Congestion Events Research Articles

Impact of COVID-19 pandemic on greenhouse gas and criteria air pollutant emissions from the San Pedro Bay Ports and future policy implications

The Ports of Los Angeles and Long Beach, collectively known as the San Pedro Bay Ports, serve as vital gateways for freight movement in the United States. The COVID-19 pandemic and other influencing factors disrupted freight movement and led to unprecedented cargo surge, vessel congestion, and increased air pollution and greenhouse gas emissions from seaport and connected freight system operations beginning in June 2020. In this study, we conducted the first comprehensive monthly assessment of the excess particulate matter, oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions due to the heightened congestion and freight transport activity from ocean-going vessels (OGVs), trucks, locomotives, and cargo handling equipment (CHE) supporting seaport operations. Excess emissions peaked in October 2021 at 23 tons of NOx per day and 2001 tons of CO2 per day. The strategic queuing system implemented in November 2021 significantly reduced the number of anchored and loitering OGVs and their emissions near the ports, even during continued high cargo throughput until Summer 2022. Looking forward, we analyzed projected emissions benefits of adopted California Air Resources Board regulations requiring cleaner and zero-emission trucks, locomotives, and CHE over the next decade. If a repeated port congestion event were to occur in 2035, NOx emissions from land-based freight transport should be lessened by more than 80%. Our study underscores the potential emissions impacts of disruptions to the freight transport network and the critical need to continue reducing its emissions in California and beyond.

Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process

ABSTRACT The urban traffic signal-controlled intersections are of great significance for solving the problem of urban road congestion. Previous research on congestion prediction mainly aggregated data at the level of road segments or traffic flow at a coarse regulated time interval. Fine-grained prediction of congestion events at the lane-level and cycle-level enables detailed a understanding of spatio-temporal dependencies, leading to congestion reduction, improved efficiency. This paper presents a Spatio-Temporal Neural Point Process (STNPP) model that combines Graph Neural Networks and Neural Temporal Point Process to predict congestion events at urban intersections. The proposed model allows for complete prediction of congestion events, including their occurrence, development, dissipation. In the process of spatial correlation modeling, graph neural networks are used to model the spatial relationships between both region and intersections. The current intersection and its upstream/downstream areas are modeled separately. To model the temporal correlations at individual intersections, we focus on a specific lane and capture the evolution of congestion events using the Neural Point Process Gated Recurrent Unit (NPPGRU), which captures the temporal granularity changes of signal-controlled cycles in congestion events. Using actual traffic speed and signal-controlled data from Hangzhou city, we validate that the proposed method achieves stable predictive performance.

Managing oversaturation in BRT corridors: A new approach of timetabling for resilience enhancement using a tailored integer L-shaped algorithm

Bus rapid transit (BRT) is a high-capacity public transport system that typically operates along urban transit corridors with dense travel demand. Maintaining the efficiency and stability of the BRT is paramount for daily transport operations. Owing to the difficulty in ensuring an exclusive right-of-way along the entire route, stochastic congestion events may occur resulting from road segments without dedicated BRT lanes. This may lead to volatility in travel time and resulting in passenger stranding, determined as common-case disruptions in this study. These common-case disruptions frequently occur in the daily operation of oversaturated BRT routes. To manage and mitigate their negative impacts, a novel timetabling problem for enhancing the resilience of a BRT system was proposed to assess the ability of the system to withstand and recover from these disruptions. We formulated the problem as a two-stage stochastic mixed-integer optimization model and designed an exact algorithm based on a tailored integer L-shaped method. We then analyzed the structural properties of our model and developed several acceleration techniques to further improve the efficiency of the algorithm. The computational results show that the proposed algorithm outperforms the commercial solver in large-scale instances and can provide near-optimal solutions when the commercial solver is invalid. Besides, external comparisons with dynamic programming also demonstrate the superiority of the proposed algorithm in solution efficiency. Compared with the benchmark timetabling problem, which aims to reduce passenger waiting time, the proposed method can efficiently reduce the duration of the oversaturation period by 35.3%.

Congestion-aware Spatio-Temporal Graph Convolutional Network-based A* Search Algorithm for Fastest Route Search

The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, and the frequent occurrence of traffic congestion may greatly increase travel time. Thus, it is challenging to achieve the above goal. To deal with it, we present a congestion-aware spatio-temporal graph convolutional network-based A* search algorithm for the task of fastest route search. We first identify a sequence of consecutive congested traffic conditions as a traffic congestion event. Then, we propose a spatio-temporal graph convolutional network that jointly models the congestion events and changing travel time to capture their complex spatio-temporal correlations, which can predict the future travel-time information of each road segment as the basis of route planning. Further, we design a path-aided neural network to achieve effective origin-destination (OD) shortest travel-time estimation by encoding the complex relationships between OD pairs and their corresponding fastest paths. Finally, the cost function in the A* algorithm is set by fusing the output results of the two components, which is used to guide the route search. Our experimental results on the two real-world datasets show the superior performance of the proposed method.

UNDERSTANDING THE TRADE-OFF IN TCP'S CUBIC WINDOWS CONGESTION: A COMPREHENSIVE ANALYSIS

This study investigates the difficulties of the CUBIC TCP algorithm, focusing on the trade-off between efficiency and fairness in window congestion control. The CUBIC approach, which was devised to optimize high-bandwidth and long-distance networks, is currently widely used as the default TCP congestion control algorithm in Linux and other systems. Our work investigates the dynamics of CUBIC's window expansion, specifically the interaction between the cubic function and the most recent congestion event. We look at how this interplay impacts the distribution of window widths, throughput, round-trip time (RTT), loss rate, and queue size under different network configurations. Keywords: TCP CUBIC, Window Congestion, Efficiency-Fairness Trade-Off, TCP’s Cubic Function.

High-Dimensional Feature Selection for Power System Congestion Event Prognosis With Enhanced Evolutionary Computation

Power system congestion event prognosis (CEP) based on multivariate time series (MTS) learning is an effective way to improve the warning abilities against risky situations. However, it is still challenging to decide which measurement sequences of system variables should be selected to train MTS learning-based CEP models, especially when facing high-dimensional candidate features from power systems. Focusing on this issue, this paper proposes a novel high-dimensional feature selection (FS) method to identify and select variables that contain beneficial data patterns for the prognosis of network congestion events. A wrapper framework embedded with multiple MTS learning models is first built to treat FS as a combinatorial optimization task and receive MTS as inputs directly without sequence compressions. Then, to improve the combinatorial optimization efficiency and FS results in the high-dimensional case, a hybrid structure called enhanced evolutionary computation (EEC) is developed by combining with information theory-based feature priority scores, which play the role of semi-guidance in the iterative search. Test results on both the real-world and synthetic datasets validate the effectiveness of the developed EEC structure and show that the selected features by the proposed FS method are more beneficial in identifying the early patterns of network congestion events, thus contributing to efficient and accurate CEP for power systems.

A deep marked graph process model for citywide traffic congestion forecasting

AbstractForecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data‐driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the occurrence time of traffic congestion events for complex signalized road networks. Traffic congestion is considered as a nonrigorous spatiotemporal extreme event. We extend the traditional point process model by integrating a specially designed spatiotemporal graph convolutional network. This hybrid strategy takes advantage of the simple form of the point process model as well as the ability of graph neural networks to emulate the evolution of congestion. Experiments on real‐world congestion data sets show that the proposed method outperforms state‐of‐the‐art baseline methods, yielding satisfactory prediction results on a large signalized road network with superior computational efficiency.

Design and Analysis of a Peak Time Estimation Framework for Vehicle Occurrences at Solar Photovoltaic and Grid-Based Battery-Swappable Charging Stations

Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion.

Congestion management in electricity distribution networks: Smart tariffs, local markets and direct control

Increasing peaks from high-power loads such as EVs and heat pumps lead to congestion of electric distribution grids. The inherent flexibility of these loads could be used to resolve congestion events. Possible options for this are smart network tariffs, market-based approaches, and direct control of flexible loads by the network operator. In most instances, these approaches are looked at in isolation, without considering potential connections and trade-offs between them. In this contribution, we aim to bridge this gap by presenting an overarching design framework for congestion management mechanisms. We classify proposals based on design choices and qualitatively discuss their benefits and risks based on an extensive literature analysis. As there is no one-size-fits-all solution, we map possible risks and discuss the pros and cons of different mechanisms for various problem types. We caution against using market-based mechanisms for local congestion, as they can be susceptible to undesired strategic behavior of market actors.

Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction

Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various temporal encoders and graph convolution networks (GCNs), called spatio-temporal graph-based neural networks, which focus on predicting dense variables such as flow, speed and demand in time snapshots, but they can hardly forecast the traffic congestion events that are sparsely distributed on the continuous time axis. In recent years, neural point process (NPP) has emerged as an appropriate framework for event prediction in continuous time scenarios. However, most conventional works about NPP cannot model the complex spatio-temporal dependencies and congestion evolution patterns. To address these limitations, we propose a spatio-temporal graph neural point process framework, named STGNPP for traffic congestion event prediction. Specifically, we first design the spatio-temporal graph learning module to fully capture the long-range spatio-temporal dependencies from the historical traffic state data along with the road network. The extracted spatio-temporal hidden representation and congestion event information are then fed into a continuous gated recurrent unit to model the congestion evolution patterns. In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism. Finally, our model simultaneously predicts the occurrence time and duration of the next congestion. Extensive experiments on two real-world datasets demonstrate that our method achieves superior performance in comparison to existing state-of-the-art approaches.

An expressway traffic congestion measurement under the influence of service areas.

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

Decision-support system for the management of truck stays at seaports

This work presents a decision-support system for predictive analysis of truck arrivals, dwell times and possible congestion situations in seaport, through a methodology that encompasses machine learning algorithms, data visualization tools and discrete-event simulation models. This decision-support system makes it possible to have a detailed planning of the arrival and transit times of trucks in the port to predict land congestion events; and to know in advance the needs in relation to services or stopovers, and how long each truck will be in the port facilities. The system also allows for the planning and sizing of waiting areas according to expected arrivals and dwell times of trucks in port, at the same time that it optimizes port value spaces, and reduces unnecessary journeys and associated emissions, by evaluating the relationship between the sea side and the demand in land accesses. This will eventually help to improve coexistence and port-city integration between urban and heavy traffic. The system has been validated in the Port of Santander (Spain) with satisfactory results in terms of operational improvement.

Latency control in service chaining using P4-based data plane programmability

Service chaining is becoming one of the most considered service deployment frameworks in the context of Network Function Virtualization (NFV) in edge and data center environments, conveniently supported by automatic connectivity configurations offered by Software Defined Networking (SDN). Current research on the topic is focusing on how to guarantee Quality of Service (QoS) in terms of guaranteed end-to-end latency for time critical services. Indeed, latency issues may depend on intra-server virtualization inefficiencies, leading to Virtual Network Function (VNF) delivery delays, or by congestion events occurring at intermediate network elements connecting VNFs. Latency control requires stateful information such as flow delay measurements at a per-packet level, typically not available at traditional SDN switches or inside the VNF. This paper proposes the adoption of SDN data plane programmability exploiting the P4 language and presents two P4 pipeline solutions, suitable for both intra-rack and inter-rack service chain deployments, to automatically check the path latency experienced by selected high priority flows, also resorting to the recent in-band telemetry applications. The programmable pipelines enforce proactive in-network functions, such as priority change or drop actions, in order to guarantee a bound SFC segment latency delivery, including both the network and the segment VNFs. The proposed solutions are implemented and evaluated in a network testbed employing programmable software switches showing their effectiveness in guaranteeing the configured end-to-end latency, and the limited effort in terms of additional processing at the P4 switch. The evaluation is carried out using the reference P4 software switch, i.e., BMv2. The aim is to validate the full P4 capabilities and the code feasibility in terms of scalability, load and resource impact and added intra-switch latency. The experimental results show the proposed approach scales with the number of forwarded flows and achieves per-segment latency control enforcement in both congested and non-congested scenarios with a very limited impact on the switch extra-latency, exploiting finer per-packet tuning of drop and priority change simply applicable through flow entry configuration. Applicability analysis on hardware switches guaranteeing line rate performance are provided.

Resilience of Delay-Sensitive Services With Transport-Layer Monitoring in SD-WAN

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s

Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling

We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.

Intent-based zero-touch service chaining layer for software-defined edge cloud networks

Edge Computing, along with Software Defined Networking and Network Function Virtualization, are causing network infrastructures to become as distributed clouds extended to the edge with services provided as dynamically established sequences of virtualized functions (i.e., dynamic service chains) thereby elastically addressing different processing requirements of application data flows. However, service operators and application developers are not inclined to deal with descriptive configuration directives to establish and operate services, especially in case of service chains. Intent-based Networking is emerging as a novel approach that simplifies network management and automates the implementation of network operations required by applications.This paper presents an intent-based zero-touch service chaining layer that provides the programmable provision of service chain paths in edge cloud networks. In addition to the dynamic and elastic deployment of data delivery services, the intent-based layer offers an automated adaptation of the service chains paths according to the application’s goals expressed in the intent to recover from sudden congestion events in the SDN network. Experiments have been carried out in an emulated network environment to show the feasibility of the approach and to evaluate the performance of the intent layer in terms of network resource usage and adaptation overhead.

A Data-Driven Method for Congestion Identification and Classification

Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion.

Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.

Deep Learning (DL)-based adaptive transport layer control in UAV Swarm Networks

This paper focuses on the congestion control issues in Unmanned Aerial Vehicle (UAV) Swarm Networks (USNs). In a USN, many network factors can cause segment loss, including dynamic swarming, high mobility, and link fading loss. With traditional transport layer protocols such as Transmission Control Protocol (TCP), these losses are interpreted as congestion events and will cause the data sending rate being decreased dramatically, therefore impacting throughput. In this paper, a learning-based adaptive network coding scheme is proposed to handle segment loss. In this scheme, a certain amount of redundancy is attached to the original data. If the segment loss is caused by random factors (such as radio interference), the lost segments are retrieved by decoding. However, if the loss is caused by congestion, the sender will retransmit the lost segments and decrease the sending rate. The coding rate is a critical factor, which should guarantee that the random loss can be retrieved by decoding while the congestion loss triggers retransmission and sending rate deduction. To achieve this goal, a Deep Learning (DL) algorithm is proposed, which comprehensively considers the wireless network conditions and dynamically optimizes the coding rate. Our experimental results show that the DL-based network coding scheme provides improved throughput and end-to-end delay compared to the TCP and general network coding schemes.

Precision Scheduled Railroading and the Need for Improved Estimates of Yard Capacity and Performance Considering Traffic Complexity

Multiple North American freight railroads have adopted concepts of Precision Scheduled Railroading (PSR) that attempt to reduce costs by maximizing train length and minimizing railcar transit time. To achieve these objectives, PSR emphasizes pre-blocking traffic and operating general-purpose trains. These changes have altered the nature of operations at many classification yards, leading to yard closures and conversions to different yard types. Difficulty in implementing PSR-inspired operating practices at yards suggests the industry requires improved estimates of classification yard performance and capacity. While volume-based approaches may be adequate when yard operations are consistent with historical experience, it is hypothesized that approaches considering overall traffic complexity will offer improved predictions when changes are also made to the number of blocks and trains assembled in the yard. An original simulation model of a classification yard pull-down process is used to investigate this hypothesis. The simulation results suggest that a combination of factors describing yard traffic complexity can be a better predictor of yard performance than volume alone. The results are also transformed into a capacity constraint that describes the interaction between the maximum allowable daily number of railcars, blocks, and trains processed by a classification yard. Better understanding of these relationships can aid practitioners and researchers in improving network blocking models and developing train plans that properly use available yard capacity under PSR and other operating plans, reducing the likelihood of future network disturbances and congestion events.