Congestion Monitoring Research Articles

WITHDRAWN: The burden of congestion monitoring in acute decompensated heart failure: The need for multiparametric approach

WITHDRAWN: The burden of congestion monitoring in acute decompensated heart failure: The need for multiparametric approach

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

Automatic Detection of Pedestrian Crossing Platform using Congestion Monitoring

Abstract: One of the primary problems faced globally is the amount of traffic on the roads and the number of pedestrian accidents. The risk when crossing or walking on roads in urban and rural regions with significant traffic is a major factor in these incidents. A novel idea is put forth to avert such situations. Using congestion monitoring, automatic detection of pedestrian crossing platforms. An IR (Infrared) sensor module is used to continuously monitor pedestrian and traffic congestion. When there are more pedestrians on the road, the traffic light for automobiles turns red, allowing pedestrians to cross at the zebra crossing. In the proposed system, we are counting the packed pedestrians at the signal point and performing congestion monitoring of the pedestrian and vehicle count, which will be sensed by IR Infrared Sensors. This technique guarantees pedestrian crossing safety and prevents drivers from flouting the law, which could cause accidents.

Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces

Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards.

Integrating high-frequency data in a GIS environment for pedestrian congestion monitoring

Pedestrian congestion can negatively impact how visitors perceive tourist destinations, as well as turn local residents against the tourism industry. In this sense, urban planning and management principles derived from smart tourism can be enriched by applying suitable spatial analytical tools to monitor flows and congestion. This study puts forward a methodology integrating emerging geospatial data sources into a GIS environment to analyze pedestrian flows through the city of Palma. The information was obtained from devices’ geolocation data captured by the free Wi-Fi network (Smart Wi-Fi) during 2019 in the city of Palma, Spain. In order to calibrate the method, fieldwork was undertaken to count individuals in situ; this information was then contrasted with the raw data provided by the network. The study also assesses congestion in areas based on walkable space and the number of recorded devices. The results show different mobility patterns, which highlight several overloaded and congested areas within the city. The developed methodology demonstrates the usefulness of the method in providing support to decision-making in tourism management, as well as promoting sustainability in tourist destinations.

Intelligent agent based virtual clustering and multi-context aware routing for congestion mitigation in secure RPL-IoT environment

RPL (Routing Protocol for Low power Lossy network) is a routing protocol for Internet of Things (IoT) network with low power consumption. RPL faces many security challenges because of its resource constrained nature. That security threats introduced heavy congestion in the network. If the congestions are not properly managed then it will reduce overall network performance, in addition it also reduces the network lifetime. In real time IoT application, the parent nodes attacks massive amount of nodes and establish the unbalanced structure that reduces network reliability. In this research we proposed Secure RPL (Sec-RPL) to perform congestion control RPL-IoT environment. For that we proposed five phases, (1). RPL node authentication initially, all the nodes are register their ID, IP, MAC, Rank and PUF in the sink node which generate secret key for that node by blowfish algorithm, and hash values are generated by lightweight hash one algorithm. (2). To increase network reliability we propose multi-context aware parent selection, the context decisions are made by grey set theory based decision making algorithm. For optimal parent selection we deploy Hybrid Cuttle Fish-Harris Hawker Optimizer (HC-HHO) algorithm. (3) Congestion detection and mitigation, for congestion monitoring we propose Multi-Intelligent Agent based Q-Learning (MIAQ) which mainly relies on three environmental status such as buffer status, generation status and loss status. For congestion mitigation we proposed virtual cluster based congestion mitigation (VC2M) method. (4). Deep packet analysis and attack detection, Lightweight Convolutional Neural Network with Transfer Learning (LiteCNN-TL) algorithm is deployed to analyze each packet in depth to detect malicious packets. (5). Adaptive Trickle Timer, Dynamic Rule base Trickle (DR-trickle) is deployed to adaptively changed the trickle timer based on multiple parameters (Traffic Intensity, RSSI, Received Time Interval). Finally, the simulation is conducted by NS3.26 network simulator. The result shows that the proposed system achieves better performance in terms of average control traffic overhead, packet delivery ratio, average energy consumption, delay, packet loss rate ad load balancing capacity with respect to network size and number of malicious nodes.

OpenCV Based Automatic Detection of Pedestrian Crossing Platform Using Congestion Monitoring - A Survey

Road traffic congestion and pedestrian accidents are one of the major issues being faced worldwide. The reasons behind these accidents are mainly due to the risk when crossing or walking on road in urban and rural areas under heavy traffic. In order to avoid such circumstances a new idea is proposed- “Automatic uplifting of pedestrian crossing platform using traffic congestion monitoring”. The pedestrian and traffic congestion is constantly monitored using an IR (Infrared) sensor module. At the time when pedestrian density is more, the traffic signal turns red for the vehicles and let the pedestrian walk through uplifted crossing platform. The proposed system consists of motorized platform placed on the zebra crossing line which automatically uplifts when the infrared sensor senses and count the crowded pedestrians at the signal point. This method ensures safety in pedestrian crossing and will also not let drivers to break the rules, which may lead to accidents

Using Probe-Based Speed Data and Interactive Maps for Long-Term and COVID-Era Congestion Monitoring in San Francisco

Probe data that provide roadway speeds and travel times are increasingly being used for a variety of purposes in the transportation domain. A key use of these datasets has been roadway performance monitoring by state and local transportation agencies that are mandated to measure and report performance of their transportation networks. The San Francisco County Transportation Authority (SFCTA) monitors roadway performance as a part of the biennial Congestion Management Program (CMP) and primarily uses probe-based speed data for that purpose. Despite considerable savings in time and effort for data collection, integrating and processing the probe data still required a significant amount of manual work. This study highlights these challenges and proposes a data processing pipeline which includes an automated network conflation process, an efficient large data processing framework, and an interactive web-based visualization. In addition, all the scripts and code developed were made open source and are readily accessible from a public repository on GitHub. The value of the pipeline is demonstrated through the development of web-based interactive maps to monitor both long-term and short-term congestion in San Francisco. The short-term congestion monitoring application is timely given the spread of the COVID-19 pandemic and the region’s rapidly changing traffic conditions. Several valuable lessons learned from use of probe data for roadway performance monitoring are shared. Developing tools to ensure consistency of the data product and to reduce reliance on any one data vendor is of key importance.

Incorporating Crowdsourced Travel Time Data in Congestion Monitoring

Incorporating Crowdsourced Travel Time Data in Congestion Monitoring

Exploring regression models to enable monitoring capability of local energy communities for self‐management in low‐voltage distribution networks

This study proposes a data-driven approach to enable the self-management capability of local energy communities (LECs) via transformer congestion monitoring in low-voltage distribution networks. A set of regression models is adopted in this approach, while the data from residential smart meters (SMs) is leveraged. Four machine learning algorithms, namely ridge regression, support vector regression, random forest regression (RFR) and eXtreme gradient boosting regression (XGBR), are compared to select the best-performing regression model using a cross-validation method. A comprehensive framework is provided to facilitate comparison of the algorithm, consisting of data pre-processing, model fitting and validation, and model deployment. A thorough analysis is also SMs' measurements. The obtained results highlight that the regression-based method can effectively estimate the transformer loading, that is with the Pearson correlation coefficient R and root mean square error calculated for the real values and the estimated values of around 0.98 and 0.87, respectively, by using only a limited set of SM measurements (5 out of 21 SMs used) provided by the LECs while preserving customers' privacy rights. Among the examined algorithms, the XGBR algorithm appears the best method as it achieves adequate accuracy at significantly less simulation time (i.e. one-third of the simulation time of the RFR). By applying the proposed approach, the monitoring and self-management capability of the LECs can be realised.

Diagnostic and Prognostic Value of Lung Ultrasound B-Lines in Acute Heart Failure With Concomitant Pneumonia.

Purpose: To evaluate the potential confounding effect of concomitant pneumonia (PNM) on lung ultrasound (LUS) B-lines in acute heart failure (AHF).Methods: We enrolled 86 AHF patients with (31 pts, AHF/PNM) and without (55 pts, AHF) concomitant PNM. LUS B-lines were evaluated using a combined antero-lateral (AL) and posterior (POST) approach at admission (T0), after 24 h from T0 (T1), after 48 h from T0 (T2) and before discharge (T3). B-lines score was calculated at each time point on AL and POST chest, dividing the number of B-lines by the number of explorable scanning sites. The decongestion rate (DR) was calculated as the difference between the absolute B-lines number at discharge and admission, divided by the number of days of hospitalization. Patients were followed-up and hospital readmission for AHF was considered as adverse outcome.Results: At admission, AHF/PNM patients showed no difference in AL B-lines score compared with AHF patients [AHF/PNM: 2.00 (IQR: 1.44–2.94) vs. AHF: 1.65 (IQR: 0.50–2.66), p = 0.072], whereas POST B-lines score was higher [AHF/PNM: 3.76 (IQR: 2.70–4.77) vs. AHF = 2.44 (IQR: 1.20–3.60), p < 0.0001]. At discharge, AL B-lines score [HR: 1.907 (1.097–3.313), p = 0.022] and not POST B-lines score was found to predict adverse events (AHF rehospitalization) after a median follow-up of 96 days (IQR: 30–265) in the overall population.Conclusions: Assessing AL B-lines alone is adequate for diagnosis, pulmonary congestion (PC) monitoring and prognostic stratification in AHF patients, despite concomitant PNM.

Fused computational approach used in transportation industry for congestion monitoring

Fused computational approach used in transportation industry for congestion monitoring

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions.

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Grid Mapping for Road Network Abstraction and Traffic Congestion Identification Based on Probe Vehicle Data

AbstractTraffic congestion monitoring is a long-term concern in urban areas. However, due to the complex structure of urban road networks and large amounts of traffic data, it is necessary to find ...

Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics

With the explosive use of GPS-enabled devices, increasingly massive volumes of trajectory data capturing the movements of people and vehicles are becoming available, which is useful in many application areas, such as transportation, traffic management, and location-based services. As a result, many trajectory data management and analytic systems have emerged that target either offline or online settings. However, some applications call for both offline and online analyses. For example, in traffic management scenarios, offline analyses of historical trajectory data can be used for traffic planning purposes, while online analyses of streaming trajectories can be adopted for congestion monitoring purposes. Existing trajectory-based systems tend to perform offline and online trajectory analysis separately, which is inefficient. In this paper, we propose a hybrid and efficient framework, called Dragoon, based on Spark, to support both offline and online big trajectory management and analytics. The framework features a mutable resilient distributed dataset model, including RDD Share, RDD Update, and RDD Mirror, which enables hybrid storage of historical and streaming trajectories. It also contains a real-time partitioner capable of efficiently distributing trajectory data and supporting both offline and online analyses. Therefore, Dragoon provides a hybrid analysis pipeline. Support for several typical trajectory queries and mining tasks demonstrates the flexibility of Dragoon. An extensive experimental study using both real and synthetic trajectory datasets shows that Dragoon (1) has similar offline trajectory query performance with the state-of-the-art system UlTraMan; (2) decreases up to doubled storage overhead compared with UlTraMan during trajectory editing; (3) achieves at least 40% improvement of scalability compared with popular streaming processing frameworks (i.e., Flink and Spark Streaming); and (4) offers an average doubled performance improvement for online trajectory data analytics.

Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence that is used to estimate traffic congestion levels, arrival times, and average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of or on the route to reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments are conducted with a prototype implementation and the results demonstrates both system feasibility and traffic estimation accuracy.

Transfer learning based hybrid 2D-3D CNN for traffic sign recognition and semantic road detection applied in advanced driver assistance systems

Annually, deep learning algorithms have proven their effectiveness in many vision-based applications, such as autonomous driving, traffic, and congestion monitoring, and so on. In computer vision, accurate traffic sign recognition and semantic road detection are vital challenges for increased safety, which are becoming a major research topic for intelligent transport systems community. In this paper, a deep learning-based driving assistance system has been proposed. To this end, we present hybrid 2D-3D CNN models based on the transfer learning paradigm to achieve better performance on benchmark real-world datasets. The primary goal of transfer learning is to improve the learning process in the target domain while transferring relevant knowledge from the source domain. We combine a pre-trained deep 2D CNN and a shallow 3D CNN to significantly reduce complexity and speed-up the training algorithm. The first model, called Hybrid-TSR, is intended to effectively address the task of traffic sign recognition. Hybrid-SRD is the second architecture that allows the semantic detection of road space through a combination of up-sampling and deconvolutional operations. The experimental results show that the proposed methods have considerable relevance in terms of efficiency and accuracy.

An Improved Traffic Congestion Monitoring System Based on Federated Learning

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers

Multi-view video surveillance is a highly valuable tool to ensure the safety of the crowd in large public space. By utilizing complementary information captured by multiple cameras, the issue of limited views and occlusion in single views can be addressed to gain better insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowd analysis, for example pedestrian detection and tracking, while macroscopic level analysis, which deals with the whole crowd, has received little attention. We propose a multi-view framework for the generation of level of service maps, which are the most commonly used measure of congestion at macroscopic level, based on an ensemble of state-of-the-art Convolutional Neural Networks (CNNs). Several combination rules are compared and evaluated on two datasets, both in sparse and dense scenarios. Our results show that this fusion framework improves the accuracy of level of service map generation, from 83.2% to 89.8%, and eliminates blind spots in single views. Our framework is implemented on a 3 D GIS platform, which provides a suitable interface for multi-view crowd congestion management. The results of a loading test show that a maximum of 48 cameras can be processed at a map refresh rate of 2 seconds.

Internet of things-based fog and cloud computing technology for smart traffic monitoring

Internet of Things (IoT) is changing the world by connecting billions of physical and virtual objects with distinctive identities to the Internet. This fusion results in generating huge volumes of data that might not be manageable using today's storage and data analytics technologies. Although cloud computing offers services to tackle this issue at infrastructural level, its efficiency for time sensitive applications (e.g. oil, gas, and traffic monitoring) is still questionable. Arguably, transferring massive amount of data to the cloud for storage and processing may lead to cloud overloading and saturation of network bandwidth. In this study, an integrated fog and cloud computing framework is introduced to overcome the limitations of real-time analytics, latency and network congestion of basic cloud services for traffic monitoring. The proposed approach is implemented to prototype a smart traffic monitoring system (STMS). The proposed monitoring system is designed for congestion monitoring and traffic light management. It can also be tuned to detect traffic incidents that requires immediate assistance during congestion. In this framework, a tiny computer-on-module serves as a fog node to collect real-time data from geographically distributed sensors and to transfer it to the cloud for storage and processing. The results show the efficiency of the fog network in improving the performance of the cloud platform in terms of reducing the response time and increasing the bandwidth. Furthermore, the proposed integrated fog and cloud framework is interfaced with Tweeter to send alerts about traffic congestion to be subscribed users in the form of Tweet messages .