Traffic Measurements Research Articles

Estimation of population exposed to road traffic noise in Kosovo highway using GIS modelling and roadside measurements

New highways have been introduced into Kosovo landscape in recent years. The trajectory of the highways crosses through the fields and in the middle of some previously remote rural settlements. This has changed the environment for these areas and the environmental noise is a major concern. This study aims to estimate the population exposed to high traffic noise levels around the highway routes in Kosovo. GIS has been used to model the highway lines, the terrain and the buildings on site. Roadside traffic noise measurements as well as traffic flow measurements have been conducted to approximate the noise sources. An approximate noise map has been generated for the studied area. The population exposed to daytime and nighttime road traffic noise were estimated. The results show the need for road traffic noise reduction measures and areas where they are most needed.

Continuous Health Monitoring of Reinforced Concrete Bridge Deck based on Traffic Load-induced Acoustic Emission

This paper presents continuous bridge deck monitoring based on b-value obtained from traffic load-induced acoustic emission (AE) signals. Using the self powered AE system that we developed, we have observed traffic load-induced AE on an actual bridge for five years. The developed self-powered AE system demonstrated that long-term traffic load-induced AE measurements over five years could be performed without an external power source. Traffic load-induced AEs measured irregularly by event-driven measurement were accumulated, and b-values were calculated. We used an additive regression model that accounts for piecewise linear trends and seasonality to extract long-term trends accurately. Over the past five years, the bridge has deteriorated. In addition, repairs were made during the period. As a result of this monitoring, we determined the relationship between the damage condition of the bridge and the b-value trend obtained from the traffic load-induced AEs. The results confirmed that the b-value based on traffic load-induced AE corresponds well with the damage conditions on the underside of the deck slab or in a shallow area inside it.

Per-Packet Traffic Measurement in Storage, Computation and Bandwidth Limited Data Plane

Per-Packet Traffic Measurement in Storage, Computation and Bandwidth Limited Data Plane

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Research on the traffic flow measurement method of intelligent connected vehicles based on series and cascade technology

At present, the major cities in our country have entered the development of smart cities, the utilization rate and prevalence rate of intelligent connected cars in smart cities have reached a new peak,to manage the real-time traffic flow of intelligent connected vehicles effectively in smart cities. This paper proposes to calculate the flow of intelligent connected vehicles based on series cascade technology. Firstly, this paper collects multi-source heterogeneous data based on multiple collectors; Secondly, a generative adversarial network is employed for data fusion, synthetic data, closely mirroring the original, is generated for handling unknown variables during model training. Next, an autoencoder model generates input data for feature representation. Lastly, CatBoost, a regression model that leverages a cascaded machine learning algorithm, is utilized to forecast the traffic flow of intelligent connected vehicles in smart cities. The results show that the method based on CASCaded machine learning has remarkable effect in predicting smart city traffic flow and alleviating urban road congestion.

MpScope: Enabling multi-pipeline monitoring inside a switch

The core of programmable switches is the flexible data plane, composed of multiple programmable pipelines in existing programmable switches. These pipelines are isolated from each other and cannot share state and data. However, most of network monitoring systems ignore this condition and implicitly assume that the switch has only a single pipeline. This results in an inaccurate measurement and high communication overhead with the practical switch. To tackle this problem, we propose MpScope, a general multi-pipeline monitoring framework, which centers around the control plane, supporting accurate and efficient network monitoring. Specifically, MpScope combines the switch’s data plane and control plane to achieve comprehensive network monitoring of the whole switch scope. The control plane aggregates the statistical results from multiple pipelines and tunes the monitoring module residing in the different pipelines in the data plane dynamically. The data plane is responsible for real-time traffic measurement and statistic reports. Its behaviors can be adjusted periodically with the instructions from the control plane. Two typical monitoring applications, i.e., heavy hitter detection and distinct counting, are developed with MpScope to validate the effectiveness of multi-pipeline monitoring. Experiments show that MpScope significantly reduces communication overhead compared to the static threshold scheme while maintaining high detection accuracy over time.

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Predicting long-term air pollutant concentrations through deep learning-based integration of heterogeneous urban data

Accurate prediction of air pollutant concentrations, specifically concerning inhalable particulate matter such as PM2.5, is crucial for proactive measures to safeguard the well-being of urban residents. This paper focuses on addressing the perceptible latency effect for long-term PM2.5 predictions produced by existing statistical models. We emphasize the importance of numerical computations in capturing substantial changes, and enhance prediction accuracy by integrating them with high-dimensional, diverse urban data. Specifically, our approach collects data from a global-to-meso-scale atmospheric dispersion model named System for Integrated modeLling of Atmospheric coMposition (SILAM), along with numerical weather forecasts, traffic congestion measurement, meteorological factors and static sources (road network and points of interest). We find that existing deep learning models are prone to overfitting when applied to complex datasets, primarily due to their uniform treatment of diverse data types as time series without adapting to the specific characteristics of each data type. To counter this, we propose a simple yet transferable deep learning architecture, focusing on the proper use of various data types. Additionally, our comparative analysis, through a case study in Shenzhen, China, shows our model not only enhances SILAM dispersion accuracy for 24h-ahead PM2.5 forecasts by a significant 30.3%, but also mitigates the noticeable latency effect of existing models by 19.5%. Finally, an ablation study further validates the importance of each data source and module of our approach.

SDNRoute: Proactive routing optimization in Software Defined Networks

Despite opening attractive perspectives, the concept of Software Defined Networking raises doubts about the performance and practical feasibility. To contradict these concerns, we propose a deployment-ready system aimed at proactive and periodic optimization of flow paths. The modular system consists of modules responsible for traffic prediction, static optimization, measurements, flow management, and validation of optimization results. To make the system efficient, we resolved several scientific issues and proposed novel and valuable solutions, for example methods for efficient proactive flow management and periodic re-optimization of routing policies. Simultaneously, to make the system production-ready and create a reliable research environment, we provide solutions to several technical obstacles.We validate the proposed system with three network topologies, each with three load levels, following real-life traffic models. We consider Equal-Cost Multi-Path Routing as a baseline. The results indicate that the system allows network operators to handle more traffic (packet loss reduced by up to 30%), improve quality of service (less congested links resulted in even 2.5 times lower latency), and reduce operational expenses (energy consumption lowered by up to 10%).

Calibration of Microscopic Traffic Simulation in an Urban Environment Using GPS-Data

Accurate traffic models are of decisive importance for well-founded traffic engineering and represent the basic framework for comprehensive simulation studies as modelling of traffic demand. Using traffic count and speed measurements of road segments is a common approach for the calibration of a realistic traffic simulation although the data acquisition process can be at very extensive costs. From an academical point of view, there have been many studies addressing the problem of calibration. In this respect, the microscopic simulation software SUMO offers the usage of the tools flowrouter and routesampler for generating network simulations on the base of traffic count measurements. In this paper, we propose a robust method for the calibration of microscopic traffic simulations by using vehicle count and speed measurements from collected GPS-data. The developed approach is a two-step optimization process: The application of integer linear programming (ILP) as a priori optimization is followed by adopting an evolutionary algorithm for minimizing the a posteriori deviation between real and simulated traffic data. As a proof of concept, the proposed method is tested in a subnet-work model of the inner city of Friedrichshafen and compared with the ready-to-use tools from SUMO. The suggested method indicates a promising correlation between simulated and real traffic data showing better calibration results in comparison to the aforementioned functions SUMO provides. Since the approach is network-independent, it also offers the possibility of large-scale traffic calibration.

Assessing wayside traffic noise levels using long-term noise monitoring and available Department of Transportation traffic data - worst-case noise traffic conditions, effects of repaving, and methods for comparing traffic noise data sets

The availability of automated continuous traffic data from State Departments of Transportation (DOTs), such as the California Department of Transportation (Caltrans) Performance Measurement System (PeMS), provides increased opportunities for the analysis of highway noise. Traffic data that is free to access and downloadable via the internet can be matched with unattended long-term noise measurement data to provide large datasets for analysis, without requiring field staff to be continuously on site. A 2018 ICF/Caltrans paper documented how this approach was used on an operating freeway to determine the volume of traffic that results in the highest one-hour energy average traffic noise level. At the time of those measurements, the subject freeway had Portland cement concrete pavement. The freeway has since been repaved with an open-graded rubberized asphalt concrete pavement overlay. The current study uses new (2023) traffic noise measurements and PeMS data to investigate how worst-noise conditions have changed and the effect of the repaving on wayside noise levels. Changes in traffic patterns since 2018 required the authors to normalize the measurement data for comparison. Opportunities were identified to take advantage of the large measurement data sets to match before-and-after data, and those methods are also described in this paper.

Suggestions and Comparisons of Two Algorithms for the Simplification of Bluetooth Sensor Data in Traffic Cordons.

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin-destination (i-j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements.

Learning the Appropriate Local Averaging Length for Traffic Speed Deflectometer Deflection Velocity Measurements: Application to the Analysis of Rigid Pavement Joint Responses

Learning the Appropriate Local Averaging Length for Traffic Speed Deflectometer Deflection Velocity Measurements: Application to the Analysis of Rigid Pavement Joint Responses

Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization-Variational Mode Decomposition Method.

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

Enhancing road traffic flow prediction with improved deep learning using wavelet transforms

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing traffic management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits of wavelet-based denoising and Recurrent Neural Networks (RNNs). This integrated methodology is introduced to effectively capture the inherent nonlinearity and temporal dependencies in time series traffic data. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are introduced to address the challenges associated with accurately forecasting traffic flow. To enhance prediction quality, traffic flow data is preprocessed using exponential smoothing and wavelet-based filtering as denoising filters, effectively eliminating outliers. The effectiveness of the proposed techniques is evaluated using traffic measurements collected from diverse highway locations across California, including the Old Bayshore highway, situated south of Interstate 880 (I880), and the Ashby Ave highway, positioned west of Interstate 80 (I80) in the San Francisco Bay Area. The results obtained through integrating both architectures, including LSTM and GRU, within the wavelet transform-based filter demonstrate an enhancement in forecasting performance. Specifically, LSTM with wavelet-based denoising using Symlet and Haar wavelets achieved high prediction performance with an average R2 of 0.982 and 0.9811, respectively.

N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control

The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.

Traffic noise measurement, mapping, and modeling using soft computing techniques for mid-sized smart Indian city

The present study presents an investigation into the utilization of artificial neural networks (ANN) and multiple linear regression model (MLR) for the prediction of traffic noise levels in various locations of Dhanbad city at varying intervals. Traffic noise indices measurements were carried out using sound pressure level meter (SLM). The prediction of equivalent A-weighted sound level (LAeq) and the sound level exceeding 10 percent of the time (L10) is carried out using various influencing factors such as number of cars, number of 2 wheelers, number of 3 wheelers, number of heavy vehicle, number of medium commercial vehicles, and traffic speed. The findings demonstrate the ANN model's proficiency in comparison of MLR model for providing precise prediction accuracy of traffic noise indices with a R2 of 0.94 for LAeq and 0.91 for L10. Furthermore, the frequency spectrum analysis reveals that high peaks were observed at lower frequencies ranging from 31.5 Hz to 50 Hz, middle frequencies from 500 to 800 Hz and higher frequencies from 3.5 kHz to 5 kHz. The noise maps at varying intervals revealed that most of the locations are having higher noise levels due to increase in vehicular movement. The study emphasizes on the potential significance of the proposed neural network-based prediction model in collaboration with noise mapping as a vital tool for the anticipation of traffic noise levels and the formulation of noise mitigation strategies in context of the mid-sized smart city like Dhanbad, India.

Traffic-Aware Energy-Efficient Resource Allocation for RSMA Based UAV Communications

Traffic measurement will play an essential role in future networks to reveal the traffic requirements of the users, which will support network operations like resource allocation. In this paper, we study the traffic-aware resource allocation problem for downlink rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) communications. Specifically, with the help of traffic measurement, user requirements on achievable rate are known as prior knowledge to the UAV. Considering user requirements, we maximize the energy efficiency of the UAV by jointly optimizing the UAV deployment, beamforming, rate allocation, and subcarrier allocation. To overcome the non-convexity of the above problem, a joint optimization is developed to solve it iteratively. First, a heuristic approach is proposed to find the three-dimensional location of the UAV. Then, successive convex approximation approach is utilized to optimize RSMA parameters. Moreover, we formulate the subcarrier allocation problem as a many-to-one two-sided matching game, which is tackled by the swap matching algorithm. Numerical results suggest that the proposed subcarrier allocation scheme outperforms benchmark schemes, and RSMA based schemes perform close to that based on non-orthogonal multiple access, which all outperform orthogonal frequency division multiple access based scheme.

Transforms-Based Bayesian Tensor Completion Method for Network Traffic Measurement Data Recovery

Network traffic measurement is regarded as the bedrock of next-generation network systems. Its purpose is to monitor the network traffic and provide data support for traffic engineering. For this reason, monitoring traffic data from a network-wide perspective is particularly important. However, the proliferation of network services has led to the explosive growth of network traffic, which has brought significant challenges to the measure of network-wide traffic. Therefore, how to infer network-wide traffic from partial traffic data is extremely important. In this article, a transforms-based Bayesian tensor completion (TBTC) method is proposed to infer network traffic data. First, the heterogeneous network traffic data with missing entries are organized into observation tensors according to temporal dimensions and other attributes. Second, the sparse hierarchical prior is used to induce lateral slices sparsity of factor tensors, which makes the tubal rank of the observation tensor can be estimated. Further, a variational Bayesian inference method is developed for model learning, and an efficient updating method is presented. Finally, two cases of the linear transforms-based tensor completion model are implemented in the experiments. Experimental results on two real-world network traffic datasets validate that the proposed method can efficiently and accurately recover network traffic data.

Distributed Strategy for Collaborative Traffic Measurement in a Multi-Controller SDN

Distributed Strategy for Collaborative Traffic Measurement in a Multi-Controller SDN