Congestion Management Strategies Research Articles

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

Optimized ST-Moran’s I Model for Characterizing the Dynamic Evolution of Terminal Airspace Congestion

This study aims to unveil the spatiotemporal evolution of congestion within terminal airspace, offering an in-depth analysis of congestion concerns to effectively utilize airspace resources and devise targeted control strategies, thereby enhancing airspace operation safety and efficiency. Initially, converting segment flow rates into equivalent speeds serves as a quantitative benchmark for operational status. Subsequently, an enhanced version of the ST-Moran’s I index model, specifically tailored to terminal airspace, is developed by incorporating improvements across spatial weight matrices, standard state parameters, and temporal dimensions. Validating this model with actual operational data from Chengdu’s terminal airspace, the research demonstrates significant advancements. Compared to conventional models, the proposed model enhances recognition rates for congestion in spatial and temporal dimensions by 62.5% and 43.61%, respectively. Congestion within terminal airspace predominantly occurs at the intersection of departure-climb and approach-departure segments, exhibiting evident spatiotemporal migration behavior. The proposed model accurately delineates the spatiotemporal characteristics of segment congestion, offering support for tailored congestion management strategies.

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review

Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis

The maritime industry is one of the most crucial sectors in the global economy, facilitating the transportation of goods and commodities across vast distances. However, maritime network congestion has become an increasingly critical challenge that significantly affects shipping efficiency and the overall operational performance of the industry. This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. First, the dynamic analysis of vessel trajectories aims to extract valuable information from ships’ data as they navigate the oceans, enabling proactive traffic management and optimized routing. Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. Furthermore, this study introduces an enhanced Faster R‐CNN vessel detection model for real‐time tracking, integrating convolutional and SKNet networks. To improve short‐term traffic flow prediction accuracy, this study employs multiscale feature analysis through wavelet transformation. The foundational traffic data undergo wavelet decomposition for a detailed representation across frequencies. Gated recurrent unit (GRU) neural network and autoregressive moving average (ARMA) models are utilized to predict trend and noise components, respectively. Fusion of predictions demonstrates superior accuracy and is validated against real data. Empirical results showcase minimal errors and heightened prediction accuracy compared to actual data.

Optimal scheduling of BESS for congestion management considering reliability and OTS

The present research explores transmission congestion control in Battery based Energy Storage Systems (BESS) integrated grids using Optimal Transmission Switching (OTS). The oversimplification of underlying assumptions in prior OTS-based congestion management strategies leads to excessive line switching which is undesirable. Hence, a novel reliability constrained alternating current optimal power flow (AC-OPF) based OTS is presented to mitigate the line congestion in the BESS integrated grids. The suggested approach is solved to acquire the OTS solution for 24 h time frame with the primary goal of minimising generating costs subjected to ac power flow equality, BESS, reliability, and transmission congestion constraints. The proposed approach makes use of the integer variable to represent the transmission switching operations and is solved by using a mixed-integer non-linear programming (MINLP) solver. The inclusion of reliability constraints ensures the system reliability by limiting the value of load failure probability to a pre-specified limit. The feasibility of this approach is validated by testing it on a standard IEEE test system for various loading scenarios. The results exhibit that coordinated charging/discharging of BESS supports reducing the number of lines switchings required for congestion management compared to the existing approaches, substantially.

Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems

One of the fundamental concerns of distribution companies (DISCOs) is how to satisfy security requirements under uncertain conditions and real-time contingencies. To address this concern, it is vital to employ operational flexibility levers aimed at mitigating congestion and retaining optimal reserve capacity, particularly in critical feeders. Increased interaction between DISCOs and virtual energy storage systems (VESSs) offers an unparalleled opportunity to unlock the energy flexibility of distribution grids at the local level without increasing operational complexity. According to the state-of-the-art, models that tap the economic performance of VESSs while respecting the flexibility and security setpoints adopted by DISCOs in a distributed manner have remained scarce. This study goes beyond by developing a flexibility-oriented decision-making strategy as a single-leader multi-followers stochastic model; in which a multi-district DISCO involves the flexibility setpoints and congestion management strategy in the market participation plan of each VESS locally to enhance the energy flexibility of each district. In the upper level, DISCO aims to deal with stressful situations in real-time sessions by (i) determining the minimum reserve capacity requirements in each district to be provided by VESSs with respect to the existing uncertainty sources and the importance of each district, and (ii) alleviating the congestion in critical feeders to release the feeder capacity for deployment of the available reserve power promptly. In the lower level, each VESS makes optimal decisions on power and reserve bidding in day-ahead, reserve, and real-time markets that are affected by the flexibility services required by the DISCO. Inspired by the activity schedule of different users, it also examines how demand response programs can affect DISCO’s ability to unlock consumer-side flexibility in realizing congestion management strategies. The developed bi-level scheduling model is first reformulated as single-level mathematical program with equilibrium constraints (MPEC) by employing Karush–Kuhn–Tucker (KKT) optimality conditions and then linearized into a mixed-integer linear program (MILP). The proposed strategy is validated through extensive simulations conducted on the modified IEEE 33-bus test system consisting of three districts with different users. Furthermore, DIgSILENT PowerFactory is used to verify the feasibility of the proposed model under contingency cases and to provide insights for DISCOs to control multiple VESSs and capacity-correlated uncertainties.

Modeling and characterization of the impact of capacity limitation services on distribution networks

Various congestion management strategies have been proposed to tackle the operational challenges arising from the widespread integration of distributed energy resources (DERs) in distribution networks. Among these strategies, capacity limitation services have recently gained significant attention. However, comprehensive analyses are essential for system operators to fully understand the impact and effectiveness of these services in distribution networks. In this study, we develop a model to represent the aggregator’s response to capacity limitation services and assess their impact on network states. The proposed model, from the aggregator’s perspective, consists of non-flexible baseload and controllable DERs, optimizing the aggregator’s benefits based on current market conditions without accessing network states. Consequently, system operators must ensure distribution network security while facilitating flexible resources. Given the unavailability of customer privacy and the complexity of multi-period consumption behaviors, system operators concentrate on capturing aggregator behaviors during potentially risky congestion periods. Our analysis specifically aims to quantify the effects of service area coverage, power capacity limitation values, and service durations. We establish an evaluation framework to coordinate various modules by assessing these services under different scenarios. Simulation results demonstrate that the proposed model can effectively represent power consumption behavior using partially accessible information. Furthermore, we design an associated procedure to offer distribution system operators valuable insights into system behavior, empowering them to request appropriate services in anticipation of potential operational issues.

Research on Traffic Congestion Management Strategies of Primary and Secondary Schools in Cities of China

Research on Traffic Congestion Management Strategies of Primary and Secondary Schools in Cities of China

A Traffic Flow Dependency and Dynamics based Deep Learning Aided Approach for Network-Wide Traffic Speed Propagation Prediction

The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a traffic flow dependency and dynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v-CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v-CTM. In this way, we well couple the physical models (traffic dependency and v-CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v-CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy.

Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data

It is of great reference significance to exploring spatial dependence of urban traffic activities and researching internal causes of regional traffic state changes for road network optimization and residents’ travel behavior analysis. Based on trajectory data of taxis in Ningbo city of China, this study calculates average driving speed of taxis in different blocks during characteristic period and generates the global Moran’s I and the LISA clustering diagram. On this basis, the spatial clustering characteristics of congestion on working days and non-working days are analyzed. Furthermore, in order to further characterize the changes of congestion from the perspective of spatial migration, a method of measuring geometric displacement is adopted to describe spatio-temporal migration trend of traffic states, four indicators designed to identify urban frequently congested areas, including migration direction, angle, distance, and low-value area. The results show that the high-clustering area are located urban fringe and the low-clustering area are located at geometric center of major urban areas. Spatial–temporal migration law of low-value areas in city-center is obvious. Difference between trend is compared with non-working days, the offset and azimuth of low-value area in downtown on working days are even bigger. The accurate capture of the characteristics of congestion space migration at the urban scale will help to formulate more targeted congestion management strategies.

Seeking shelter: homelessness and COVID-19

Those experiencing homelessness in Canada are impacted inequitably by COVID-19 due to their increased exposure, vulnerability of environment and medical comorbidities, and their lack of access to preventive care and treatment in the context of the pandemic. In shelter environments one is unable to effectively physically distance, maintain hygiene, obtain a test, or isolate. As a result, unique strategies are required for this population to protect them and those who serve them. Recommendations are provided to reduce or prevent further negative consequences from the COVID-19 pandemic for people experiencing homelessness. These recommendations were informed by a systematic review of the literature, as well as a jurisdictional scan. Where evidence did not exist, expert consensus from key providers and those experiencing homelessness throughout Canada was included. These recommendations recognize the need for short-term interventions to mitigate the immediate risk to this community, including coordination of response, appropriate precautions and protective equipment, reducing congestion, cohorting, testing, case and contact management strategies, dealing with outbreaks, isolation centres, and immunization. Longer-term recommendations are also provided with a view to ending homelessness by addressing the root causes of homelessness and by the provision of adequate subsidized and supportive housing through a Housing First strategy. It is imperative that meaningful changes take place now in how we serve those experiencing homelessness and how we mitigate specific vulnerabilities. These recommendations call for intersectoral, collaborative engagement to work for solutions targeted towards protecting the most vulnerable within our community through both immediate actions and long-term planning to eliminate homelessness.

A bi-partitioning approach to congestion pattern recognition in a congested monocentric city

This paper proposes a bi-partitioning approach using link density data to identify an optimal restricted zone (RZ) in a congested monocentric city to assist with the implementation of area-wide congestion management strategies. A composite similarity measure is developed for each link in the network as a weighted average of a density similarity measure (ensuring homogeneity) and a distance similarity measure (ensuring connectivity and compactness). The resulting similarity matrix is fed into a graph clustering method termed symmetric nonnegative matrix factorization (SNMF) to bi-partition the network. To determine the optimal weight as a hyperparameter, we propose a hierarchical search algorithm (HSA) based on the concept of “knee” that is used to find the most significant solution from the Pareto front. The proposed approach is demonstrated on the Melbourne network using a simulation-based dynamic traffic assignment model. Results show that the methodology (i) can effectively capture the spatial variations of the congestion pattern in the network; (ii) is robust to moderate parameter changes; (iii) can be extended to the time-dependent case and hence inform when to activate the area control; and (iv) can perform relatively well in the presence of missing data. When varying the distance threshold as a design parameter, we can observe how the optimal RZ evolves in space, a feature that is critical to devising a double- or multi-layered RZ for hierarchical control purposes.

Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies

Given the extreme difficulty of estimating crash likelihoods, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. Such factors must be identified to develop crash management strategies and congestion management strategies. The objectives of this study are to identify causal factors that affect non-recurrent congestion and to propose some operational strategies for mitigating crash-induced non-recurrent traffic congestion. To achieve these objectives, a case study was conducted to identify spatiotemporal non-recurrent congestion regions using a previously developed method based on historical inductance loop detector data collected from six major freeways in Orange County, California. Based on the case study results, potentially significant factors in non-recurrent congestion were identified using the Cox proportional hazard model. Additionally, with the factors identified as significant, operational strategies were proposed for mitigating non-recurrent congestion due to freeway crashes.

Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach

Cooperation is an emergent social state related to the dynamics and complexity of road traffic and is reinforced through adaptive learning. Game theory and research in behavioural economics provide ample evidence that cooperation can efficiently solve social dilemmas similar to traffic congestion in dynamic settings. Traffic theory, asserts User Equilibrium, is both a stable and equitable, albeit inefficient, network state, which is a behavioural outcome of the selfish uncoordinated decision of drivers. In contrast, the System Optimum is an efficient network state that minimizes the total travel costs but is hard to maintain due to the inherent cost inequalities drivers will incur. In this paper, we describe how the principles of game-theory in a simple 2-player game allow the emergence of a stable system optimum through cooperation. We then investigate what happens in n-player games by applying an agent-based route-choice model. The model shows how reinforced learning and different behavioural specifications regarding agents’ cognition – selfish or cooperative - brings a simple road network from User Equilibrium towards the system optimum while preserving sufficient equity amongst drivers. The results suggest that a sufficient number of route alternations between drivers and a certain degree of altruism allow for a self-organizing formation of a fairness equilibrium that can maintain the network in the system optimum. The implications of future congestion management strategies that can be implemented with information and communication technologies are discussed.

AIDS TO NAVIGATION SYSTEMS ON INLAND WATERWAYS AS AN ELEMENT OF COMPETITIVENESS IN ULCV TRAFFIC

Estimation of Annual Average Daily Traffic (AADT) is vital for departments of transportation (DOTs) work because AADT provides the basic information for planning new road construction, determination of roadway geometry, traffic control needs, congestion management strategies and safety considerations. AADT is used to determine state wide vehicle miles travelled on all roads and are used by transportation agencies to determine compliance with federal and state rules and regulations. DOTs spend heavily to collect traffic counts on state roads, but mostly traffic counts are not available for off-system, or low volume roads. Often estimates rely on a comparison with roads that are subjectively considered to be similar. Such comparisons are inherently subject to large errors, and also may not be repeated often enough to remain current. Therefore, a better method is needed for estimating AADT for off-system roads. This research developed a technique to estimate AADT for local roads in Alabama incorporating various facets from previous studies. A model has been developed using linear regression using known AADTs and collection of socio-economic and location variables as a means to estimate the AADT. The model relied upon five independent variables: nearby population, number of households in the area, employment in the area, population to job ratio and access to major roads. The model was used to generate AADT estimates on low-volume rural, local roads for 12 counties in Alabama. The model was developed using 70 percent of the collected data and validate to the remaining 30 percent of the data. Consistent with the recent literature on AADT estimation, a log transformation was attempted to determine if any improvements were determined. The paper concludes that a straight linear regression model can be used to predict the AADT for low-volumes roadways in Alabama for future applications.

Study to Evaluate Express Toll Lanes on Florida’s Turnpike

This paper describes a study conducted by Florida’s Turnpike Enterprise to evaluate the potential for implementing congestion management strategies on the turnpike system. The study was partially funded by two grants from the FHWA through the Value Pricing Pilot Program. The Integrated Congestion Pricing Plan (ICPP) study focused on the large urban areas of the state that experience long periods of traffic congestion. These include turnpike facilities in southeast Florida, and in Tampa and Orlando. The turnpike’s ICPP is among the first in the nation to consider a large-scale program of express lanes on an existing system of toll roads. This approach allows the addition of express lanes alongside the existing toll lanes, providing drivers with a new and attractive choice for their travel needs. In this study, three turnpike widening projects, one in each of the major urban areas, were selected for express lanes. In addition, the Florida Department of Transportation established various policies for express lanes, including access points and gantry location, toll pricing, signing and design standards, and vehicle eligibility. The turnpike initiated a public outreach program to solicit customer input through market research and focus groups. An informational brochure, a project video, and a website were prepared as part of this effort. A traffic and revenue study was also completed for the selected projects. The ICPP study includes a master plan showing future regional networks of express lanes on the turnpike along with performance measures to evaluate the effectiveness of such lanes.

On Ride-Sharing: A Departure Time Choice Analysis with Latent Carpooling Preference

This paper presents a departure time choice analysis, based on the notion of a latent carpooling preference. The study is based on combined revealed preference and stated preference survey data collected on the Maryland side of the Capital Beltway (I-495). A conditional logit model is estimated to identify drivers’ choice when tolls and congestion management strategies, including high-occupancy vehicle (HOV) lanes and high-occupancy toll (HOT) lanes, are implemented. Then a latent class model accounting for heterogeneity across groups of drivers is proposed and estimated to examine the difference in behavioral preferences across groups. The latent class model reveals significant heterogeneity in drivers’ latent preference toward ride-sharing, which can potentially support ranges of transportation policy and incentive design related to congestion management strategies such as HOV/HOT lane usage.

Sampling and Analysis Methods for Estimation of Average Vehicle Occupancies

Average vehicle occupancy (AVO) has been used in various applications, including conversion of person trips and vehicle trips in travel demand modeling, evaluation and monitoring of congestion management strategies, and computation of person delays in traffic analysis. AVO data are traditionally collected in the field using the roadside windshield method and the carousel method. The accuracy of AVO estimates is of particular importance because in traffic analysis a relatively small percentage of AVO will convert to the same percentage of vehicles. This paper describes a detailed process for estimating AVOs at the individual location, facility-type, and county levels. A detailed sampling process designed to select data collection locations and dates on three different facility-types (surface streets, freeways, and toll facilities) is presented. To ensure that all areas are covered, the sampling was performed in proportion to roadway vehicle miles traveled (VMT) within individual census tracts over a one-year period. A comprehensive statistical analysis of the collected data was then demonstrated. The results showed no significant difference in the AVO estimates among the three facility-types. Also, no significant difference was found among the AVO estimates derived from facility-weighted composite values and those derived from averaging the simple means. The process presented in this paper provides a rigorous example for the collection and analysis of vehicle occupancy data.

Prototype Information System for Estimating Average Vehicle Occupancies from Traffic Accident Records

Vehicle occupancy data are increasingly being used to assess and monitor congestion management strategies. They are traditionally collected in the field with the roadside windshield method and the carousel method. Although these traditional methods have the benefit of collecting up-to-date data and can be tailored to specific application needs, they are also much more costly to conduct, especially when repeated data collection is needed for monitoring purposes. An alternative to these traditional methods is to estimate average vehicle occupancies (AVOs) from vehicle occupancy information recorded in existing traffic accident records. This method is cost-effective because it involves no new data collection, and it can provide better spatial and temporal coverage. However, this method is also known to be affected by potential biases resulting from under-and overinvolvement of certain population sectors in traffic accidents. Also, for corridor studies, the number of accident records available may not meet the minimum required sample size for the desired accuracy. An effort was made to develop a prototype information system that can make use of vehicle occupancy information in accident data to estimate AVOs. The system was designed to account for driver's age and driver's gender biases. The system also includes a number of variables that can be used for spatial and temporal AVO monitoring, including district, county, hour, week, month, year, vehicle type, facility type, area type, and crash severity. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations.

Day-to-Day Evolution of Network Flows Under Departure Time Dynamics in Commuter Decisions

Day-to-day dynamics in an urban traffic network induced by departure time dynamics in commuter decisions are investigated. This investigation relaxes some key restrictions about fixed departure time and equilibrium assumptions to analyze the stability and performance of urban traffic networks over a multiple day planning horizon. A simulation-based framework is developed to analyze day-to-day dynamics by integrating an empirically calibrated model of dynamic departure time decisions with a dynamic network assignment model. Computational experiments are used to investigate the effect of the following experimental factors: recurrent network congestion level, time-dependent loading profile, and users’ sensitivity to commute experience and trip-time volatility on network performance and reliability. The findings provide evidence of considerable day-to-day variations and stochasticity in network flows and performance, even under the assumption of fixed routes and in the absence of information. The results indicate that ( a) the network performance under departure time dynamics can deviate significantly from equilibrium; ( b) the departure time adjustment process is remarkably stable and reaches stationarity, although the departure time choices do not appear to be at equilibrium; ( c) departure time dynamics introduce significant volatility in trip times from day to day; and ( d) increasing the sensitivity of users to commute and network performance attributes (schedule delay, trip-time variability) can lead to more stable system behavior and reliability. These results have important implications for estimation of time-dependent origin–destination matrices, dynamic network analysis, and effective congestion management strategies.