. This study provides a procedure to develop the performance prediction models of flexible pavements based on the AASHTOWare PMED approach and in accordance with different regions’ input levels. [ABSTRACT FROM AUTHOR]

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models. [ABSTRACT FROM AUTHOR]