Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies

With the rapid development of urban transport and the sharp increase in vehicle population, traffic accidents form one of the most important causes of urban traffic congestion other than the imbalance between traffic supply and demand. Traffic congestion causes severe problems, such as environment contamination and energy dissipation. Therefore, it would be useful to analyze the congestion propagation characteristics after traffic accidents. Numerical analysis and computer simulation were two of the typical methods used at present to study the traffic congestion propagation properties. The latter was more widespread as it is more consistent with the actual traffic flow and more visual than the former. In this paper, an improved cellular automata (CA) model was presented to analyze traffic congestion propagation properties and to evaluate control strategies. In order to apply them to urban traffic flow simulation, the CA models have been improved and expanded on. Computer simulations were built for congestion not only extending to the upstream intersection, but also the upstream intersection and the entire road network, respectively. Congestion propagation characteristics after road traffic accidents were obtained, and controls of different severities and durations were analyzed. The results provide the theoretical foundation and practical means for the control of congestion.

Traffic congestion in urban areas has become a critical issue in the world. As the limits of road topology and the influence of residents' commuting periodic patterns, the same part of the road network usually gets congested, and there are similar propagation paths when congestion occurs in an area. This paper aims to model the congestion propagation phenomenon and further discover the congestion propagation paths that frequently appeared. To this end, we propose an algorithm which analyzes the correlations between congestions on different road segments and constructs a congestion propagation graph to reflect the dynamic process of congestions. The traffic flow entropy is novelty considered to identify the potential essential road segment in traffic congestions. We adopt the Jaccard correlation to estimate the parameters employed. The experiments with the road network in Beijing, China and real-world GPS trajectory data indicate that our approach can reveal congestion propagation patterns and the dynamic process of congestion propagation.

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

Urban traffic network congestion propagation prediction model: A case of non-recurrent congestion

Traffic congestion propagation inference using dynamic Bayesian graph convolution network

Analysis and control of saddle-node bifurcation based on continuum model with headway fluctuation

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

With the rapid development of society, urban traffic congestion has gradually become an important social problem that many cities need to solve. For Chongqing, traffic congestion not only affects residents’ normal travel but also brings more serious environmental pollution. Aiming at the problem of urban traffic congestion and automobile exhaust pollution, this paper adopts the system dynamics method to establish a model for studying urban traffic congestion system from the perspectives of private cars, trucks, and public transportation. First, we determine city motor vehicle trips as an indicator of the degree of traffic congestion in this paper. Second, we analyze the causal relationship between the growth of private cars, the travel of trucks, public transportation, population, and other factors and then build a model and test the stability of the model. Then, we add some practical policies to the model for policy analysis. Finally, it is concluded that the private car restriction policy is effective in controlling the amount of private car travel, and the purchase restriction policy controls the growth of the number of private cars from the root cause, but the development of public transportation is the most effective treatment measure in the long run.

In this paper, we have made a comprehensive study about the key technologies including applying wireless sensor networks to the traffic monitoring network, its traffic flow forecast based on gray forecasting model and traffic congestion control. According to the features that wireless sensor networks have no space constraints, flexible distribution, mobile convenience and quick reaction, we present a scheme that uses wireless sensor networks to monitor city transport vehicles and have designed a traffic monitoring system based on wireless sensor network that is applicable to all types of city environment. With the system, we can monitor the important roads that are easily blocked and find out the time changing law of traffic congestion, and then put the focus on monitoring them in order to greatly reduce the investment and achieve high efficiency. As far as the traffic flow forecasting methods concerned, we use Adaptive GM (1, 1) Model which have a real-time rolling forecast for city traffic and have a better forecast results. Because of the fewer study about the traffic congestion control in the current academia, we make a deep study about the traffic congestion control issues in this article. Learn from mature congestion control algorithm in computer network, we have designed an algorithm of traffic flow congestion control and scheduling for traffic network, which is called TRED. We have used it for real-time traffic scheduling and have opened up a new way to study and solve traffic congestion control problems.

Urban expressways serve as the main arteries of urban transportation. Congestion and traffic disruptions on expressways can easily lead to the paralysis of the entire regional transportation system. Accurately understanding the patterns of congestion formation and influencing factors on expressways is beneficial for improving traffic efficiency and reducing travel costs. This study takes typical sections of expressway diverging and exit sections as examples, introducing traffic data provided by navigation systems to explore the potential influencing factors and formation processes of urban expressway traffic congestion. This study explores the effects of operating conditions, control facilities, road properties, weather, and other factors on the smoothness of exit sections based on navigation and field survey data. The traffic congestion index is used as an indicator of congestion degree to evaluate the smoothness of typical area of urban expressways exit sections and the overall safety of urban roads. A structural equation model is used to construct a traffic congestion impact model. The results show that traffic facilities (β = 0.462, p < 0.001), road conditions (β = 0.177, p < 0.001), road location (β = 0.129, p < 0.001), spatiotemporal characteristics (time of day: β = 0.295, p < 0.001; day of week: β = −0.105, p < 0.001), environment (β = 0.021, p < 0.001), and driving behavior (β = 0.326, p < 0.001) have a significant impact on traffic congestion. And driving behavior can be used as an intermediate variable to affect the relationship between transportation facilities, road conditions, road location, spatiotemporal characteristics, environment, and traffic congestion. The research contributes to a precise understanding of the formation patterns and influencing factors of urban expressway traffic congestion, laying the groundwork for the adoption of targeted traffic management measures to improve traffic flow efficiency and reduce accident occurrences.

In the context of large cities, the growth in traffic congestion has become a major problem. Static control systems may block emergency vehicles due to traffic congestion. The goal of detecting, forecasting, and reducing traffic congestion is to improve the level of service provided by the transportation system. One of the main issues that automatic way of traffic management plan will address is the current urban traffic congestion. Due to its capability to cope with dynamic behavior over time and with vast numbers of parameters in massive data, neural networks (NN) and machine-learning (ML) techniques are being employed more and more to tackle real-world issues, surpassing analytical and statistical methods. Therefore, deep learning is becoming more relevant for these jobs as access to bigger datasets at greater resolution increases. This study focuses on a critical assessment of the state of the art for the use of Artificial Intelligence in this specific field of Intelligent Transportation Systems. The literature that is now accessible uses a variety of methods to identify and dassify traffic congestion. The conclusions obtained from a review of recent articles using two taxonomic criteria are finally presented.

Hopf bifurcation control for the traffic flow model considering the tail light effect

In order to alleviate flight delay it is important to understand how air traffic congestion evolves or propagates. In this context, this paper focusses on the aggravation of airport congestion by the accumulation of delayed departure flights. We start by applying a heterogeneous network model that takes congestion connection/degree into consideration to predict departure congestion clusters. This is on the basis of the fact that, from a micro perspective, the connection between congestion and discrete clusters can be embodied in models. However, the results show prediction to be of high accuracy and time consuming due to the complexities in capturing the connection in congested flights. The problem of being highly time consuming is resolved in this paper by improving the models by stages. Stage partitioning based on the variation of delay clusters is similar to the typical infectious cycle. For heterogeneous networks the model can describe the congestion propagation and its causes at the different stages of operation. If the connection between flights is homogeneous, the model can describe a more indicative process or trend of congestion propagation. In particular, for single source congestion, the simplified multistage models enable short-term prediction to be fast. Furthermore, for the controllers, the accuracy of prediction using simplified models can be acceptable and the speed on the prediction is significantly increased. The simplified models can help controllers to understand congestion propagation characteristics at different stages of operation, make a fast and short-term prediction of congestion clusters, and facilitate the formulation of traffic control strategies.

Urban traffic congestion and carbon emissions pose significant challenges to the sustainable development of megacities. Traditional single-policy interventions often fail to simultaneously mitigate congestion and reduce emissions effectively. This study employs a system dynamics approach to construct a multidimensional dynamic model that analyzes the feedback mechanisms and dynamic interactions of policy variables within the urban traffic system. Furthermore, a TOPSIS multi-criteria decision-making framework is integrated to quantitatively evaluate the overall effectiveness of multiple policy combinations, exploring optimization pathways for achieving synergistic governance. Using Shanghai’s traffic system as a case study, simulation analyses under six policy scenarios reveal significant discrepancies in short- and long-term policy performance. Results demonstrate that traffic congestion, carbon emissions, and environmental pollution are tightly coupled, forming a non-coordinated feedback loop that challenges single-policy solutions. For example, the “two-license-plate restriction” policy reduces traffic congestion by 2.72%, carbon emissions by 10.37%, and pollution by 2.47% compared to the baseline scenario, achieving the highest TOPSIS score of 0.68. The “new energy vehicle promotion” policy significantly contributes to long-term emission reduction; however, its overall effectiveness score is relatively low at 0.5. These findings underscore the need for a systematic approach to urban traffic governance, providing actionable insights for balancing short-term effectiveness and long-term sustainability through dynamic policy integration.

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.