Congestion Indexes Research Articles

Data-driven approach for port resilience evaluation

As pivotal nodes in international trade, ports have faced unprecedented challenges, particularly in the context of the COVID-19 pandemic. From the perspective of port congestion, this study investigates port resilience based on a quantitative approach. Port resilience is specifically evaluated using five novel metrics derived from resilience capacities and port congestion indexes, employing data-driven approaches, which are then applied to analyze nine global ports across six regions. The findings of the study indicate that: 1) The ports of Southampton and Shanghai display better resilience levels, whereas New York/New Jersey and Los Angeles/Long Beach show poorer resilience performance during the research period. 2) Ports in West Coast North America demonstrate relatively low resilience levels, while those in South East Asia showcase superior resilience. 3) The changing dynamics and evolution of port resilience rankings across different years underscore the multifaceted nature of port resilience and its capacity to adapt to external factors such as global events. Our study emphasizes the importance of quantifying port resilience, with a specific focus on port congestion. It provides valuable insights that have significant implications for port management and disaster preparedness within the maritime industry.

Coexistence of a fluid responsive state and venous congestion signals in critically ill patients: a multicenter observational proof-of-concept study

BackgroundCurrent recommendations support guiding fluid resuscitation through the assessment of fluid responsiveness. Recently, the concept of fluid tolerance and the prevention of venous congestion (VC) have emerged as relevant aspects to be considered to avoid potentially deleterious side effects of fluid resuscitation. However, there is paucity of data on the relationship of fluid responsiveness and VC. This study aims to compare the prevalence of venous congestion in fluid responsive and fluid unresponsive critically ill patients after intensive care (ICU) admission.MethodsMulticenter, prospective cross-sectional observational study conducted in three medical–surgical ICUs in Chile. Consecutive mechanically ventilated patients that required vasopressors and admitted < 24 h to ICU were included between November 2022 and June 2023. Patients were assessed simultaneously for fluid responsiveness and VC at a single timepoint. Fluid responsiveness status, VC signals such as central venous pressure, estimation of left ventricular filling pressures, lung, and abdominal ultrasound congestion indexes and relevant clinical data were collected.ResultsNinety patients were included. Median age was 63 [45–71] years old, and median SOFA score was 9 [7–11]. Thirty-eight percent of the patients were fluid responsive (FR+), while 62% were fluid unresponsive (FR−). The most prevalent diagnosis was sepsis (41%) followed by respiratory failure (22%). The prevalence of at least one VC signal was not significantly different between FR+ and FR− groups (53% vs. 57%, p = 0.69), as well as the proportion of patients with 2 or 3 VC signals (15% vs. 21%, p = 0.4). We found no association between fluid balance, CRT status, or diagnostic group and the presence of VC signals.ConclusionsVenous congestion signals were prevalent in both fluid responsive and unresponsive critically ill patients. The presence of venous congestion was not associated with fluid balance or diagnostic group. Further studies should assess the clinical relevance of these results and their potential impact on resuscitation and monitoring practices.

Usefulness of Combined Ultrasound Assessment of E/e' Ratio, Pulmonary Pressure, and Cava Vein Status in Patients With Acute Heart Failure

Congestion is poorly investigated by ultrasound scans during acute heart failure (AHF) and systematic studies evaluating ultrasound indexes of cardiac pulmonary and systemic congestion during early hospital admission are lacking. We aimed to investigate the prevalence of ultrasound cardiac pulmonary and systemic congestion in a consecutive cohort of hospitalized patients with AHF, analyzing the relevance of each ultrasound congestion component (cardiac, pulmonary, and systemic) in predicting the risk of death and rehospitalization. This is a prospective research study of a single center that evaluates patients with an AHF diagnosis who are divided according to the left ventricular ejection fraction in patients with heart failure with preserved ejection fraction or reduced ejection fraction. We performed a complete bedside echocardiography and lung ultrasound analyses within the first 24hours of hospital admission. The ultrasound congestion score was preliminarily established by measuring the following parameters: cardiac congestion, which was defined as the contemporary presence of E/e' >15 and pulmonary systolic pressure >35mm Hg and the pulmonary congestion, defined as the total B-line number >25 at the lung ultrasound performed in 8 chest sites; moreover, the systemic congestion was defined if the inferior vena cava (IVC) was >21mm and if it was associated with a reduced inspiratory collapse >50%. We thoroughly assessed 230 patients and evaluated their results. Of these patients, 135 had heart failure with reduced ejection fraction and there were 95 patients with heart failure with preserved ejection fraction; 122 patients experienced adverse events during the 180-day follow-up. The receiver operating characteristic curve analysis showed that the tricuspid annular peak systolic excursion (TAPSE) (area under the curve [AUC] 0.34 [0.26 to 0.41], p <0.001), E/e' (AUC 0.62 [0.54 to 0.69], p=0.003), and IVC (AUC 0.70 [0.63 to 0.77], p <0.001) were all significantly related to poor prognosis detection. The univariate Cox regression analysis revealed that cardiac congestion in terms of E/e' and pulmonary systolic pressure (hazard ratio [HR] 1.49 [1.02 to 2.17], p=0.037), TAPSE (HR 0.90 [0.85 to 0.94], p <0.001), and systemic congestion (HR 2.64 [1.53 to 4.56], p <0.001) were all significantly related to the 180-day outcome. After adjustment for potential confounders, only TAPSE (HR 0.92 [0.88 to 0.98], p=0.005) and IVC (HR 1.92 [1.07 to 3.46], p=0.029) confirmed their prognostic role. The multivariable analysis of multiple congestion levels in terms of systemic plus cardiac (HR 1.54 [1.05 to 2.25], p=0.03), systemic plus pulmonary (HR 2.26 [1.47 to 3.47], p <0.001), and all 3 congestion features (HR 1.53 [1.06 to 2.23], p=0.02) revealed an incremental prognostic role for each additional determinant. In conclusion, among the ultrasound indexes of congestion, IVC and TAPSE are related to adverse prognosis, and the addition of pulmonary and cardiac congestion indexes increases the risk prediction accuracy. Our data confirmed that right ventricular dysfunction and systemic congestion are the most powerful predictive factors in AHF.

Traffic flow matrix-based graph neural network with attention mechanism for traffic flow prediction

Traffic flow forecasting is of great importance in intelligent transportation systems for congestion mitigation and intelligent traffic management. Most of the existing methods depend on deep learning to extract the spatial–temporal correlation of traffic nodes but ignore the traffic flow characteristics. In this paper, we design three traffic congestion indexes to reflect the operational status of nodes based on traffic flow theory and design a traffic flow matrix to better represent the relationship between nodes. We also design a novel graph convolution network with attention mechanisms called TFM-GCAM to better capture the spatial–temporal features and dynamic characteristics of nodes. A novel Fusion Attention mechanism is proposed to effectively fuse the dynamic characteristics and the spatial–temporal features for improvement. Experiments and ablation studies on the public dataset show the superiority of TFM-GCAM. We also discuss it with our previous works for a better understanding. Our research proposes to better integrate traffic flow theory into deep learning models and to better combine the respective strengths of attention mechanisms and graph neural networks for more effective traffic flow prediction.

A Simulation Approach for Evaluating Congestion and Its Mitigation Measures on Urban Arterials Operating with Mixed Traffic Conditions

Present study evaluates the various congestion indexes in terms of speed performance index, volume to capacity ratio and congestion index based on travel time on the urban roadway network of 5.7 km in length, operating with mixed traffic conditions (traffic with varying static and dynamic characteristics of vehicles). The mitigations measures have been proposed to cater to the present traffic demand by converting the uncontrolled intersection to a partially controlled or signal-controlled intersection. In order to check the effectiveness of such mitigation measures, various scenarios have been generated in the developed and a well calibrated simulation model. It has also been observed that the speed performance index has improved by 8.88 percent while providing channelized intersection at one of the point elements and 11.52 %when the two-point elements have converted to channelizing intersection.

Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model.

With the acceleration of urbanization, traffic congestion and vehicle exhaust pollution are becoming increasingly serious problems. Focusing on the problem of urban pollution from vehicle exhaust, this study used system dynamics to establish an urban congestion mitigation and emission-reduction management model. Specifically, a nonlinear function that integrates system dynamics and a non-homogeneous discrete grey model (SD-NDGM) was used to construct an algorithm, which improved the accuracy of the model. Thereafter, the mid- and long-term effects of the restriction policy were explored. The main findings from dynamic model simulations were as follows: All types of restrictions alleviated traffic congestion to varying degrees, but “odd and even” restrictions had more obvious effects, with an average annual reduction rate of 43.53% in the number of motor vehicle trips. The driving-restriction policy had a time effect, significantly reducing the number of vehicle trips in the short term. However, it could have negative effects in the long term (e.g., agglomeration effect, emission-reduction paradox), and it does not fundamentally solve traffic and environmental problems. Thus, it could only be used as a phased policy, not a long-term measure. The purchase-restriction policy controlled excessive increases in the number of private cars, but it had little effect in terms of solving environmental problems. Compared with a single policy, the combination of public-transport development and driving-restriction policy not only reduced traffic congestion, air pollution, and air quality health indexes by 29.13%, 52.63%, and 54.63%, respectively, but also improved environmental carrying capacity by 294.26%. A combined approach can therefore be said to have certain benefits for society, health, and the environment.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-022-02319-9.

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.

Variable Cell Transmission Model for Mixed Traffic Flow with Connected Automated Vehicles and Human-Driven Vehicles

The current research on the mixed traffic flow characteristics of human-driven vehicles (HDVs) and connected automated vehicles (CAVs) mainly focuses on the micro-level. To study the characteristics of the mixed traffic flow from the medium and macro level, this paper proposes a variable cell transmission model (VCTM). First, the fundamental diagram is introduced based on the phenomena of hysteresis of traffic flow. Second, the VCTM with different market penetration rates (MPR) of CAVs is proposed based on the classical cell transmission model (CTM). Then, the effectiveness of VCTM is verified by micro-simulation based on the intelligent driver model (IDM). Finally, some congestion indexes are selected to discuss the characteristics of mixed traffic flow based on the VCTM with an expressway simulation. The results show that the traffic capacity and congestion dissipation capacity gradually are increased with the increase of MPR of CAVs. The homogeneous CAVs traffic flow capacity can reach 1.41 times that of the homogeneous HDVs traffic flow, and the congestion dissipation time can be reduced by 25%. The larger MPR is, the greater the improvement effect is. In addition, compared with CTM, VCTM can reflect the delay, queuing, and dissipation of mixed traffic flow more accurately, which is helpful to capture the evolution mechanism of mixed traffic flow in the future.

Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

Estimating the influence of disruption on highway networks using GPS data

Incidents, such as natural disasters, public events, and holidays, often cause problems to highways, even paralyze the operation of the whole networks, leading to a serious threat to travel efficiency and safety of the public. To provide better transport management and plans for emergencies, it is important to quickly and accurately identify such incidents and estimate their disruptive effects on the networks. To this end, a novel approach has been proposed in this paper, which is based on the Bayesian theory and thrice-standard-error principle while utilizing vehicle GPS data. Two important indicators, including traffic flows and congestion indexes, along with their change ratios, are built to detect the incidents and evaluate the extent of the disruption. The specific disrupted and detour roads are further determined. The proposed method has been tested using two real-world events in China, and the potential and effectiveness of this technique are demonstrated. With more and more vehicles being equipped with GPS devices worldwide, the designed method can be easily transferable to other countries, paving a way for the adoption of the approach for a more spatial–temporal sensitive highway network disruption analysis method that supports the establishment of a more resilient transport system for emergencies.

Quantitative Modeling of Congestion in Metro Station Based on Passenger Time Perceptions

In the metro station, passengers’ psychological state can be affected by space congestion, slow walking speed, increasing queue length, and other factors, resulting in a time-lapse effect, which can be described as people feeling that they spend more time than the actual. To measure the effect, this paper develops congestion indexes for the walking and queuing areas in metro stations, and designs stated preference (SP) and revealed preference (RP) surveys and scene simulation experiments. Based on the survey and experiment data, we attempt to describe the psychological state and space–time perception of passengers in metro stations and propose quantitative models of congestion indexes at different service levels. Furthermore, on the basis of these models, thresholds of walking distance and waiting time can be calculated for different service levels. The results can provide a reference for real-time passenger flow monitoring and theoretical supports for metro operators to measure passenger flow status and adopt passenger flow management strategies under different conditions. Overall, this study offers promising insights into passenger flow monitoring and management, but some limitations need to be addressed in future work.

Towards an agent-based framework for urban traffic congestion management

This paper introduces an integrated solution to the road congestion problem by modelling the road network, using real-time traffic data and drivers' parameters to compute the proposed congestion index for each road segment and generating recommendations to avoid the most congested trajectories. The proposed framework combines the benefits of the multi-agent systems, traffic data from static sensors and big data tools in order to optimise the traffic flow in urban areas. The congestion indexes are used in the road network generation which is represented by a weighted graph. The edges' costs are computed based on the congestion indexes and the edge's properties and vary when new traffic records are retrieved. In this study, the Hadoop framework is used in the data gathering and analysis along with an improved version of Dijkstra for the least congested path finding which allows the proposed framework to reach a higher level of performance.

Towards an agent-based framework for urban traffic congestion management

This paper introduces an integrated solution to the road congestion problem by modelling the road network, using real-time traffic data and drivers' parameters to compute the proposed congestion index for each road segment and generating recommendations to avoid the most congested trajectories. The proposed framework combines the benefits of the multi-agent systems, traffic data from static sensors and big data tools in order to optimise the traffic flow in urban areas. The congestion indexes are used in the road network generation which is represented by a weighted graph. The edges' costs are computed based on the congestion indexes and the edge's properties and vary when new traffic records are retrieved. In this study, the Hadoop framework is used in the data gathering and analysis along with an improved version of Dijkstra for the least congested path finding which allows the proposed framework to reach a higher level of performance.

Floating Car Data and Fuzzy Logic for classifying congestion indexes in the city of Shanghai

Abstract. In this paper, we use Floating Car Data from the city of Shanghai and Fuzzy Inference model to detect congestion indexes throughout the city. We aim to investigate to which extent traffic congestion is severe during afternoon rush hour. Additionally, we compare our results to the ones obtained by calculating congestion indexes on conventional way. Although we do not argue that our model is the best measure of congestion, it does allow the mechanism to combine different measures and to incorporate the uncertainty in the individual measures so that the compound picture of congestion can be reproduced.

Geographical patterns of traffic congestion in growing megacities: Big data analytics from Beijing

Traffic congestion is one of the key issues relating to sustainability and livability in many large cities. In particular, the situation in the growing megacities of developing countries has been worsening and is now attracting considerable attention from researchers and politicians. An understanding of the spatio-temporal patterns of this congestion is necessary in order to formulate effective policies to relieve it. Much of the research to date has focused on single districts for relatively short periods (days or weeks) using GPS, while long-term analysis of spatial and temporal patterns of traffic congestion at the city level has been rare. The aim of this paper is to help fill this gap in the literature by applying a big data analytic approach to a sample of 10.16 million records of traffic congestion indexes for 233 roads in the Beijing area over a six-month period. This analysis revealed four typical traffic congestion patterns in Beijing, which can be described as the weekend mode, holiday mode, weekday mode A, and weekday mode B. Each of these patterns possesses unique spatial and temporal characteristics. Compared with working days, on which congestion is regular and agglomerated, weekends and holidays are characterized by long-lasting congestion peaks throughout the day. Non-commuting travel on weekends and holidays, including trips for tourism, shopping, entertainment, and children's after-school activities, are major contributors to traffic congestion of the weekend and holiday mode. Owing to poor jobs-housing balance, the suburban new towns and job centres had relatively higher congestion than other areas. These findings shed significant light on geographical patterns of traffic congestion in growing megacities.

A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index

This study aimed to analyze traffic congestion in urban road networks. The speed performance index was adopted to evaluate the existing road network conditions of congestion, then road segment and network congestion indexes were introduced to respectively measure the congestion levels of urban road segment and network. This study also carried out a traffic congestion analysis for Beijing expressway network, based on the speed performance data collected from January 1 to November 1, 2012, by Beijing Traffic Management Bureau (BTMB). Based on these analyses the proposed congestion indexes can well assess the traffic congestion conditions of urban road networks, more importantly, such an assessment study provides traffic control and management agencies an accurate and clear understanding of operation status of traffic networks.

A Reasonable Route Choice Set Generation Algorithm Based on Link Penalty Method

AbstractTo input a reasonable route choice set containing more routes of drivers’ real choices into the route choice model, a route choice set generation algorithm based on link penalty method was proposed. According to congestion frequency, link congestion index was defined to establish the penalty rule of the algorithm. The impedances of the links with large congestion indexes in the shortest path of last iteration, which is different from all of the links in the original algorithm, were increased in the proposed algorithm. Reasonable indexes were defined to evaluate the efficiency of route choice set generation algorithm. According to measured data of drivers’ real travel routes, an evaluation of the proposed link penalty algorithm, the original link penalty algorithm and the widely used Monte Carlo labeling combination algorithm was performed. The results show that the proposed algorithm reproduces better observed behavior.

Smoker's nose: Structural and functional characteristics

The effects of smoking on endonasal geometry and airflow remain largely unknown. Our study examined the relationship between smoking status and objective measures of nasal cavity dimensions, nasal congestion, and nasal airflow, using acoustic rhinometry (AR) and peak nasal inspiratory flow (PNIF). Cross-sectional study. Included in the study were 2,523 consecutive patients referred for evaluation of chronic nasal or sleep-related complaints. Smoking history was recorded, and AR and PNIF were measured at baseline and after decongestion of the nasal mucosa. Minimal cross-sectional areas (MCA), nasal cavity volumes (NCV), and PNIF, as well as quantified reversible mucosal congestion based on nasal congestion indexes (NCI) were analyzed to reveal possible associations with smoking status. Linear and logistic regressions were applied adjusting for possible confounders. Smokers exhibited lower values of MCA, NCV, and PNIF than nonsmokers, both at baseline and after decongestion. Further, smokers had a lower decongestive capacity of the nasal mucosa, reflected by lower NCI for AR measures. Cigarette consumption, expressed as either pack-years or cigarettes smoked per day, showed a similar inverse relationship with the rhinometric measures even though a linear dose-response relationship could not be established. We have clearly demonstrated that smokers exhibit lower MCA and NCV, achieve lower PNIF values, and have a less-compliant nasal mucosa than nonsmokers. Our results are unique, and provide evidence that smoking has adverse effects on the nasal airway, possibly due to mucosal inflammation. This might have further implications because altered nasal function could compromise the lower airways.

Value of nasal mucosa congestion index in choosing treatment strategies for chronic rhinosinusitis

Conclusion: The mucosa compliance in lesions is determined by the congestion index, enabling us to determine whether the patient is sensitive to pharmacotherapy and to choose surgical management rationally. Objective: To explore the correlation between the nasal mucosa congestion index and tissue remodeling in chronic rhinosinusitis (CRS) and to provide objective evidence for clinical choice of therapeutic methods. Patients and methods: Forty-four patients with CRS who underwent nasal endoscopic surgery were enrolled in the study. The congestion indexes of the nasal mucosa were measured using an acoustic rhinometer and specimens were taken from the uncinate process mucosa to evaluate vascular density and degree of fibrosis. The correlations were statistically analyzed. Results: There was an inverse correlation between the congestion index and degree of fibrosis (r=− 0.522, p=0.00) and a direct correlation between the congestion index and vascular density (r=0.838, p=0.00) in nasal mucosa. There was a significant correlation between the congestion index and the combination of degree of fibrosis and vascular density with a determination coefficient (R2) of 0.778.

Portal and splenic hemodynamics in cirrhotic patients: relationship between esophageal variceal bleeding and the severity of hepatic failure.

The relationship between portal and splenic vein hemodynamics, liver function, and esophageal variceal bleeding in patients with cirrhosis remains unclear. The aim of the present study was to investigate quantitative Doppler parameters of splanchnic hemodynamics in cirrhotic patients and to determine the value of the Doppler parameters in predicting esophageal variceal bleeding. With the help of pulsed Doppler ultrasonography, we investigated portal and splenic hemodynamics in 18 healthy controls and in 45 patients with liver cirrhosis, in whom the relationship of splenic hemodynamics with esophageal variceal bleeding and the grade of cirrhosis was examined. Portal flow velocity was decreased in cirrhotic patients with Child's C cirrhosis, as compared to those with Child's A cirrhosis ( P < 0.001). The portal blood flow volume in Child's C cirrhosis were also significantly low compared to patients with Child's A and Child's B cirrhosis ( P < 0.001 and P < 0.05, respectively). There was a significant increase in the portal vein congestion index and splenic vein congestion index in patients with Child's C cirrhosis as compared to patients with Child's A cirrhosis ( P < 0.001). Among cirrhotic patients, the group with esophageal variceal bleeding had significantly greater splenic blood flow volume and splenic vein congestion index ( P < 0.001). Patients with ascites had significantly lower portal flow velocity ( P < 0.001) and higher portal vein congestion index and splenic vein congestion index ( P = 0.003 and P = 0.05, respectively) as compared to those without ascites. In this report we have shown that the decrease in blood flow and increased congestion indexes in the portal vein and splenic vein are related to the impairment of liver function in cirrhotic patients; these indexes may be valuable factors for predicting esophageal variceal bleeding.