Real-time Congestion Research Articles

Impact of In-Cab Alerts on Connected Truck Speed Reductions in Indiana

Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors in Indiana from April–June 2024. Approximately 20,000 alerts were analyzed, with 92% being Congestion alerts and 8% being Dangerous Slowdown alerts. Observations showed that 15% of trucks lowered their speeds by at least 5 mph 30 s after receiving a Congestion alert, while 21% of trucks reduced their speeds by at least 5 mph 30 s after receiving a Dangerous Slowdown alert. The analysis also showed that a majority of Congestion alerted trucks encountered slow-speed traffic about 3 min after receiving an alert, while a majority of Dangerous Slowdown alerted drivers had traveled through the zone of slow speeds 2 min after receiving the alert. Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study reports that 43% of trucks that received Dangerous Slowdown alerts never reduced their speed below 45 mph. The paper concludes that it is important to converge on a shared vision for these performance measures so that public agencies, in-cab alert providers, and trucking companies can agilely improve these systems and increase driver confidence in the alerts.

The Value of Urgency: Evidence from Real-Time Congestion Pricing

The Value of Urgency: Evidence from Real-Time Congestion Pricing

A passenger flow spatial–temporal distribution model for a passenger transit hub considering node queuing

Passenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate ‘human-facility-environment’ complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels.

Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks

In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism based on backpressure feedback (BFCC), which is designed with the primary goal of switch-to-switch congestion control to resolve congestion in a one-hop RTT. This approach utilizes a programmable data plane to continuously monitor network congestion in real time and identify real-congested flows. In addition, it employs targeted flow control through backpressure feedback. We validate the feasibility of this mechanism on BMV2, a programmable virtual switch based on programming protocol-independent packet processors (P4). Simulation results demonstrate that BFCC greatly enhances flow completion times (FCTs) compared to other end-to-end congestion control mechanisms. It achieves 1.2–2× faster average completion times than other mechanisms.

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Real Time Congestion Control and Simulation of Large-Scale Video Conference System Based on GRU Algorithm

The comprehensive performance and user experience of large-scale distributed video conference systems are constrained by network traffic, and conventional network services are difficult to ensure the stability and reliability of the above data transmission activities. This article combines conference video transmission and congestion control related technologies to propose a conference video traffic prediction model based on the large neural network GRU algorithm, and constructs a large-scale distributed video conference transmission congestion control mode on this basis. The simulation experiments on the real-time congestion control model of research results show that it effectively avoids system fluctuations and significantly improves the user experience, providing a feasible method for improving the comprehensive performance of large-scale distributed video conference systems.

Data-Driven Real-Time Congestion Forecasting and Relief With High Renewable Energy Penetration

Data-Driven Real-Time Congestion Forecasting and Relief With High Renewable Energy Penetration

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.

Robust [formula omitted] control for line power flow tracking in power systems

Traditionally, the problem of congestion arising in power system networks due to various contingencies and varying renewable generation is addressed by optimal power flow-based methods, once in every 5–30 min. As these techniques are performed for a block period of time using forecasted data, they are not effective in real-time congestion mitigation through corrective actions. This paper proposes a controller-based approach for real-time congestion management through line power flow regulation. We consider a robust H∞ power flow tracking control strategy to regulate the line power flows to the specified limit, while attenuating the effect of disturbances on the system. Using synchrophasor measurements from the lines, the proposed controller can detect line overloading and automatically re-dispatch the selected generators and battery energy storage systems to maintain power flow within the desired limit. The robust line power flow tracking problem with disturbance rejection is formulated as a dynamic game between the controller and disturbance entities, and robust static output feedback H∞ controller gains are obtained via an iterative algorithm. The efficacy of the proposed controller in regulating line power flow and mitigating congestion is verified by extensive simulations on a 16-machine 68-bus test system and Kundur’s 4-machine 2-area test system. It is shown that the designed controller can regulate line power flows within 40–60 s, which is fast enough to avoid overheating of transmission lines. Furthermore, the proposed strategy is simple from the perspective of design and implementation, and outperforms the existing congestion management techniques.

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

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

Toward real-time congestion measurement of passenger flow on platform screen doors based on surveillance videos analysis

Most metro stations lack automatic methods for monitoring the congestion and order of alighting and boarding passengers at each door on the platforms. Existing methods attempt to analyze historical data and predict the potential congestion but perform poorly on the burst of passengers. Furthermore, the number of passengers is the most direct indicator, but it is rough and ignores the interaction between passengers. To precisely measure the congestion of passenger flow, this paper proposes a real-time congestion measurement of passenger flow based on surveillance video analysis. First, a passengers trajectory extracting module is proposed to extract the passengers’ moving tracks from the surveillance videos. Then, the passenger flow of a train gate is treated as a bidirectional flow that goes through a bottleneck. The congestion is measured from three aspects and calculated in entropy: (1) position entropy: considering the effect of counterflow passengers (2) speed entropy: considering the effect of speed changes between passengers, and (3) angle entropy: considering the effect of the direction of passenger movement. Moreover, to improve the sensitivity of the entropy to the crowding behavior of bidirectional flows, the K-Means clustering method is applied to the speed entropy calculation process. Finally, an entropy for measuring the congestion and order of alighting and boarding passengers can be obtained. The proposed entropy measurement method is applied to the real-world data of a Guangzhou Metro station and effectively reflects the actual congestion situation.

Market-based Real Time Congestion Management in a Smart Grid Considering Reconfiguration and Switching Cost

Network Real-Time Congestion (RTC) is a bottleneck that limits energy transfer from the generation units or up-grid to the loads. Some factors, such as intermittent generation of renewable resources and forced outages of generating units and load forecasting errors, can lead to Real-Time Congestion Management (RTCM) in a smart grid network. RTCM is a set of methods to eliminate congestion in real-time. To implement RTCM, some approaches can be employed, including network reconfiguration by Remote Control Switches (RCS), load shedding generation and up-grid power rescheduling. In this paper, a two-stage programming model is proposed to find the optimal solution for RTCM using the integration of reconfiguration and market-based approaches. Therefore, following the occurrence of congestion, at the first stage, microgrid central controller (MGCC) or central energy manager implements reconfiguration as the lowest-cost approach to mitigating RTC. The Soccer League (SL) algorithm is employed at the first stage to find the optimal network topology. Subsequently, based on the results obtained from the first stage, a programming model is applied at the second stage to completely eliminate the RTC. The proposed model minimizes a weighted objective function that includes the generation and up-grid rescheduling cost, load shedding cost, switching cost, and congestion clearing time. In order to model switching costs, a new index is defined to prevent risky switching and the depreciation caused by frequent switching. This index is determined based on the critical locations in the network and the age of RCSs. The numerical results demonstrate the efficacy of the proposed model.

Evacuation Time Estimation Model in Large Buildings Based on Individual Characteristics and Real-Time Congestion Situation of Evacuation Exit

Fire is one of the most common and harmful disasters in real life. In 2021, firefighting teams in China reported 748,000 fires, resulting in 1987 deaths, 2225 injuries and CNY 6.75 billion of direct property losses, which account for 0.05‰ of GDP. Scientific and accurate estimation of evacuation time can provide decision support for intelligent fire evacuation. This paper aims to effectively improve the evacuation efficiency of people in large buildings, especially for a scenario with intricate evacuation passages. There are many factors that make a difference in evacuation time, such as individual behavior, occupant density, exit width, and so on. The people distribution density is introduced to effectively assess the impact of unstable pedestrian flow and unbalanced distribution in the process of evacuation. The verification results show that there is a strong positive correlation between people distribution density and evacuation time. Combining the people distribution density with many other factors, the training dataset is built by Pathfinder to learn the relationship between evacuation time and influencing factors. Finally, an evacuation time prediction model is established to estimate the consumption time that occupants spend on moving in the evacuation process based on stacking integration. The model can assist occupants in choosing different channels for evacuation in advance. After testing, the average error between the predicted evacuation consumption time and the reference time is 3.63 s. The result illustrates that the model can accurately predict the time consumed in the process of evacuation.

Multimodal routing framework for urban environments considering real-time air quality and congestion

The travel choices of citizens can be based on distance, time, exposure, emissions, etc., but the current route planning systems are mostly based only on distance and time. This study develops a route planning framework by integrating the real-time congestion pattern and air pollution levels in the GraphHopper multi-modal routing engine. This study showcases the application for Delhi and Bangalore, India. However, it is transferable to any other city in the world. The route planning algorithms provide alternatives for the fastest, shortest, LEAP (least exposure to air pollution), and the balanced route, considering four travel modes: car, motorbike, bicycle, and foot (pedestrian). GraphHopper libraries involve custom weightings for each of the constraints for route choices, and these weightings are modified to find the optimal route as per users’ requirements. The study’s novelty lies in integrating real-time congestion patterns and air pollution levels in a multi-modal routing engine. The preference for low travel time and air pollution levels can be altered by adjusting factors in balanced route to suit travelers. Using different travel modes on a specific route found that the exposure reduction ranged from 42.73 % to 71.50 % for fastest to balanced routes and 64.52 % to 82.71 % for fastest to LEAP routes in Delhi. Higher exposure reduction percentages are observed in Bangalore. Using the routing engine can help travelers reduce their exposure by altering travel choices. The interface of the routing engine is made user-friendly and intuitive.

Using an Interpretable Machine Learning Framework to Understand the Relationship of Mobility and Reliability Indices on Truck Drivers’ Route Choices

This paper explores the relationships between travel time index and planning time index, two proven indices used to measure real-time congestion and travel time reliability, and their impacts on truck drivers’ route choices through training of a predictive model based on a machine learning algorithm—eXtreme Gradient Boost (XGBoost). Moreover, this study adopts an interpretable machine learning framework called SHapley Additive ExPlanation (SHAP) in the predictive model to reveal the insights usually hidden inside the machine learning “black box.” The predictive model is trained through a truck trajectory dataset provided by the Maryland Department of Transportation State Highway Administration and INRIX. The classical logistic regression model is adopted as the baseline model. The results show the XGBoost model can better handle nonlinearity and provide more reliable predictions. Through the SHAP framework, the results indicate that mobility and reliability indices and total trip time nonlinearly influence route choices. Truck drivers are more sensitive to real-time congestion information and reliability information when the differences in mobility and reliability indices on candidate routes reach certain thresholds. Moreover, the interaction study on trip time and mobility index shows that truck drivers are more sensitive to real-time congestion information if the candidate routes’ travel time is a larger portion of the total trip time.

Optimal Distributed Frequency and Voltage Control for Zonal Electricity Markets

Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. Based on a dynamic network model that takes line losses and power exchange with adjacent price zones into account, distributed continuous-time control laws are derived which require only neighbor-to-neighbor communication. Application to a real-time congestion management strategy illustrates how spatially and temporally differentiated prices enable grid-supportive operation of the participants without interventions by a superordinate control authority. Effectiveness of different zonal pricing concepts compared to an isolated grid operation is demonstrated through simulations on the IEEE-57 bus system.

A Microscopic Platoon Stability Model Using Vehicle-to-Vehicle Communication

With Vehicle-to-Vehicle (V2V) communication capability, vehicle platoon on the highway helps to reduce traffic congestion. However, the dynamic nature of vehicles imposes challenges on the V2V-based platoon management. In this paper, by considering the characteristics of a Vehicular Ad-hoc Network (VANET), a microscopic platoon management scheme is proposed to deal with three basic dynamic platoon maneuvers, namely merging, splitting, and speed-change. The congestion detection feature of VANET is used as a scale for platoon merging, splitting, and speed selection. Real-time congestion is detected if the number of vehicles in a given road segment exceeds the occupancy rate or the time headway is less than the thresholds. In the proposed platoon management scheme, platoon maintenance is triggered in congestion detection. Finally, a VANET-based platoon platform is built by using Network Simulator Version 2 (NS2) network simulation to assess the performance over some real road traces generated by Simulation of Urban MObility (SUMO). It is shown that V2V-based dynamic vehicle platoon management provides an inexpensive technique to cope with the dynamic platoon management requirement.

Day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles

Abstract The day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles is studied to effectively solve the day-ahead and real-time congestion scheduling problem of distribution network. The charge adjustment strategy of charge and discharge service fee is established to realize the double response regulation between the distribution system’s scheduling organization and the charging pile operator; considering the adjustment strategy of charging service fee, a day-ahead congestion scheduling model is established with the goal of minimizing the charging cost of electric vehicles; based on the day-ahead congestion scheduling, a real-time congestion scheduling model is established to minimize the regional power fluctuation; the day-ahead and real-time congestion scheduling model is solved by differential evolution algorithm, and the optimal scheduling scheme is obtained. Experiments show that this method can reduce the line load rate of distribution network, avoid re-congestion, reduce the congestion scheduling cost and improve the security and economy of power grid operation.

Smarter and Safer City Using Internet of Things

Internet of Things (IoT) can make a significant contribution in making cities greener, more secure, and more viable. This paper proposes 4 subsystems that help make cities smarter. First is a Smart Super Market, which enables customers to acquire product information with smart storage details and also makes the checkout process hassle-free. A waste collection system, in light of offering intelligence to garbage canisters, using IoT is also introduced. A Smart Streetlight and Traffic Signals Control System is presented which has two parts. The first part automates streetlight control using sensors that detect daylighting and other weather conditions. The second part presents traffic signals controlled by sensing the real-time congestion on the roads. A Women Safety System which helps women feel safer by notifying the local authorities in alarming situations is also introduced.

Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data

ABSTRACT Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.