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Varanasi's prominence as a cultural and historical center means it receives visitors from all over the world. The city's tourism industry is a boon to the local economy. Better transportation infrastructure is crucial to attracting more tourists and increasing revenue. Varanasi is currently experiencing terrible difficulties due to various roadside frictional activities. Vehicle ownership grows in tandem with population growth. The increase in the number of vehicles on the road significantly impacts the reliability of the transportation network since land availability is fixed. The various roadside frictional activities usually found in the streets of Varanasi are on-street parking, pedestrian crossing, and Non-Motorized Vehicles (NMVs). There are not enough legal on-street parking and segregated lanes for NMVs/slow-moving vehicles or demarcation for pedestrian movements in the old city of Varanasi. Vehicles in Varanasi are traditionally parked on the street due to the narrow carriageways. Slow-moving vehicles are forced to move with fast-moving vehicles, and pedestrian crossings affect traffic flow. Integrated movement of slow-moving vehicles and rapid-moving vehicles affects traffic speed, pedestrian crossings impact the Level of Service (LOS), and on-street parking results in the reduction of the effective carriageway width and, hence, road capacity. This paper aims to identify the impact of pedestrian crossings, NMVs, and on-street parking on the traffic flow, speed, capacity, and LOS of urban arterial roads in Varanasi. To achieve this objective, two case studies were considered: the base section (with minimum side friction) and the friction section (with maximum side friction). The videography method was used for data collection. Nine hours of data were collected from 9:30 am to 6:30 pm. The video was played on the screen for data extraction. The speed model was developed by using fundamental diagram methods. Speed-density curve was drawn using Greenshield’s model. The speed-flow curve was derived from the speed-density curve to estimate the capacity at the base and friction sections. Reduction in capacity was determined by comparing friction section capacity with base section capacity. V/C ratio of a particular road compared with the V/C value provided by IRC 106 to predict LOS. A correlation model was developed between the percentage reduction in capacity and road width. Increase in parking, the proportion of NMVs, and pedestrian crossing frequency reduce the traffic flow. It was observed that a 34.10% capacity loss occurs at a 29.31% reduction in effective width, and a 40.54% loss occurs at a 40.98% reduction in effective width. Roads with frictional activities affected its level of service up to LOS-E (for the Bhelupur section). [ABSTRACT FROM AUTHOR]

Although transportation systems are an increasing necessity in today's connected world, the over-reliance on internal combustion engine vehicles has caused an increase in air and noise pollution. Air and noise pollutions are among the negative environmental factors that contribute to disease occurrence due to their negative health consequences. Literature has showed that air and noise pollutions were responsible for thousands of premature deaths in Europe. This has pushed scientists to search for models to calculate the effect of traffic on air and noise pollution, to help predict future scenarios, and to work on ways to mitigate that increase in pollution. In this paper, a statistical model is done by using data measured for 25 speed bump locations in Kuwait, which included traffic flow data in the form of vehicle count and classification, as well as noise level measurements taken through an Amprobe SM20 sound meter, in addition to air pollutant data obtained from the Environment Public Authority (EPA) in Kuwait. The results of the multivariate linear regression model showed that high traffic counts resulted in significantly higher noise levels, reaching more than 70 decibels in certain locations, which is considered unhealthy for extended time periods. The model also showed that sulfur dioxide levels were impacted by both light and heavy vehicles, while particulate matter less than 10 µm was affected mainly by heavy vehicles. An online survey on speed bumps was completed by 803 participants to understand the behavior of people at speed bumps in Kuwait and understand if age and gender might be a predictor of how people would behave, and this was done by doing Pearson's chi-squared correlation tests on the results obtained from the survey. [ABSTRACT FROM AUTHOR]

Predicting future traffic state (e.g., traffic speed, volume, travel time, etc.) accurately is highly desirable for traffic management and control. However, network-wide traffic flow has complicated spatial-temporal dependencies, making it challenging to predict. This study proposes a multi-weighted graph 3D convolution network (MWG3D) to predict future network-wide traffic speed, considering the spatial-temporal heterogeneous effects of multiple external factors (i.e., points of interests (POIs), roadway physical characteristics and incidents). The network is composed of a Graph-3D convolution (G3D) module and an incident impact module. In G3D module, a weighted graph convolution is developed first, which extracts complex spatial dependencies of traffic flow considering heterogeneous effects of POIs and roadway physical characteristics. These external factors have great influence on the periodicity of human daily activities, which in turn cyclically affect traffic flow. The weighted graph convolution is further connected with 3D convolutions to extract temporal dependencies of traffic flow, accounting for temporal heterogeneous effects of these external factors. An incident impact module is separately developed to account for spatial-temporal heterogeneous effects of incidents. These external factors could lead to abrupt and temporary changes in traffic flow. The proposed network is evaluated on two real-world datasets. The results show that MWG3D outperforms a selection of the state-of-the-art models. Furthermore, the spatial-temporal heterogeneous effects of external factors are crucial to prediction accuracy. [ABSTRACT FROM AUTHOR]

Results of the study of the roughness and friction properties of the road surface of urban streets influence on the traffic safety are presented in this article. As a result of this study, an empirical dependence was obtained, reflecting the relationship between the traffic speed and a load factor under different traffic conditions; the relationship between the flow density and street capacity was also determined. The maximum permissible value of the friction coefficient for experimental streets is 0.4, and 50% of the object of study meets the requirements of operation; 50% does not. From the proposed graph of the relative dependence of pavement roughness and safety factor, it follows that the most dangerous are the streets, the pavement roughness of which is in unsatisfactory condition. To effectively solve the problem of destruction of the road surfaces, it is first of all necessary to eliminate the cause of the damage that occurs, not just its consequences. [ABSTRACT FROM AUTHOR]

The traffic speed deflectometer (TSD) can collect network-level deflection data in a cost-effective and timely manner. However, an automated feature extraction method is needed to interpret such large amounts of data. Basis pursuit (BP) is one such technique: BP can sparsely decompose TSD measurements over a given basis or set of bases of the signal vector space that can represent a particular signal feature. For instance, the TSD surface deflection estimates can be reconstructed as a combination of wavelets, which represent continuously varying features like changes in pavement properties, plus pulses representing the response from structurally weak spots. Yet, the denoised measurements estimated by BP may either be riddled with several false positives (spikes that mismatch real weak spots) or be constructed out of true positive features with damped amplitude. This paper presents reweighed L1 minimization (RWL1), an enhancement to BP to both correct the dampening and discard false positives. This paper introduces RWL1 with examples from simulated data to show its advantage over vanilla BP denoising, plus a demonstration featuring real TSD measurements from a networkwide survey to demonstrate RWL1's potential as an exploratory analysis tool to detect structurally weak locations within the pavement network worthy of further investigation at the project level. [ABSTRACT FROM AUTHOR]

• Motorized two-wheeler rider behavior in mixed naturalistic traffic was clustered. • Riders' baseline behavior in acceleration and deceleration events was identified. • Baseline behavior indicated moderate speeds with low accelerations/decelerations. • Young students had more over-speeding events compared to working professionals. • Risky and non-risky riders were identified through over-speeding aggressive events. Riding behavior is characterized by speed choices and evasive actions performed by riders while riding under normal conditions through naturalistic riding studies (NRS). Riders accelerate to attain free speeds and respond to traffic conflicts through various evasive actions. As safety of a motorized two-wheeler (MTW) rider predominantly depends upon his/her acceleration and deceleration behavior, we aim to classify acceleration and deceleration events obtained through a short-term NRS, and identify participants' riding behaviors. The NRS included 58 riders and captured their kinematic profiles and surrounding traffic conditions through an instrumented MTW. Riders consisted of students and working-professionals with varying ages. Riders drove a 32 km round trip, and acceleration and deceleration events were extracted from riding profiles. The k-means clustering technique revealed three acceleration and three deceleration clusters. Acceleration clusters A1, A2, A3 were high-speed moderate-acceleration, moderate-speed low-acceleration and low speed high-acceleration clusters, respectively. Deceleration clusters D1, D2, D3 were low-speed high-deceleration, high-speed moderate-deceleration and moderate-speed low-deceleration clusters, respectively. This study revealed the most frequent riding behaviors (acceleration cluster A2 and deceleration cluster D3) which form MTW riders' base-line riding behaviors. Principal component analysis revealed unique riding behavior of each acceleration and deceleration cluster. Study results also revealed significant differences between cluster contributions of student-riders and working-professionals, indicating that age and experience of riders also affect the number of acceleration/deceleration events. [ABSTRACT FROM AUTHOR]

The new generation smart highways (NGSH) have emerged as irresistible trends to enhance the efficiency and safety of transportation systems. An integral component of the NGSH is the automation of the intelligent traffic management system (ITMS). This study investigates an integrated framework for the ITMS that incorporates the fine-grained microscopic simulation and deep learning technologies based on real-time traffic data. The framework commences by performing dynamic corrections based on the license plate, vehicle speed, location, and other information provided by the real-time bayonet data in order to simulate the realistic traffic flow along the highway. A deep learning model based on long short-term memory (LSTM) is then applied to predict the short-term traffic volume on major highway segments. Based on prediction results, a collaborative management method is constructed that combines variable speed limits and ramp metering. The case study on the Shanghai–Hangzhou–Ningbo Highway in China suggests the real-time simulation model can control the average error of the traffic volume on the main segments by 4.58%. The LSTM-based model can accurately predict the short-term traffic volume with a relative error of 85% below 15% in both offline and online modes. Consequently, the proposed collaborative framework improves the average speed and traffic volume of controlled sections by 3.62% and 4.35%, respectively, demonstrating its effectiveness in improving the operation and management of the smart highways. DOI: 10.1061/JTEPBS.TEENG-7729. © 2023 American Society of Civil Engineers. [ABSTRACT FROM AUTHOR]

The phase angle is a good indicator of the current fatigue condition of asphalt concrete (AC) layers, but estimating phase angles from drilled core samples is destructive, expensive, and unsuitable for large-scale applications. Similar to the phase angle, a lag between load and response has recently been observed in the deflection basin of the nondestructive traffic speed deflectometer (TSD), i.e., the lag between the loading point and the maximum deflection point, and the deflection lag may be closely related to the phase angle. This study investigated the potential of TSD deflection lag as a nondestructive indicator of pavement fatigue conditions. The relationship between AC phase angle and TSD deflection lag was investigated using the 3D-Move program, and the effects of pavement structure, TSD speed, and test temperature on the deflection lag were also investigated. Field TSD data collected in Tennessee were used to verify the relationship between deflection lag and fatigue cracking. The results show that the lag distance increases uniformly with increasing fatigue levels (phase angles) until fatigue failure occurs. However, the lag distance is also closely related to the asphalt thickness and subgrade modulus; therefore, only the lag distance of pavements with the same structures can be compared to identify the fatigue sections. Overall, the lag distance can be used as an implicit indicator of the fatigue condition of the pavement to predict the initiation and growth of fatigue cracks. Fatigue cracking is expected to occur where the lag distance is relatively large. [ABSTRACT FROM AUTHOR]

Correctly capturing the spatial‐temporal correlation of traffic sequences will benefit to make accurate predictions of the future traffic states. In the paper, the methods of enhancing road spatial and temporal information representation are proposed. Firstly, the parameter matrix of each road is constructed to represent the road‐specific traffic patterns for the graph convolution neural network and the recurrent neural network. Then, the node embedding, and matrix factorization are used to reduce the scale of the parameter matrix. Secondly, the node embedding‐based Data Adaptive Graph Generation model was introduced to infer the indirect relationship of each node, and the gating mechanism is designed to control the weights of the direct spatial information and the indirect spatial information. Thirdly, to enhance the traffic sequence representation, the time tag and peak tag for the sequences are designed at each sampling moment. At last, the Enhanced Road Information Representation in Graph Recurrent Network (En‐GRN) is proposed to predict traffic speed, and the prediction performance is tested on SZ‐taxi and Los‐loop dataset. The experimental results show that the presented works are effective for improving traffic prediction accuracy. [ABSTRACT FROM AUTHOR]

Sidoarjo Regency has several road sections that often have traffic problems such as congestion. The congestion is caused by economic improvement which also causes a person's mobility to increase so that there is a fairly high traffic movement. Likewise, what happened to the Sruni Buduran Highway, Tebel Buduran Highway, and Buduran Sidoarjo Highway East Java as study areas. By reaching a road length of 3 Km. The study in this study aims to analyze and map the mathematical relationship of Volume - Density - Traffic Speed, so that the value of free flow speed, maximum density, and maximum volume can be obtained on the Sruni Buduran Sidoarjo road section. With the method used is the Greenshields model, which is then compared with capacity using the Indonesian Road Capacity Guidelines method (2014), the survey was conducted at 08.00 - 18.00, with 4 survey days, Monday to Thursday in each segment to obtain primary data in calculating the Greenshields mathematical model. From this study, it shows that the highest free flow speed (Sff) value occurred on Wednesday, May 17, 2023 on segment 3 of the Buduran Sidoarjo Highway, (Sidoarjo - Surabaya) with a value of Sff = 70.888 km / hour, the highest capacity or maximum current (VM) occurred on Tuesday, May 2, 2023 on segment 1 of the Sruni Buduran Highway, (Surabaya - Sidoarjo) VM = 722.005 emp/hour, the highest speed in maximum traffic flow (SM) conditions occurred on Wednesday, May 17, 2023 on segment 3 of the Buduran Sidoarjo Highway, (Sidoarjo - Surabaya) SM = 37.444 km / hour, and the highest density in maximum traffic flow (DM) conditions occurred on Tuesday, May 2, 2023 on segment 1 of the Sruni Buduran Highway, (Surabaya - Sidoarjo) DM = 22.549 emp/km. [ABSTRACT FROM AUTHOR]

The transform-based tensor nuclear norm (TNN) methods have shown good recovery results for tensor completion. However, the TNN methods are based on the single-tube transforms in which transforms are applied to each tube independently. The performance of the single-tube transformbased TNN methods is not good for recovery of missing tubes in multidimensional images (e.g., all the observations are missing in a pixel location of multispectral images). The main aim of this paper is to address this issue by proposing and developing a learnable group-tube transform-based TNN (GTNN) method that can effectively explore the correlation of neighboring tubes by leveraging a learnable group-tube transform. The proposed learnable group-tube transform is a separable three-dimensional transform that consists of a one-dimensional spectral/temporal transform (i.e., single-tube transform) and a two-dimensional spatial transform. Such group-tube transform can effectively explore the correlation of neighboring tubes. Based on the elaborately designed low-rank metric GTNN, we suggest a low-rank tensor completion model. To solve this highly nonconvex model, we design an efficient multiblock proximal alternating minimization algorithm and establish the convergence guarantee. A variety of numerical experiments on real-world multidimensional imaging data including traffic speed data, color images, videos, and multispectral images collectively manifest that the GTNN method outperforms some state-of-the-art TNN methods especially when the observations along tubes are missing. [ABSTRACT FROM AUTHOR]

Background. The problem of this work is to develop a methodology for calculating cash throughput, which is based on the well-known formulas for calculating cash throughput, the main innovation of which is to take into account the time spent by trains on acceleration and deceleration en route. Currently, in the process of calculating the available capacity, a formal approach is used to determine the removal coefficients, and the site speed is also taken into account, calculated taking into account acceleration and deceleration, but representing only the ratio of the length of a certain section of the railway polygon to the time of the train following it. Materials and/or methods: simulation modeling, economic and mathematical modeling, AnyLogic software package, methods of general systems theory, logistics. Results. An analytical formula is proposed that allows calculating the available throughput with higher accuracy. The results of the study can be used in the dispatch centers of transportation management of JSC “Russian Railways” in order to more fully analyze the operation of the landfill of the road, railway lines or individual sections in order to more fully analyze the work performed on transportation. Conclusion. The article develops a methodology for calculating the available capacity on a railway line with full consideration of the effect of acceleration and braking of trains on their way to the resulting available capacity.

Results of the study of the roughness and friction properties of the road surface of urban streets influence on the traffic safety are presented in this article. As a result of this study, an empirical dependence was obtained, reflecting the relationship between the traffic speed and a load factor under different traffic conditions; the relationship between the flow density and street capacity was also determined. The maximum permissible value of the friction coefficient for experimental streets is 0.4, and 50 % of the object of study meets the requirements of operation; 50 % does not. From the proposed graph of the relative dependence of pavement roughness and safety factor, it follows that the most dangerous are the streets, the pavement roughness of which is in unsatisfactory condition. To effectively solve the problem of destruction of the road surfaces, it is first of all necessary to eliminate the cause of the damage that occurs, not just its consequences.

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip. [ABSTRACT FROM AUTHOR]

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory. [ABSTRACT FROM AUTHOR]

The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. [ABSTRACT FROM AUTHOR]

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

With the increasing availability of location‐aware devices, passively collected big GPS trajectory data offer new opportunities for analyzing human mobility. Processing big GPS trajectory data, especially extracting information from billions of trajectory points and assigning information to corresponding road segments in road networks, is a challenging but necessary task for researchers to take full advantage of big data. In this research, we propose an Apache Spark and Sedona‐based computing framework that is capable of estimating traffic speeds for statewide road networks from GPS trajectory data. Taking advantage of spatial resilient distributed datasets supported by Sedona, the framework provides high computing efficiency while using affordable computing resources for map matching and waypoint gap filling. Using a mobility dataset of 126 million trajectory points collected in California, and a road network inclusive of all road types, we computed hourly speed estimates for approximately 600,000 segments across the state. Comparing speed estimates for freeway segments with speed limits, our speed estimates showed that speeding on freeways occurred mostly during the nighttime, while analysis of travel on residential roads showed that speeds were relatively stable over the 24‐h period. [ABSTRACT FROM AUTHOR]

The ability of a Traffic Speed Deflectometer (TSD) to detect joint movement on a rigid pavement was investigated. Based on a mathematical model of a moving vehicle on a concrete pavement, a method of calculating the joint displacement from a TSD measurement was developed. The joint displacement indicator was tested in practice on a concrete highway in northern Germany. Short-term repeatability, day-to-day repeatability, and reproducibility between two different TSD vehicles were evaluated based on the measurements. It was found that the TSD is able to consistently find movement at the same joints, and that the magnitude of the movement has a high short-term repeatability. Day-to-day repeatability and reproducibility between vehicles was lower due to temperature effects in the concrete slabs. However, also for these cases, there was a high level of consistency in the detected joints. Based on the distance between successive joints, a method of identifying potential locations of transverse cracks was developed. Finally, a way of quantifying joint movement in terms of spatially averaged quantities is presented. [ABSTRACT FROM AUTHOR]

A significant association between crash severity and operating speed is known to exist. However, the findings related to the speed-crash association are inconclusive. Some studies found that higher speed is associated with a higher number of crashes, whereas other studies found the opposite result. Some of the critical issues in this research problem result from study design, the definition of operating speed measures, types and granularity of operating speed measures, spatial correlation, and design standards of different roadway facilities. The road safety profession will benefit greatly from informative research on the impact of vehicle operating speed, roadway design elements, and traffic volume on crash outcomes. This study investigated the speed-crash association in both annual and daily level datasets to determine how roadway characteristics interact with various speed measures to impact the likelihood of crash occurrences on both annual and daily levels. For annual models, the average operating speed is positively associated with both fatal and injury and property damage only (PDO) crashes. However, for daily models, this association is mostly negative and insignificant. The standard deviation of operating speed is positively associated with crash occurrences for both daily and annual models. The findings of this study can provide additional insights into the speed-crash association literature. [ABSTRACT FROM AUTHOR]

Accurate highway traffic forecasting is a critical task in intelligent transportation systems (ITSs), which needs to capture complex spatiotemporal dependencies from traffic sensors data. Recently, spatial-temporal graph networks have become one famous technology for various traffic forecasting tasks. Nevertheless, most of these works have assumed that the correlations between the sensors are fixed, so that it is often unable to effectively deal with the more realistic and dynamic traffic environment. To tackle this issue, we propose a joint pretraining framework for traffic flow forecasting with a gating diffusion graph attention network (JointGraph). Specifically, our proposed joint training architecture consists of two parts: network reconstructor (reconstruct a discrete graph from input data) and spatiotemporal model (forecast traffic speed with the generated network). Owing to the capabilities of the network reconstructor in generating graph structure through input node features, it is possible to apply our spatiotemporal model among multiple datasets directly. In addition, our model can expediently use information from data-rich regions and improve traffic forecasting performance on data-lacking regions in highway networks. Experiments are conducted on traffic datasets: METR-LA, PEMS-BAY, and trajectory dataset: BikeNYC. The results show that our JointGraph achieves superior performance with the state-of-the-art baselines, which further indicate that our pre-training mechanism in JointGraph provides an effective solution for multi-dataset cooperative training. [ABSTRACT FROM AUTHOR]

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Network traffic state prediction has been constantly challenged by complex spatiotemporal features of traffic information as well as imperfection in streaming data. This paper proposes a traffic flow prediction model for spatiotemporal graph networks based on fusion of attention mechanisms (STGNN-FAM) to simultaneously tackle these challenges. This model contains a spatial feature extraction layer, a bidirectional temporal feature extraction layer, and an attention fusion layer, which not only fully considers the temporal and spatial features of the traffic flow problem but also uses the attention mechanism to enhance the critical temporal and spatial features to achieve more accurate and robust predictions. Experimental results on a network traffic speed dataset PeMSD7 show that the proposed STGNN-FAM outperforms several important benchmarks in prediction accuracy and the ability to withstand interference in the data stream, especially for mid- and long-term prediction of 30 minutes and 45 minutes. [ABSTRACT FROM AUTHOR]

Tunnel lining falling blocks can cause great threats to traffic safety, resulting in traffic interruptions or speed limits and endangering operational safety. Traditional lining disease remediation measures have low efficiency and high costs and are time consuming. Combining the research methods used in the literature, indoor testing, and model experiments, a very normal material, namely, polyurea spray film material, is proposed as a means by which to remediate the lining falling block disease in tunnels. A 1 : 1 tunnel lining structure model was established at the site, and four experimental conditions were tested. The experiments revealed that the tensile stress was less than the tensile strength and that no tensile fracture occurred in the polyurea spray film material under the joint action of the block's self-weight and aerodynamic loads. Factors such as side seam treatment, surface sanding, and spray temperature may influence the bond strength of the polyurea spray film material. In order to remediate the issue of lining blocks falling, we recommended increasing the bond strength between the spray film material and the concrete or increasing the thickness of the polyurea spray film material. [ABSTRACT FROM AUTHOR]

As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle detector (VD) data to predict crash occurrences. However, traffic accidents are influenced by multiple factors, such as traffic speed differences, traffic density, and weather conditions, and direct associations may not exist between sensor data and crash incidents. Therefore, data integration and association methods must be used to examine ETC and VD data through traffic flow theories, to extract key data from datasets and to facilitate model training. In this study, a data association method and framework combined with deep learning was proposed to construct a crash prediction and warning model for national highways in Taiwan. The results revealed a model accuracy of 94%, indicating that the model had a low error rate and was suitable for the prediction of traffic accidents. Overall, this study provides referential data for the Freeway Bureau of Taiwan to conduct comprehensive assessments and develop strategies for crash prevention. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice. [ABSTRACT FROM AUTHOR]

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours. [ABSTRACT FROM AUTHOR]

Traffic calming devices (TCD) like speed bumps are effective measures in reducing speed on residential roads but in a limited section of the road. Previous studies found that if the entrance speed of the traffic calming road (TCR) is higher than the posted speed limit (30 kmph), it will affect speed reduction by TCD. Little research has been undertaken on predicting speed at the entrance of a TCR. Thus, the purpose of this study is to predict the variables of interest in the context of the study area by applying a methodological approach described in this study; a combination of multiple linear regression, and artificial neural networking (ANN) models. Seventeen variables derived from four categories including road design, roadside environment, traffic, and speed were considered in the proposed model. Two approaching criterion was defined in this study: a minor (speed limit 30 km/h) and a major approach (speed limit 40/50 km/h). Over 1000 vehicle speeds were collected from almost 20 different urban roads in Japan. Entrance characteristics, lane width, barrier types, posted speed limit, etc. have found as significant predictors in the regression output. Furthermore, the ANN technique was employed where the input variables were the significant predictors of regression models, and variable importance was also calculated using sensitivity analysis. Although the results of sensitivity and regression are different, sensitivity plays a role in illuminating the relationship between road factors and speed. By comparing the prediction performance between the two models this study reveals that developing an ANN model followed by regression output could be a consistent method for analyzing the entry speed of TCR for varying approaches. Additionally, the findings can be used to effectively predict the entry speed of TCR without installing a gateway design and help to achieve a safer speed through modifications to the road features. [ABSTRACT FROM AUTHOR]

We present a general multi-scale approach for modeling the interaction of controlled autonomous vehicles (AVs) with the surrounding traffic flow. The model consists of a scalar conservation law for the bulk traffic, coupled with ordinary differential equations describing the possibly interacting AV trajectories. The coupling is realized through flux constraints at the moving bottleneck positions, inducing the formation of non-classical jump discontinuities in the traffic density. In turn, AVs are forced to adapt their speed to the downstream traffic average velocity in congested situations. We analyze the model solutions in a Riemann-type setting, and propose an adapted finite volume scheme to compute approximate solutions for general initial data. The work paves the way to the study of general optimal control strategies for AV velocities, aiming at improving the overall traffic flow by reducing congestion phenomena and the associated externalities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Work zone speed is one of the most important factors in road construction safety management. This work presents a computer vision based technique designed to measure lane-specific individual vehicle speed using existing traffic monitoring cameras and computers. The resulted speeds support the influence analysis of factors including traffic control, lane positions, and construction activity. Object detection (YOLOv5) and tracking (Deep-SORT) algorithms are combined to track the vehicles. In particular, 21 days' worth of road construction videos are collected from a pole-mounted traffic monitoring camera operated by the Texas A&M University Transportation Services. Based on the object detection results, a novel construction activity inference technique is developed to approximate the times when construction workers are present. Based on this time separation, the vehicle speeds with and without the presence of construction activity are compared. The proposed framework is able to measure speeds with an error ranging from 0 to 6.4 kilometers per hour (KPH). Detailed analysis of this video data suggests that traffic control with barrels in the median work zone lowers the average speed (for all vehicles) by 15 KPH. The lane adjacent to the work zone also has higher speed variation than the other lanes. The construction activity speed comparisons show when the traffic is slow (possibly traffic after a red light), the difference is statistically significant with a p-value ranging from 0.01 to 0.03. When the traffic is fast (possibly traffic encountering a green signal as they approached the nearby intersection) construction activity has no significant effect on the work zone speeds. The proposed CV technique is a reliable and cheap method to measure lane-specific work zone speeds. The derived measurements support detailed safety analysis. Other than work zone speeds, the proposed technique can also be used for regular traffic speed monitoring. [ABSTRACT FROM AUTHOR]

Failure of pipelines below road pavement results to the disruption of both the traffic movement and the consumers of the pipelines. Intermediate safeguard layer can be used to protect the pipeline from heavy traffic loads. The present study proposed analytical solutions to obtain the dynamic response of buried pipe below road pavement with and without considering safeguard based on the concept of triple and double beam system respectively. Pavement layer, safeguard and the pipeline are considered as Euler Bernoulli's beam. Advanced soil model is used (viscoelastic foundation with shear interaction between springs) to model the surrounding soil. Self-weight of soil is also considered in the present study. The obtained governing coupled differential equations are solved adopting finite sine Fourier transform, Laplace transform and their inverse transformation. The proposed formulation is initially verified with the past numerical and analytical studies and then validated with the three-dimensional finite element based numerical analysis. From parametric study it is perceived that the stability of the pipe can be significantly increased by providing intermediate barrier. Further, pipe deformation is increases with increasing traffic loads. At very high-speed range (> 60 m s−1), pipe deformation is significantly rises with increasing traffic speed. The present study can be useful in preliminary design stage before performing rigorous and expensive numerical or experimental study. [ABSTRACT FROM AUTHOR]

Background: The availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models. Purpose: Such features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams. Methodology: This paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis. Results: Compared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%. Conclusions: Results obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) and variable advisory speed (VAS) signs are efficient, cost-effective and among the state-of-the-art active traffic management (ATM) strategies. They adopt the idea of dynamically changing posted speed limits to improve highway safety performance and operation by harmonizing traffic speed. VSL/VAS system involves changing speed limits according to real-time traffic events and weather conditions. Hence, traditional average annual daily traffic (AADT) based crash prediction models may not capture the temporal effect of traffic characteristics due to the high level of aggregation. To address this issue, short-term safety performance functions (SPFs) with aggregation levels of average annual weekday hourly traffic (AAWDHT) and average annual weekday peak traffic (AAWDPT) along with AADT-based SPFs were developed using high-resolution traffic detector and VSL/VAS operational data. In this study, the Poisson log-normal model performed well at each level of aggregation and thus is recommended for developing short-term SPFs. In line with previous studies, traffic volume and standard deviation of speed were found to be positively associated with crash frequency in all the estimated models. In addition, it was found that implementation of VSL/VAS significantly reduced crash frequency by 15.97% and 26.14% for the AAWDHT and AAWDPT models, respectively. The safety improvement was captured in the short-term models in a more distinguished way than in the highly aggregated AADT-based model. The findings of this study pave the way for practitioners and policymakers to evaluate and select important parameters for VSL/VAS strategy implementation on freeways. [ABSTRACT FROM AUTHOR]

In the road traffic safety domain there is a need for using proactive (non‐crash‐based) indicators, known as safety performance indicators (SPIs). Traffic speed based on big data (floating car data [FCD]) could help develop network‐wide SPIs, but related knowledge and experience are insufficient so far. The authors attempted to fill this gap by using nationwide Italian FCD to develop speed‐related SPIs and validating their relationship to crashes to see their potential explanatory value. The authors calculated the coefficient of variance (CV), congestion index (CI), and the number of incidents as candidate SPIs. For validation, the authors used linear correlation, crash frequency model, and ranking consistency. Incidents turned out to be the best SPI, especially for motorways. [ABSTRACT FROM AUTHOR]

With the emergence of connected vehicle data and high-resolution weather data, there is an opportunity to develop models with high spatial-temporal fidelity to characterize the impact of weather on interstate traffic speeds. In this study, 275,422 trip records from 41,234 unique journeys on 42 rainy days in 2021 and 2022 were obtained. These trip records are categorized as no rain, slight rain, moderate rain, heavy rain, and very heavy rain periods using the precipitation rate from NOAA High-Resolution Rapid-Refresh (HRRR) data. It was observed that average speeds decreased by approximately 8.4% during conditions classified as very heavy rain compared to no rain. Similarly, the interquartile range of traffic speeds increased from 8.34 mph to 12.24 mph as the rain intensity increased. This study also developed a disaggregate approach using logit models to characterize the relationship between weather-related variables (precipitation rate, visibility, temperature, wind, and day or night) and interstate speed reductions. Estimation results reveal that the odds ratio of reducing speed is 5.8% higher for drivers if the precipitation rate is increased by 1 mm/h. The headwind was found to have a positive significant impact of only up to a 10% speed reduction, and speed reduction is greater during nighttime conditions compared to daytime conditions by a factor of 1.68. The additional explanatory variables shed light on drivers' speed selection in adverse weather environments, providing more information than the single precipitation intensity measure. Results from this study will be particularly helpful for agencies and automobile manufacturers to provide advance warnings to drivers and establish thresholds for autonomous vehicle control. [ABSTRACT FROM AUTHOR]

The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020–2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The technology of traffic sign detection is an important technology in autonomous driving. However due to the small size of traffic signs and the complicated background there are some problems in the practical application of traffic sign detection technology: the detection accuracy of one-stage traffic sign detection algorithm is low, the detection speed of two-stage traffic sign detection algorithm is slow. To achieve real-time and accuracy detection for traffic signs, we treat traffic sign detection as an end-to-end problem in this paper and proposed an improved one-stage traffic sign detection algorithm: Attention-YOLO V4. In order to achieve real-time and high-accurate detection of traffic signs so we analyzed the principle of YOLO V4 for small objects detection: in order to improve the ability of YOLO V4 backbone network extracting traffic sign features we decide to combine channel attention mechanism with residual block, in order to improve the ability of YOLO Head detect traffic signs we combine channel attention mechanism with YOLO head. We used TT100K dataset to evaluate Attention-YOLO V4 algorithm, compared with the existing methods, our method achieve real-time and accurately performance in complex backgrounds. [ABSTRACT FROM AUTHOR]

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NO x (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH 3 (ammonia), 0.26 Gg for N 2 O (nitrous oxide) and 11.38 Gg for CH 4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NO x , and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NO x emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022). [ABSTRACT FROM AUTHOR]

A non‐recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non‐recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non‐recurrent anomalies for their potential ability to distinguish non‐recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non‐recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T‐GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the "normal" traffic speeds. The hierarchical time memory detector (HTM‐detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non‐recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T‐GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM‐detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection. [ABSTRACT FROM AUTHOR]