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For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

Rapid urbanization increases the number of vehicles on a road section significantly throughout the country. Traffic in India is generally heterogeneous consisting of variety of vehicles which comprise wide range of static and dynamic characteristics. To estimate volume of such heterogeneous traffic it is essential to convert the different types of vehicles into equivalent passenger cars and express the volume in terms of Passenger Car Unit (PCU) per hour. The equivalency unit is universally adopted for measurement of traffic volume and the value is obtained by taking the passenger car as the 'Standard Vehicle.' The present study has been concentrated on four and six lane divided urban arterials in Kolkata. The PCU of vehicles have been presented for both categories of urban roads and the values are found to be higher than the values given in IRC: 106-1990. It has also been observed that, PCU values of different types of vehicles have been found to be different for different ranges of volume, composition and v/c ratio. Mathematical relationships have been developed on the basis of the variation of PCU with traffic stream parameters. The relationships are developed to forecast the PCU for several vehicular categories that have been statistically validated for different ranges of traffic stream parameters. [ABSTRACT FROM AUTHOR]

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector's recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model's ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1-8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios. [ABSTRACT FROM AUTHOR]

The critical role of network traffic measurement and analysis extends across a range of network operations, ensuring quality of service, security, and efficient resource management. Despite the ubiquity of flow-level measurement, the escalating size of flow entries presents significant scalability issues. This study explores the implications of adaptive gradual flow aggregation, a solution devised to mitigate these challenges, on flow information distortion. The investigation maintains flow records in buffers of varying aggregation levels, iteratively adjusted based on the changing traffic load mirrored in CPU and memory utilization. Findings underscore the efficiency of adaptive gradual flow aggregation, particularly when applied to a specific buffer, yielding an optimal balance between information preservation and memory utilization. The paper highlights the particular significance of this approach in Internet of Things (IoT) and contrasted environmentstringent resource constraints. Consequently, it casts light on the imperative for adaptability in flow aggregation methods, the impact of these techniques on information distortion, and their influence on network operations. This research offers a foundation for future studies targeting the development of more adaptive and effective flow measurement techniques in diverse and resource-limited network environments. [ABSTRACT FROM AUTHOR]

Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10 % to 100 % and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario. [ABSTRACT FROM AUTHOR]

This paper focuses on the analysis of traffic streams on two-lane highways, which are crucial components of transportation networks. Traffic flow measurement technologies, such as detection stations, radar guns, or video cameras, have been used over the years to detect the level of traffic and the operating conditions. This type of sensor can record a large amount of data which is useful to evaluate and monitor road traffic conditions, and it is possible to identify free-moving and constrained vehicles by processing the collected data. This study introduces an exponential headway model to identify the headway threshold above which vehicles can be considered as unconditioned. However, this value could identify vehicles which still retain some autonomy in their speed and maneuvering. Therefore, an additional criterion to distinguish between apparently and actually conditioned vehicles has been introduced, analyzing the speed differences between a vehicle and the preceding one. Three-month sequences of traffic monitored through radar devices placed on some Italian two-lane roads have been analyzed and an exponential headway model has been introduced, as an illustrative example. The results show that introducing the criterion of maneuvering freedom can significantly improve traffic flow analysis, modifying the starting critical values of 4 and 8 s per each studied section, to 2.5 and 3 s, approaching the values suggested by international manuals for traffic flow quality analysis. [ABSTRACT FROM AUTHOR]

Millions of commuters face congestion as a part of their daily routines. Mitigating traffic congestion requires effective transportation planning, design, and management. Accurate traffic data are needed for informed decision making. As such, operating agencies deploy fixed-location and often temporary detectors on public roads to count passing vehicles. This traffic flow measurement is key to estimating demand throughout the network. However, fixed-location detectors are spatially sparse and do not cover the entirety of the road network, and temporary detectors are temporally sparse, providing often only a few days of measurements every few years. Against this backdrop, previous studies proposed that public transit bus fleets could be used as surveillance agents if additional sensors were installed, and the viability and accuracy of this methodology was established by manually processing video imagery recorded by cameras mounted on transit buses. In this paper, we propose to operationalize this traffic surveillance methodology for practical applications, leveraging the perception and localization sensors already deployed on these vehicles. We present an automatic, vision-based vehicle counting method applied to the video imagery recorded by cameras mounted on transit buses. First, a state-of-the-art 2D deep learning model detects objects frame by frame. Then, detected objects are tracked with the commonly used SORT method. The proposed counting logic converts tracking results to vehicle counts and real-world bird's-eye-view trajectories. Using multiple hours of real-world video imagery obtained from in-service transit buses, we demonstrate that the proposed system can detect and track vehicles, distinguish parked vehicles from traffic participants, and count vehicles bidirectionally. Through an exhaustive ablation study and analysis under various weather conditions, it is shown that the proposed method can achieve high-accuracy vehicle counts. [ABSTRACT FROM AUTHOR]

The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways. [ABSTRACT FROM AUTHOR]

The design of an efficient routing algorithm for the Internet of Vehicles (IoV) poses several problems due to its special characteristics, including nodes high-speed mobility, frequent topology change, link instability, and the presence of radio obstacles. Geographic routing protocols are a promising solution for the IoV, since the main routing parameter used by these protocols is the location information accessible through a location-based service. However, location services and traffic status measurements generate high network overhead. In this paper, we first propose a novel proactive location service called ATLAS that utilizes smart mobile agents to avoid the traffic overhead of traditional services and reduce the location update latency. Then, we present an Agent-Based Proactive Geographic Routing Protocol called ARDENT to route data packets with reduced delay and higher delivery ratio. The proposed systems aim to benefit from the IoV environment and multiagent characteristics to adapt to high-speed mobility, reduce the communication interruptions and the number of control messages overhead generated by the location service, and produce a complete geographic routing protocol suitable for urban settings. The evaluation of ATLAS shows significant performance improvements in terms of query success ratio and location update delay. In addition, the results of ARDENT illustrate its superiority in terms of data packet delivery ratio and end-to-end delay as compared to similar routing protocols. [ABSTRACT FROM AUTHOR]

This paper concentrates on the issue of detecting abnormal flows in distributed monitoring systems, which has many network management applications such as anomaly detection and traffic engineering. Collecting massive network traffic in real‐time remains a large challenge due to the limited system resource. Most existing approaches perform abnormal flow detection at one measurement point, while they cause large computation and memory overhead for recovering abnormal flows. In this paper, we propose a novel data streaming method that supports accurate abnormal flow detection with a low memory requirement. The key idea of our method is that each monitor compresses flow information to summary data structure, sends the generated data structure to the controller; then the controller aggregates the received data structures, recovers candidates of abnormal flows and estimates their size and change to find abnormal flows on the basis of the aggregated data structure. The experimental results based on real network traffic show that the proposed approach can detect up to 97% of abnormal flows with low memory and update requirements in comparison with related approaches. [ABSTRACT FROM AUTHOR]

Bridges generally exhibit complicated mechanical behaviors under external loads, such as flexural-shear coupling, compression-bending coupling, and flexural-shear-torsion coupling. In the context of deterministic design approaches such as design codes, these complicated coupled issues are generally simplified to the safety verification of bridge components under a single mechanical state (i.e. flexural, shear, torsion). Currently, it is available to collect external loads acted on bridges and understand bridge performance under these stochastic external loads. In this manner, the reliability-based full probabilistic approach could be applied to investigate the performance of bridges over their lifetime. However, the majority of existing bridge reliability assessments involving sitespecific traffic load measurements focuses on the analysis of bridge components under a single mechanical state. To address this shortcomings, a reliability-based probabilistic analytical framework is established to investigate the flexural-shear capacity of girder bridges subjected to random traffic loadings. The flexural-shear coupled failure path of bridge girders under random traffic loading is characterized for the first time, where the bivariate extreme value theory is incorporated to develop the extreme value distribution of combined flexural and shear load effects. The modified compression field theory recommended by AASHTO is employed to establish the coupled flexural-shear coupling resistances. Finally, the reliability of the flexural-shear performance of bridge girders is evaluated by solving the multivariate ultimate limit state equation. The proposed analytical framework is applied to a realistic bridge. The results show that the reliability index of the flexural-shear coupling evaluation is lower than that of the flexural or shear evaluation, which highlights the importance of the flexural-shear performance checking in the reliability assessment of bridges under random traffic loading. The proposed analytical framework could be further applied to the probabilistic assessment of bridge components subjected to combined loading mechanisms under random loadings. [ABSTRACT FROM AUTHOR]

In urban areas, efficient parking management is crucial for reducing traffic congestion and environmental impact. This research introduces a new view for making the parking management system that leverages the capabilities of the NVidia Jetson Nano Single Board Computer (SBC) and OpenCV for real-time detection and classification of parking slot occupancy. Unlike traditional systems that rely on intrusive sensors, our proposed solution employs non-intrusive Oriented Fast and Rotated Brief (ORB) key point detection techniques using video feeds. The system architecture integrates video stream processing, ORB via OpenCV, cloud-based data storage, and a Flask server for user notifications. The methodology prioritizes traditional computer vision methods optimized for the Jetson Nano's CUDA cores, offering a computationally efficient alternative to deep learning approaches. Python's versatility and MongoDB's document-based storage are employed for backend development. Our system's performance, evaluated using open datasets, demonstrates high accuracy, precision, recall, and F1 scores, underlining its effectiveness in real-world urban parking scenarios. This study not only presents a robust solution for parking management but also opens avenues for similar applications in traffic measurement and urban planning. [ABSTRACT FROM AUTHOR]

In recent decades, special attention has been given to the adverse effects of traffic congestion. Bike-sharing systems, as a part of the broader category of shared transportation systems, are seen as viable solutions to these problems. Even if the quality of service in bike-sharing service systems were permanently improved, there would still be some issues that needed new and more efficient solutions. One of these refers to the rebalancing operations that follow the bike depletion phenomenon that affects most stations during shorter or longer time periods. Current work develops a two-step method to perform effective rebalancing operations in bike-sharing. The core elements of the method are a fuzzy logic-controlled genetic algorithm for bike station prioritization and an inference mechanism aiming to do the assignment between the stations and trucks. The solution was tested on traffic data collected from the Citi Bike New York bike-sharing system. The proposed method shows overall superior performance compared to other algorithms that are specific to capacitated vehicle routing problems: standard genetic algorithm, ant colony optimization, Tabu search algorithm, and improved performance compared to Harris Hawks optimization for some scenarios. Since the algorithm is independent of past traffic measurements, it applies to any other potential bike-sharing system. [ABSTRACT FROM AUTHOR]

Objective: Noise indicator Lden is used in Health Risk Assessment (HRA) to estimate noise effects using dose-effect relationships. Determination of its value is often impossible due to the unavailability of noise data in the evening. The aim of this paper is to propose estimation methods for noise indicator Lden based on Ldn indicator for road traffic in the conditions of the Czech Republic and to determine the uncertainties of this estimation.Methodology: A total of 217 individual 24-hour road traffic noise measurements were analysed. They were obtained within the framework of the activities of Czech National Institute of Public Health and Public Health Institute Ostrava. The median of calculated values of the difference between noise indicators Lden and Ldn for individual measuring (the coefficient k) is used to estimate indicator Lden according to the formula: Lden ≅ Ldn + k.Results: The coefficient for the whole data set k = 0.6 and subcoefficients k1, k2 and k3 depending on the difference LAeq,16h - LAeq,8h were calculated. The uncertainties of the estimates are expressed by 95% confidence intervals.Conclusion: It is recommended to estimate noise indicator Lden for road traffic by adding coefficient k = 0.6 to the values of indicator Ldn, or alternatively coefficients k1 = 0.5 (for LAeq,16h - LAeq,8h < 5 dB), or k3 = 0.7 (for LAeq,16h - LAeq,8h ≥ 8 dB) can also be used. Coefficient k2 (for LAeq,16h - LAeq,8h between 5 and 7,9 dB) is identical to coefficient k for the whole data set. The estimate can only be used after an evaluation of the local situation if a smooth pattern of sound pressure levels is expected during 24 hours with the decrease of daytime values towards night-time values and without fluctuations due to local impacts. [ABSTRACT FROM AUTHOR]

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host cardinality estimation is the premise of super host identification and it can be used to measure the trust degree of a network. Sketches have been widely used in super host identification. However, expanding network scale and increasing link rate bring challenges to the efficiency and accuracy of sketch-based super host identification. The size of the counters in most sketches requires a trade-off between memory resources and accuracy. To meet the need of measuring high cardinalities, large size counters are applied, which leads to memory waste in monitoring low cardinality hosts. On the other hand, most sketches have high computational overhead when tracking superhosts, resulting in inefficiency. In order to address these issues, we propose a novel memory efficient and reversible sketch, named ExtendedSketch+, to provide accurate host cardinality estimation with the purpose of super host identification and network host trust evaluation. ExtendedSketch+ achieves both high memory utilization efficiency and high accuracy, in addition to high super host identification efficiency. It applies extensible counters to record unbalanced host cardinality distribution, by adaptively expanding the counters with the increase of cardinality. It adopts a lossless traffic information transfer strategy during counter extension to ensure the accuracy of cardinality estimation. It can directly tracks the host with the highest cardinality in each bucket, which greatly improves the identification efficiency of super hosts. Based on accurate cardinality estimation of ExtendedSketch+, we can further accurately evaluate host trust degree. We theoretically analyze ExtendedSketch+ with regard to its space and time complexities and estimation accuracy. We conduct performance evaluation based on real network traffic traces. Experimental results show that ExtendedSketch+ performs better than state-of-the-art sketches regarding super host identification and provides accurate host trust evaluation. • A memory efficient and reversible sketch named ExtendedSketch+. • A quick and accurate super host identification method based on ExtendedSketch+. • An accurate network host trust evaluation method based on ExtendedSketch+. • A detailed theoretical analysis on ExtendedSketch+ on complexities and accuracy. • A performance comparison to show the superiority of ExtendedSketch+. [ABSTRACT FROM AUTHOR]

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

Analysis of traffic flow signals plays an important role in traffic prediction and management. As an intrinsic property, the singular point of a traffic flow signal labels a new nonsteady status. Therefore, detecting the singular point is an effective approach to determine the moment of traffic flow prediction. In this paper, an improved wavelet transform is proposed to detect singular points of real-time traffic flow signals. The number of detected singular points is output via the heuristic selection of multiple scales. Then, a weighted similarity measurement of historical traffic flow signals is utilized to predict the next singular point. The position of the next singular point decides the duration of prediction adaptively. The detected and predicted singular points are applied to dynamically update the unequal interval prediction of traffic flow. Furthermore, a Vasicek model is used to predict the traffic flow by minimizing the sum of the relative mean standard error (RMSE) between the traffic flow increment in the predicted interval and the sampled increments of previous intervals. A decomposition method is used to solve the unequal matrix problem. Based on the scenario and traffic flow imported from the real-world map, the simulation results show that the proposed algorithm outperforms existing approaches with high prediction accuracy and much lower computing cost. [ABSTRACT FROM AUTHOR]

With the emergence of new internet traffic types in modern transport networks, it has become critical for service providers to understand the structure of that traffic and predict peaks of that load for planning infrastructure expansion. Several studies have investigated traffic parameters for Virtual Reality (VR) applications. Still, most of them test only a partial range of user activities during a limited time interval. This work creates a dataset of captures from a broader spectrum of VR activities performed with a Meta Quest 2 headset, with the duration of each real residential user session recorded for at least half an hour. Newly collected data helped show that some gaming VR traffic activities have a high share of uplink traffic and require symmetric user links. Also, we have figured out that the gaming phase of the overall gameplay is more sensitive to the channel resources reduction than the higher bitrate game launch phase. Hence, we recommend it as a source of traffic distribution for channel sizing model creation. From the gaming phase, capture intervals of more than 100 s contain the most representative information for modeling activity. Dataset:  https://doi.org/10.6084/m9.figshare.22191160. [ABSTRACT FROM AUTHOR]

The main aim of the research was to diagnose drivers' behavior in urban road traffic. It was preceded by careful observation and testing of drivers' behavior in terms of the violations assumed to be observed. It was taken into account, inter alia, aspects such as failure to comply with road signs and signals, behavior at the rail-road crossing, or offenses occurring in the pedestrian-driver relationship. During the observations, the assumption was to capture negative behaviors of drivers, but each time attention was also paid to positive behaviors related to road culture. The analysis of the results allowed the researchers to identify the most common mistakes made by drivers. This can help not only to better enforce regulations at selected measurement sites but also lead to improved education about these specific violations. [ABSTRACT FROM AUTHOR]

Identifying traffic congestion accurately is crucial for improving the expressway service level. Because the distributions of microscopic traffic quantities are highly sensitive to slight changes, the traffic congestion measurement is affected by many factors. As an essential part of the expressway, service areas should be considered when measuring the traffic state. Although existing studies pay increasing attention to service areas, the impact caused by service areas is hard to measure for evaluating traffic congestion events. By merging ETC transaction datasets and service area entrance data, this work proposes a traffic congestion measurement with the influence of expressway service areas. In this model, the traffic congestion with the influence of service areas is corrected by three modules: 1) the pause rate prediction module; 2) the fitting module for the relationship between effect and pause rate; 3) the measurement module with correction terms. Extensive experiments were conducted on the real dataset of the Fujian Expressway, and the results show that the proposed method can be applied to measure the effect caused by service areas in the absence of service area entry data. The model can also provide references for other traffic indicator measurements under the effect of the service area. [ABSTRACT FROM AUTHOR]

On 20 and 22 August 2019, a small tripod was outfitted with a sonic anemometer and placed in a highway shoulder to compare with measurements made on an instrumented car as it traveled past the tripod. The rural measurement site in this investigation was selected so that the instrumented car traveled past many upwind surface obstructions and experienced the occasional passing vehicle. To obtain an accurate mean wind speed and mean wind direction on a moving car, it is necessary to correct for flow distortion and remove the vehicle speed from the measured velocity component parallel to vehicle motion (for straight-line motion). In this study, the velocity variances and turbulent fluxes measured by the car are calculated using two approaches: (1) eddy covariance and (2) wavelet analysis. The results show that wavelet analysis can better resolve low frequency contributions, and this leads to a reduction in the horizontal velocity variances measured on the car, giving a better estimate for some measurement averages when compared to the tripod. A wavelet-based approach to remove the effects of sporadic passing traffic is developed and applied to a measurement period during which a heavy-duty truck passes in the opposite highway lane; removing the times with traffic in this measurement period gives a reduction of approximately 10 % in the turbulent kinetic energy. The vertical velocity variance and vertical turbulent heat flux measured on the car are biased low compared to the tripod. This low bias may be related to a mismatch in the flux footprint of the car versus the tripod or perhaps to rapid flow distortion at the measurement location on the car. When random measurement uncertainty is considered, the vertical momentum flux is found to be consistent with the tripod in the 95 % confidence interval and statistically different than 0 for most measurement periods. [ABSTRACT FROM AUTHOR]

Featured Application: Optimizing urban traffic/logistics; estimating pollution due to combustion engines; studying the relation between urban pollution and respiratory/cardiovascular problems. Traffic models have gained much popularity in recent years, in the context of smart cities and urban planning, as well as environmental and health research. With the development of Machine Learning (ML) and Artificial Intelligence (AI) some limitations imposed by the traditional analytical, numerical and statistical methods have been overcome. The present paper shows a case study of traffic modeling with scarce reliable data. The approach we propose resorts on the advantages of ensemble learning using a large number of related features such as road and street categories, population density, functional analysis, space syntax, previous traffic measurements and models, etc. We use advanced regression models such as Random Forest, XGBoost, CatBoost etc., ranked according to the chosen evaluation metrics and stacked in a weighted ensemble for optimal fitting. After a series of consecutive data imputations we estimate the annual average daily traffic distribution in the street and road network of Sofia city and the metropolitan municipality for 2018 and 2022, and the NO 2 levels for 2021 with accuracy resp. 78 % , 74 % and 92 % , using AutoGluon and Scikit-Learn. [ABSTRACT FROM AUTHOR]

This paper presents research on the collection, analysis, and evaluation of the fundamental data needed for road traffic systems. The basis for the research, analysis, planning and projections for traffic systems are traffic counts and data collection related to traffic volume and type. The quality and accuracy of this data are very important for traffic planning or optimization. Therefore, the purpose of this research is to apply advanced methods of automatic counting of motorized traffic and to evaluate the impact of this data on the measurement of important traffic indicators. The accuracy of measurements arising from the traditional method of data collection through manual counting will be compared with the most advanced methods of automatic counting through cameras. For this purpose, an analytical algorithm for the recognition and processing of data related to road users as a function of the time of day was applied. The program was written in the programming language Python, and the accuracy of the data and its effect on the results of qualitative traffic indicators were analyzed using the Synchro software model. The developed program is capable of recognizing and classifying different types of vehicles in traffic, such as motorbikes, motorcycles, cars, pick-ups, trucks, vans and buses, as well as counting the traffic volume over time. The results obtained from these two models show the advantages of applying advanced methods of data collection and processing related to dynamic traffic processes, as well as the quality in terms of the impact on the measurement of qualitative traffic indicators. A comparison of the quality of results for the different time intervals and varying levels of visibility in traffic is presented using tables and graphs. At nighttime, when visibility was poor, the discrepancy between the manual and automatic counting methods was around 9.5%. However, when visibility was good, the difference between manual counting and the automated program was 4.87% for the period 19:00–19:15 and 3.64% for the period 05:00–05:15. This discrepancy was especially noticeable when distinguishing between vehicle categories, due to the limitations in the accuracy in recognizing and measuring the dimensions of these vehicles. The difference between the two calculation models has a minor effect on qualitative traffic indicators such as: approach LOS, progression factor, v/s, v/c, clearance time, lane group flow, adj. flow, satd, and flow approach delay. [ABSTRACT FROM AUTHOR]

Network tomography aims at estimating source–destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for Poisson traffic over networks operating under deterministic as well as random routing regimes. In this article, we expand Vardi's second‐order moment matching rate estimation approach to higher‐order cumulant matching with the goal of increasing the column rank of the mapping and consequently improving the rate estimation accuracy. We develop a systematic set of linear cumulant matching equations and express them compactly in terms of the Khatri–Rao product. Both least squares estimation and iterative minimum I‐divergence estimation are considered. We develop an upper bound on the mean squared error (MSE) in least squares rate estimation from empirical cumulants. We demonstrate that supplementing Vardi's approach with the third‐order empirical cumulant reduces its minimum averaged normalized MSE in rate estimation by almost 20% when iterative minimum I‐divergence estimation was used. [ABSTRACT FROM AUTHOR]

The efficacy of the application of traffic models depends on a successful process of model calibration. Microsimulation models have a significant number of input parameters that can be optimized in the calibration process. This paper presents the optimization of input parameters that are difficult to measure or unmeasurable in real traffic conditions and includes parameters of the driver's behavior and parameters of Wiedemann's psychophysical car-following model. Using neural networks, models were generated for predicting travel time and queue parameters and were used in the model calibration procedure. This paper presents the results of a comparison of five different applications of neural networks in calibrating the microsimulation model. The VISSIM microsimulation traffic model was selected for calibration and field measurements were carried out on two roundabouts in a local urban transport network. The applicability of neural networks in the process of calibrating the microsimulation models was confirmed by comparison of the modelled and measured data of traffic indicators in real traffic conditions. Methods of calibration were validated with two sets of new measured data at the same intersection where the calibration of the model was carried out. The third validation was made at the intersection in a different location. The selection of the optimal calibration methodology is based on the model accuracy between the simulated and measured data of traveling time, as well as queue parameters. The microsimulation model provides access to the raw data of observed traffic parameters for each vehicle in the simulation. The dataset of the calibrated model simulation results of all travel times of the selected traffic flow was compared with the dataset of the measured field data to determine whether the data are statistically significantly different or not. [ABSTRACT FROM AUTHOR]

Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning. [ABSTRACT FROM AUTHOR]

Traffic accidents in mountainous highway tunnels have resulted in significant negative effects and losses. Among the potential hazards that can lead to fatal injuries, human-related hazards have been recognized as the leading cause. Determining the risk management effectively and prioritizing unsafe human behavior are the basis for preventing and controlling traffic accidents in mountainous highway tunnels. Therefore, hazards that could potentially cause highway tunnel traffic accidents were identified by using a tail-biting fish diagram combined with the fault tree method. A risk assessment model was constructed based on probability, degree of importance, and loss. Furthermore, the probability can be calculated by assessing the degree of unreliability, which can be obtained by assessing the degree of importance of unsafe behavior in the fault tree, and the loss can be acquired from the authority. The case of 8–10 traffic accidents that occurred in the Qinling No. 1 Tunnel of the Ankang section of the Beijing-Kunming expressway was studied, and the values of the unsafe behaviors were assessed. According to the risk values, the priority for controlling unsafe behaviors can be acquired and tailored measures can be taken to prevent and control the risks, which provides a theoretical basis and new method for the effective control of mountainous highway tunnel traffic accidents. [ABSTRACT FROM AUTHOR]

Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits. [ABSTRACT FROM AUTHOR]

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand. [ABSTRACT FROM AUTHOR]

To improve the specific quality of service, internal network management and security analysis in the future mobile network, accurate flow-awareness in the global network through packet sampling has been a viable solution. However, the current traffic measurement method with the five tuples cannot recognize the deep information of flows, and the Deep Packet Inspection (DPI) deployed at the gateways or access points is lack of traffic going through the internal nodes(e.g., base station, edge server). In this paper, by means of Deep Q-Network (DQN) and Software-Defined Networking (SDN) technique, we propose a flow-level sampling framework for edge devices in the Mobile Edge Computing (MEC) system. In the framework, an original learning-based sampling strategy considering the iterative influences of nodes is used for maximizing the long-term sampling accuracy of both mice and elephant flows. We present an approach to effectively collect traffic packets generated from base stations and edge servers in two steps: 1) adaptive node selection, and 2) dynamic sampling duration allocation by Deep Q-Learning. The results show that the approach can improve the sampling accuracy, especially for mice flows. [ABSTRACT FROM AUTHOR]

The article deals with a deformation analysis of a road embankment under different types of static and cyclic loading. A typical cross-section of a road embankment supported by geogrid retaining structures is considered. In the case of a design, the load is usually applied as a distributed static load. The analysis presented in the paper includes all the most loading methods used. Because there are only a few measurements for these types of construction in a given region, the analysis also includes a cyclic loading that aims to simulate real traffic loading. The cyclic loading considered the weights of cars and trucks and the distances between them, based on traffic intensity measurements. The study results showed how significant the differences in vertical and horizontal deformations of an embankment are when the different types and methods of static loading are used in a design. The results of the cyclic loading showed the deformations that can be expected from current real traffic. [ABSTRACT FROM AUTHOR]

Field measurement campaigns with traffic participants using wearable sensors and questionnaires can be challenging to carry out because of inconsistent interfaces across manufacturers for accessing sensor data and campaign-specific questionnaire contents bear large potential for errors. We present an app able to consolidate data from multiple technical sensors and questionnaires. The functionality includes providing feedback for correct sensor platform mounting, accessing and storing all sensor and questionnaire data in a uniform data structure. To do this, the app implements a sensor data bus class that unifies data from technical sensors and questionnaires. The app can also be extended to accommodate other sensor platforms provided they have a suitable API. We also describe a database structure holding the data from multiple campaigns and test subjects in a privacy preserving fashion. Finally, we identify some potential issues and hints that practitioners may encounter when conducting a measurement campaign. [ABSTRACT FROM AUTHOR]

Summary: In smart cities, a large number of vehicles are connected into an intelligent transportation system and share information via the vehicular communication network (VCN). Accurate, fine‐grained, and comprehensive traffic measurements are very crucial for the controller's decision making in software‐defined networking (SDN) in VCN. Fine‐grained traffic measurements can accurately portray network behaviors for VCN. However, this will increase a large amount of measurement overhead. Therefore, how to effectively obtain accurate and fine‐grained traffic is a huge challenge for VCN. To the end, this paper proposes a novel accurate and SDN‐based fine‐grained traffic measurement approach to obtain comprehensive traffic in VCN. Firstly, based on SDN architecture, we exploit the pull‐based sampling mechanism to quickly obtain coarse‐grained traffic measurement values. Secondly, based on the matrix completion theory, we use both interpolation and optimization methods to obtain fine‐grained traffic measurements. Thirdly, the optimization model and detailed algorithm are proposed to attain accurate traffic. Finally, we conduct a larger number of simulations to validate the measurement approach proposed in this paper. Simulation results show that our approach exhibits better performance and is promising. [ABSTRACT FROM AUTHOR]

Simulation models of transport systems are a key tool for solving many problems in the field of management of these systems. The methodologies for creating such models use datasets on both transport infrastructure and demand for the delivery of goods or passenger transport, however, many factors are considered based on assumptions due to the complexity. This article describes the approach to modeling the cargo transportation system for road transport in Poland based on data obtained by the Central Statistical Office from the TD-E survey. This approach avoids many assumptions about demand as the demand parameters are estimated based on a sample representing the general population—a set of all economic entities generating freight traffic. Basic procedures in the developed approach have been implemented as Python scripts. As a result of the use of the proposed methodology, a country-wide road transport model was obtained based on the TD-E survey from 2018. The adequacy of the developed model was assessed based on the results of the General Traffic Measurement from 2015. The obtained model is of satisfactory quality (the coefficient of determination equals 0.62), which can be improved after calibrating the space resistance functions and improving the traffic distribution procedure. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for bus lane allocation including different strategies, dynamic bus lanes, and exclusive bus lanes. Choosing the right solution depends on many factors, such as traffic flow, passenger flow, and time losses. Analytical or simulation models can be used to evaluate the effectiveness of a separate bus lane. Analytical methods are simple to use and provide results in a short time. Simulation models, unlike analytical ones, require much more time and data to prepare but they are also much more detailed and accurate data. Therefore, analytical models may be particularly needed in the first stage of planning work during which potential sections for separated bus lanes are indicated. In this article, the author proposed an analytical model based on the 6th edition of the Highway Capacity Manual, which can be used to assess the implementation of separated bus lanes in different strategies. The final model developed was calibrated using traffic measurement results collected in a Polish city. As a result of the work, the author proposed the calculation procedure of the assumptions and diagrams, enabling the assessment of the selection of the appropriate solution. [ABSTRACT FROM AUTHOR]

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression. [ABSTRACT FROM AUTHOR]

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions. • Main traffic measurement tasks and advantages of applying sketches. • Criteria to evaluate sketch-based network traffic measurement methods. • Serious review on the state-of-art sketch-based traffic measurement. • Significant open issues and promising future research directions. [ABSTRACT FROM AUTHOR]

The accuracy of stress estimates in Orthotropic Bridge Decks (OBD) may be negatively impacted by the complex load transfer and asphalt properties. Yet, accurate stress estimates are crucial for optimal fatigue verifications. A field measurement and modelling study has been conducted to evaluate the accuracy of finite element (FE) models. The level of detail of the FE model resembled engineering practice. Measurements comprised of strains caused by normal flowing traffic and by single vehicles with known load. The study gave insight into the distribution of the transverse vehicle location, dynamic vehicle–bridge deck interaction, and the asphalt influence. Comparing with international guidelines, the prediction error of FE models of OBDs is on the edge of acceptable. • Vehicle-induced strains are measured in an Orthotropic Bridge Deck (OBD). • Single vehicle tests and flowing traffic measurements are performed. • Data give insight in distribution of transverse location and asphalt influence. • Dynamic amplification appears a random factor with limited effect on fatigue. • Prediction errors of finite element models of OBDs are on the edge of acceptable. [ABSTRACT FROM AUTHOR]

Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage. [ABSTRACT FROM AUTHOR]

Annoyance and sleep disturbance are the most common community responses to road traffic noise, and they account for the majority of the environmental noise burden in Europe. The findings of a cross-sectional study - a questionnaire survey of residents living in newly built apartment houses in some Slovak towns (Bratislava, Trencin, Martin, Piestany) are presented in this paper. The average age of the 543 respondents was 45 ± 4 years, 53 % were females, and 81 % had lived in their current home for more than five years. Traffic noise measurements on noisy facades LAeq,day = 51.8 dB to 72.4 dB, LAeq,night = 41.9 dB to 64 dB were supplemented by a standardized questionnaire. In comparison to respondents with bedroom windows facing quiet streets, those who face noisy streets are less satisfied with their quality of life, less sensitive to noise, and more irritable and restless during the day and night OR = 2.54; 95 % CI = 1.38-4.92. After the results have become completed, we hope to suggest temporary solutions for noisy apartment building facades, as well as intervention techniques, and to apply sound city planning principles. [ABSTRACT FROM AUTHOR]

The paper considers the main ways of describing the process that characterizes the arrival of packets to a multiservice node of a telecommunications network. The features of the process under consideration are best represented by the cumulative distribution function A(t). It determines the distribution of the interval size between the moments of arrival of neighboring packets to the multiservice node. These intervals are random values. If it is not possible to perform measurements that allow the choosing of the A(t) function, then the distribution law of random variables is selected based on reasonable assumptions. For telephone switching nodes, the Poisson flow hypothesis was used, which is often similar to the symmetric distribution of the number of calls at time interval t. The results of traffic measurements for multiservice switching nodes showed that the studied distribution is inherently asymmetric. This paper mainly considers the possibility of choosing the A(t) function based on the measurement results presented in a form of the histogram a(t), which contains a series of values. This histogram allows us to obtain the desired distribution as a stepwise function by integration of the a(t). Practical interest is associated with the possibility of reducing the number of readings used to assess the A(t) function. The methods used by some authors are based on the application of arbitrarily chosen functions A(t) with so-called heavy tails. The proposed approach is based on real distributions defined at a finite time interval. As a result of this research, a methodology has been developed to accurately describe the process of packet arrival at the input of the multiservice node. The proposed methodology is based on analytical methods. It guarantees error minimization when investigating the probabilistic characteristics of a switching node in a multiservice network. [ABSTRACT FROM AUTHOR]

Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people's quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%). [ABSTRACT FROM AUTHOR]

Nowadays, many applications need varying levels of Quality of Service (QoS). The network that provides the communication service connects the servers and clients. The network traffic which is routed through the network should be engineered. Traffic Engineering (TE) is a mechanism for transferring the packets considering the different QoS level requirements among applications. The optimal resource allocation is the primary strategy for TE so that the network can provide the QoS requirements for each application. The TE can improve network efficiency, performance, and user satisfaction. Software Defined Network (SDN) has been proposed as the novel network architecture that could make networks agile, manageable, and programmable using control and data plane separating compared to traditional network architecture. In this paper, we survey network traffic engineering in SDN. We investigate and cluster the articles published between 2017 and 2022 on traffic engineering in SDN. The state-of-the-art articles about the traffic engineering mechanisms in SDN have been examined and classified into four types: topology discovery, traffic measurement, traffic load balancing, QoS, and dependability. Finally, the cutting-edge issues and challenges are discussed for future research in SDN-based TE. [ABSTRACT FROM AUTHOR]

• The ability of ISS and NISS in predicting multiple injuries to a rider is compared. • Discriminatory ability of ISS and NISS in predicting clinical outcomes is examined. • Compared with the NISS, the ISS underestimated the actual injuries. • ISS and NISS are correlated with higher odds of mortality and trauma risk. • NISS exhibits a better discriminatory ability in predicting clinical outcomes. Introduction: Little evidence exists in the literature regarding the discrimination power of better anatomical injury measures in differentiating clinical outcomes in motorcycle crashes. Furthermore, multiple injuries to different body parts of the rider are seldom analyzed. This study focuses on comparing anatomical injury measures such as the injury severity score (ISS) and the new injury severity score (NISS) in capturing injuries of multiple injured riders and examining the discriminatory capabilities of the ISS and NISS in predicting clinical outcomes post motorcycle crash. Methods: The study harnessed unique and comprehensive injury data on 322 riders from the US DOT Federal Highway Administration's Motorcycle Crash Causation Study (MCCS). Detailed exploratory analysis is performed and discrete/ordered statistical models are estimated for three clinical outcomes: mortality risk, trauma risk, and trauma status. Results: Around 9% of the riders died and 45% of the riders had injuries. Around 36% of the riders were hospitalized, disabled, or institutionalized. While a very strong dependence was found between ISS and NISS, ISS underestimated injuries sustained by riders. Statistical models for mortality risk revealed that a unit increase in the ISS and NISS was correlated with a 1.18 and 1.17 times increase in the odds of mortality, respectively. Moreover, a unit increase in ISS and NISS values was correlated with a higher trauma risk by 1.48 and 1.36 times, respectively. Our analysis reveals that the probability of a rider being hospitalized or disabled/institutionalized increases with an increase in the NISS. Conclusions and practical applications: The NISS exhibits significantly better calibration and discriminatory ability in differentiating survivors and non-survivors and in predicting trauma status – underscoring the importance of accounting for microscopic body-part-level injury data in motorcycle crashes. We consider that compared with the KABCO scale, the ISS and NISS are more nuanced scores that can better measure the overall injury intensity and can lead to more targeted countermeasures. [ABSTRACT FROM AUTHOR]

Saturation flow estimation is a challenge in non-lane based mixed traffic streams due to the presence of mixed vehicle-types, each having different static and dynamic characteristics. Such a mix gives way to traffic behaviours like weak lane discipline, multiple-leader following, etc. Due to these behaviours, queue discharge flow during green time fluctuates, which makes it difficult to identify a prolonged steady period of queue discharge required to estimate saturation flow. The present study is based on the hypothesis that the Passenger Car Unit (PCU) can explain the fluctuations in discharge caused by mixed vehicle-types. Accordingly, this study proposes a new model to estimate saturation flow along with PCUs from field data. Mathematical properties of the model are studied before field applications. Field data collected from six different locations are used to estimate the saturation flow and PCUs. The results indicate that the model can capture complex interactions of various vehicle-types. [ABSTRACT FROM AUTHOR]

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection. • We show the utility of using adjacent traffic sensor measurements to reduce TTDA. • XGBoost classifier produces the best results as quickly as 1.0 min. • XGBoost classifier produces the best results (AUC-ROC 83% and AUC-PR 49%). • We show that features of both upstream and downstream sensors are the most important. • We report results on two different datasets (i.e., I-75 and I-24) in Chattanooga, TN. [ABSTRACT FROM AUTHOR]

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety. • Building on an in-depth exploration of the intrinsic attributes of ships, this paper introduces ship domain (SD) into the calculation of proximity factor within the ship traffic complexity measurement model. By constructing different SDs for different ships, the model can more accurately assess collision risks. • Incorporating the mitigation index, based on ships' speed and course parameters, into the ship traffic complexity measurement model enables the quantification of ships' movement trends within specified temporal and spatial domains. This integration allows the model to accurately assess the mitigation complexity of ships in various MTS, taking into account their dynamics. • Expanding the ship traffic complexity measurement model from focusing on pairs of ships to encompassing the quantification of traffic complexity in multi-ship interactive scenarios has significantly enhanced the model's practical application value. [ABSTRACT FROM AUTHOR]

Sampling is widely used to handle the mismatch between the line rate and the processing throughput of a traffic measurement module. Spread sampling, which only samples the elements in each flow when they appear for the first time and ignores them for subsequent appearances, is required for flow‐spread measurement. The previous works on spread sampling suffer from poor accuracy and slow speed. In this paper, the authors proposed an augmented two‐phase method with fast speed and low error to achieve spread sampling in that each distinct element is sampled with a predefined probability only at the first occurrence. The experiment results based on real Internet traffic traces show that our method achieves a smaller sampling error and a high line rate. [ABSTRACT FROM AUTHOR]

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 and 2019. We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures. • We assessed traffic congestion in an environmental justice community over 2 years. • We analyzed time series of crowd-sensed traffic maps. • Traffic increased in the South Bronx in New York City between 2017 and 2019. • The local community will use the data to advocate for mitigating measures. • Crowd-sensed traffic data allow assessing community impacts of traffic globally. [ABSTRACT FROM AUTHOR]