Congestion Patterns Research Articles

Transmission impedance control impacts on carbon emissions and renewable energy curtailment

Increased levels of renewable energy penetration have created new congestion patterns in recent years. Since the grid is not designed for the new patterns, the operators may need to curtail renewable energy to maintain transmission flows within acceptable limits. Transmission line impedance control, using flexible ac transmission system (FACTS) devices, has been proposed as an approach to relieve congestion in transmission systems and enhance renewable energy utilization. In this paper, we conduct a comprehensive study to provide insights on FACTS implementation’s impact on renewable energy integration and carbon emission reduction. The study considers variations in renewable energy penetration level, system loading patterns, location of renewable generation, and location of FACTS devices. Furthermore, generation mix data from prominent regional transmission organizations (RTO) are used to achieve more realistic results. Simulations studies are carried out on a modified RTS-96 system with a two-stage stochastic unit commitment model. The results show that, even though impedance control is effective in cost reduction, it has limitations in facilitating renewable energy integration in systems with prominent cheap fossil fuel power plants.

Merging and diverging operations: benchmark of three European microscopic simulation tools and comparison with analytical formulations

PurposeWe benchmark three European microscopic simulation software’s ability to reproduce congested patterns at merges and diverges by comparing their macroscopic outputs to validated analytical formulations. The capacity drop and, in the specific case of merges, the priority ratio are assessed. At the microscopic scale, the spatial distribution of lane changes at merges is examined.MethodA single reference state is built for all three simulation tools. A point-based diverge and an extended merge are reproduced in the simulation tools. Under traffic conditions ranging from free-flow to congestion, vehicles counts and vehicles trajectories are collected to compute the selected indicators, which help to conclude for the considered reference state.ResultsThe considered simulation tools correctly reproduce the merges and diverges elementary behaviors. However, their default configuration does not, entirely or partially, reproduce the traffic conditions induced by insertions and desertions as predicted by the analytical models.DiscussionThe study could be enriched by including the benchmark of other simulation tools. In addition, the networks studied are elementary and may not reflect completely the traffic situations encountered on the highways.

Acute heart failure: differential diagnosis and treatment.

Acute heart failure is a heterogeneous clinical syndrome and is the first cause of unplanned hospitalization in people >65 years. Patients with heart failure may have different clinical presentations according to clinical history, pre-existing heart disease, and pattern of intravascular congestion. A comprehensive assessment of clinical, echocardiographic, and laboratory data should aid in clinical decision-making and treatment. In some cases, a more accurate evaluation of patient haemodynamics via a pulmonary artery catheter may be necessary to undertake and guide escalation and de-escalation of therapy, especially when clinical, echo, and laboratory data are inconclusive or in the presence of right ventricular dysfunction. Similarly, a pulmonary artery catheter may be useful in patients with cardiogenic shock undergoing mechanical circulatory support. With the subsequent de-escalation of therapy and haemodynamic stabilization, the implementation of guideline-directed medical therapy should be pursued to reduce the risk of subsequent heart failure hospitalization and death, paying particular attention to the recognition and treatment of residual congestion.

Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

The effect of the built environment on spatial-temporal pattern of traffic congestion in a satellite city in emerging economies

Traffic congestion has been widely perceived as an inevitable byproduct in the process of global urbanization, leading to significant negative economic and environmental impacts. Existing studies have attached significant importance to revealing the interaction between traffic congestion patterns and built environment features in many metropolises with little attention, if any, paid to their counterparts of satellite cities (SC). For being aware that developing SCs has been a main trend to address many urban issues of metropolises in the rapid urbanization process of emerging economies, this study makes one of the first research attempts to investigate how traffic congestion temporally and spatially evolves with the built environment characterized by land use and transportation network features in Foshan, a typical SC of Guangzhou in China by using valuable hyperlocal travel data. The results show that while weekdays experience traffic peaks in both morning and evening, weekends or holidays generally only have the evening peak. The traffic congestion in Foshan during the pre-weekend is found much heavier compared with normal weekdays and weekends with a long-lasting effect possibly due to Foshan's role as a SC of Guangzhou. Additionally, the rising traffic congestion in a SC associated with company land use during the peak hours can be partly offset by the increasing supply of public transit, suggesting urban planners increase the density of public transit where there is a denser distribution of companies. This study provides new knowledge on travel behaviors in SCs in emerging economies, supplying urban governors with new insights for improving their traffic conditions generally.

Traffic congestion patterns in the urban road network: (Dammam metropolitan area)

Traffic congestion is a significant problem affecting the sustainable development of urban traffic. It is important to analyze the congestion and forecast future traffic models to prevent traffic congestion. This study is conducted with the main aim to determine the most congested area of the road network and determine how they are related to the demand of the drivers. This study uses the Floating Car Data method to find the traffic congestion and the degree to which observed congestion clusters are a meaningful representation of congestion patterns within a more extensive urban road network. Statistical calculations have been carried out to determine the correlation between clusters based on which conclusions are drawn. Findings have shown that this approach can effectively identify the traffic congestion patterns in the urban road network. The analyses of the traffic congestion behaviour have shown that congestion is more severe and widespread in evening rush hours than morning. Overall, the results can be used to develop a framework to describe potential traffic issues and a system for predicting congestion.

Integrated Real-Time Interchange Control System for Freeway Ramps and the Connecting Arterial

Contending with various recurrent congestion patterns on major freeway corridors has long been a prioritized task of the transportation community. Most of these patterns take place on a freeway interchange’s on-ramp and off-ramp segments where both exiting and merging flows often trigger a series of lane changes, resulting in significant freeway speed reduction and propagation of traffic queues to neighboring surface streets. To concurrently mitigate freeway traffic congestion and minimize any negative impacts on the nearby local arterials, this study has developed an Integrated Real-time Interchange Control (IRIC) system, which maximizes the benefits of both freeway and arterial users by fusing an integrated off-ramp signal control (IOSC) module, an off-ramp queue impact (OQI) model, a lane group-based (LGB) traffic model, and a ramp metering control strategy as well as arterial signal optimization modules. The proposed system with both ramp metering functions and local arterial signal design capabilities can determine whether to implement system-wide optimization or merely the ramp metering based on real-time detected traffic conditions. The evaluation results from experiments have confirmed the effectiveness of the developed real-time interchange control system, especially with respect to its ability of preventing ramp queue overflows to the neighboring arterials and minimizing the off-ramp spillback onto the freeway mainline.

Identifying Spatio-Temporal Patterns of Traffic Congestion Using Data Obtained from Google Maps Service Traffic Image

تحلیل وضعیت ترافیکی و پیشنهاد روش‌های مدیریت جریان ترافیک نقش اساسی در ارزیابی عملکرد بسیاری از سیستم‌های حمل‌ونقلی ایفا می‌کند. در بین روش‌های جمع‌آوری داده‌های ترافیکی، رویکردهای مبتنی‌بر فنّاوری‌های نوین که امکان گرد‌آوری حجم بسیاری از داده‌های پویای زمانی‌ـ مکانی را فراهم می‌آورند و استخراج روندها و الگوها را تسهیل می‌کنند اهمیت بسیاری دارند. در این پژوهش، تهران به‌منزلة پایتخت ایران، با ویژگی‌های اقتصادی و اجتماعی خاصی که دارد و تنوع سفرها که به وضعیت ترافیکی متغیر منجر می‌شود، مطالعه شده است. داده‌های حاصل از پردازش رقومی تصاویر ترافیکی به‌دست‌آمده از سرویس نقشة گوگل در بازة زمانی پیوستة یک‌ماهه‌ای (هفدهم فروردین تا هفدهم اردیبهشت 1398)، نخستین بار به‌منظور ارزیابی روند تغییرات میانگین ازدحام ترافیکی در سطح نواحی منطقة مطالعاتی، به‌کار رفته است. پس از استخراج داده‌های اولیه و با توجه به تغییر الگوی سفرها و در نتیجه، میزان ازدحام ترافیکی، شاخص ازدحام ترافیکی (CI) به‌تفکیک در روزهای کاری و غیرکاری، محاسبه شد و به مرکز نواحی 117‌گانة شهر تهران اختصاص یافت. با استفاده از تحلیل‌های توصیفی روی کلان‌داده‌های مورد بررسی، ساعات اوج ازدحام ترافیکی در بازة زمانی مورد مطالعه استخراج شد. سپس شاخص Getis Ord، نواحی پرازدحام منطقة مطالعاتی را براساس ارزیابی خوشه‌های مکانی، مشخص کرد. همچنین ارتباط زمانی بین مقادیر ازدحادم ترافیکی، در برش‌های زمانی متفاوت طی کل بازة زمانی مورد مطالعه، با استفاده از آزمون آماری کروسکال والیس ارزیابی شد و فرض صفر مبتنی‌بر همبستگی بین مقادیر میانگین ازدحام و در نتیجه، همبستگی زمانی بین مقادیر تأیید شد. با استفاده از تحلیل‌های پوششی نقشه‌های ترافیکی نیز، خوشه‌های ترافیکی پرازدحام در سطح اطمینان 90%، در اوج صبح و عصر، به‌تفکیک روزهای کاری و غیرکاری استخراج شد. نتایج این پژوهش می‌تواند در اصلاح و بازنگری محدوده‌های ترافیکی مؤثر باشد و همچنین به تحلیل‌های مرتبط با آلودگی هوا، مطالعات در زمینة قیمت‌گذاری معابر و بررسی روند شکل‌گیری و انتشار گلوگاه‌های ترافیکی در بازه‌های زمانی دلخواه، یاری برساند.

A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments

Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.

Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis.

Identifying Traffic Congestion Patterns of Urban Road Network Based on Traffic Performance Index

Urban congestion has become a global problem with urbanization and motorization. The analysis of time-varying traffic congestion patterns is necessary to formulate effective management strategies. The existing studies have focused on traffic flow patterns developed by the volume, speed and density of road sections in a limited district, while the long-time analysis of congestion patterns of the macro road network at the city level is inadequate. This paper aims to recognize traffic congestion patterns of the urban road network based on the traffic performance index (TPI) of 699 days in 2018, 2019 and 2021 in Beijing. The self-organizing maps (SOM) method improved by an automatic clustering number determination algorithm is proposed to cluster congestion patterns based on time-varying TPI. The traffic congestion of the macro road network is clustered into Mondays, Fridays, ordinary weekdays, congested weekdays, weekdays of winter and summer vacation, Saturdays, Sundays and festivals patterns. Patterns of Mondays and congested weekdays have a prominent morning peak, while patterns of Fridays, ordinary weekdays, and weekdays of winter and summer vacation have a prominent evening peak. Saturdays, Sundays and festivals are less congested than weekday patterns. It is verified that the SOM method proposed in this paper clusters traffic congestion into more detailed and accurate patterns, and it is applicable to TPI clustering in different years. The degree of congestion in 2021 increases by 7.15% in peak hours and decreases by 7.50% in off-peak hours compared with that in 2019 due to COVID-19. This method is helpful for traffic management in terms of making decisions according to different congestion patterns.

Land Use Impacts on Traffic Congestion Patterns: A Tale of a Northwestern Chinese City

Traffic congestion is a contemporary urban issue plaguing transportation planners, land developers, policy-makers, and citizens. While many studies have investigated the impact of built environments on traffic behavior in large metropolises on a regional scale, little attention has been paid to smaller urban areas, in China’s context, especially on a neighborhood level. This study investigates the spatial–temporal pattern of traffic congestion in a small-scale city, Xining, in China. By applying multivariate least-square regression analysis to social-sensing hyperlocal travel data, the results indicate that Xining is experiencing morning and evening traffic peaks on the weekdays and pre-weekends and only the evening peak during the weekends or holidays. The pre-weekend congestion is significantly worse than on a normal weekday, implying that stronger measures to consolidate traffic management should be implemented during this time. Educational land use and residential areas were found to contribute significantly to traffic congestion in Xining, and their combined effects tend to exacerbate the situation. The study furthers the understanding of traffic congestion in small urban areas, providing urban planners and policy-makers with new insights to formulate evidence-based strategies for mitigating traffic congestion.

Travel demand matrix estimation for strategic road traffic assignment models with strict capacity constraints and residual queues

This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters.The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function.Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice.

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

The market for on-demand mobility services is growing worldwide. These services include, for example, ride-hailing, ride-sharing, and car-sharing. Large-scale fleets of such services collect GPS trajectory (probe vehicle) data constantly everywhere in the network. At a certain penetration rate, this data becomes representative of the entire road network. It can give valuable insights into traffic dynamics and the evolution of congestion. In this paper, we use such GPS trajectory data from Chengdu, China, to investigate the stability and recurrence of macroscopic traffic patterns. Using the two-fluid theory, we find that the two-fluid coefficients are robust on between-day variation, not only supporting the theory itself but also emphasizing that the general evolution of traffic is a robust pattern. We investigate the deviations from the model using time series analysis of the residuals of the two-fluid model. Here, we find evidence for daily and weekly seasonality in the residuals, indicating that congestion patterns are convincingly recurring. These patterns can be used for network-wide traffic state prediction. We conclude that GPS trajectory data from large on-demand mobility fleets is a promising data source for observing traffic patterns in urban road networks once the data becomes representative.

The co-determination of home and workplace relocation durations using survival copula analysis

The dynamics of household behavior regarding home and occupation relocation is a critical consideration in multiple fields, including transport planning, as it influences the majority of daily trips and urban congestion patterns. While a causal dependence between home and occupation relocation has been widely investigated, the simultaneous dependence of relocation durations has not. Here, households' residential and workplace relocation durations are simultaneously modeled using Bayesian, copula-based, multivariate, parametric survival analysis. Three copula formulations are compared. Nested and independent model structures are proposed for modeling occupation durations in households with more than one employed member. A comprehensive, retrospectively revealed, life-course dataset with a focus on the homeownership, occupation, education, and vehicle ownership statuses of households was collected from Chicago, USA, and Sydney, Australia. The impacts of sociodemographic, home, and occupational attributes were mostly consistent in both cities, demonstrating similarities in the behaviors of their metropolitan residents. The results reveal that, contrary to expectations, home and workplace relocation durations are not necessarily strongly correlated. This result prompts many valuable research directions in this field.

Data-driven traffic congestion patterns analysis: a case of Beijing

Data-driven traffic congestion patterns analysis: a case of Beijing

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.

Operational Characteristics of Mixed-Autonomy Traffic Flow on the Freeway With On- and Off-Ramps and Weaving Sections: An RL-Based Approach

As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.

Partitioning of urban networks with polycentric congestion pattern for traffic management policies: Identifying protected networks

AbstractLarge‐scale urban networks are usually loaded heterogeneously with a polycentric congestion pattern, resulting in a highly scattered network macroscopic fundamental diagram (NMFD or MFD). Thus, researchers have tried to partition city networks into homogeneous subzones. In this study, a six‐step partitioning algorithm is proposed. The framework allows the NMFD information to be used. It combines traffic variables and geographic connectivity to obtain partitions. The framework includes graph definition, data preprocessing, feature handling, clusters and partitions identification, and boundaries reshaping. Tests on a simplified grid network and the city of the Melbourne road network demonstrate the suitability of the framework for characterizing the traffic states by the partitions. The framework on the missing data scenarios and monocentric and polycentric traffic concentrations scenarios, as well as applying multiple data types, has been challenged. Thereafter, parameter impact analysis demonstrates that manipulation of the parameters enables users to find the desired partitions. Last but not least, a comparison with an existing method also implies the uniqueness and efficiency of the developed framework.

Research on the impact of entry points on urban arterial roads in the framework of Kerner’s three-phase traffic theory

Urban arterial roads, characterized by high traffic volumes and driveway densities, are prone to congestion and accidents due to external disturbances. The disturbance from the driveways (entry points) between arterials and adjacent properties has aroused wide concern in recent years. In this paper, based on the well-known KKSW (Kerner–Klenov–Schreckenberg–Wolf) CA (cellular automaton) model, an adapted KKSW-EP (entry points) CA model is proposed to study the influence of entry points on urban arterial roads. Traffic flow patterns under several entrance inflow rates and different locations of entry points are found by simulation. Besides, explanations for these patterns are provided in the framework of Kerner’s three-phase traffic theory. Moreover, the impact of entry points on the safety of urban arterial roads is also assessed. The results show that the congested patterns on the two lanes upstream of the entry point are not the same because they are caused by different types of bottlenecks and the right lane suffers from greater bottleneck strength. The safety assessment shows that severe conflicts are associated with the speed difference between vehicles caused by entry points and wide moving jams.