V2X-Based Traffic Congestion Recognition and Avoidance

Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the adaptive proportional integral rate controller, a congestion control algorithm designed for the Internet, to deal with the problem of vehicle traffic congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data traffic congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle traffic congestion in a VANET.

Vehicular traffic in urban areas faces congestion challenges that negatively impact our lives. The infrastructure associated with intelligent transportation systems provides means for addressing the associated challenges in urban areas. This study proposes an effective and scalable vehicular traffic congestion avoidance methodology. It introduces a traffic thresholding mechanism to predict and avoid vehicular traffic congestion during route computation. Our methodology was evaluated and validated by employing four road network topologies, three vehicular traffic density levels and various traffic light configurations, resulting in 26 urban traffic scenarios. Using our approach, the number of vehicles that can run in free flow can be increased by up to 200%, whereas for traffic congestion scenarios, the time spent in traffic may be reduced by up to 69% and CO2 emissions by up to 61%. To the best of our knowledge, in the vehicular traffic flow prediction domain, this is the first approach that covers a set of road network topologies and a large and representative set of scenarios for simulated urban traffic congestion testing. Moreover, the comparative analysis with different other solutions in the domain, showed that we obtained the best driving time and CO2 emission reduction.

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.

Problem. The problem of studying the reliability and efficiency of transport systems is multifaceted, since it includes the question of their formation, development, modernization, as well as the direct content of the structure and the totality of processes that advocate them. In today’s conditions, to determine the criteria and factors for the sustainable development of transport systems, a new approach to research is needed that allows for rapid response to changes in the environment, to respond adequately to changing behavior of competitors and consumers, and to effectively influence demand and supply for transport services. Goal. The purpose of this work is to develop a conceptual approach to studying the problem of improving the reliability and efficiency of functioning of automobile transport systems. Methodology. Based on the recommendations of the physical-informational approach, it is possible to consider all processes of using, researching and improving transport systems within a single approach. Results. The criteria are presented that reflect the purpose of the processes and the optimal management decisions regarding transport systems. A hierarchical scheme of the process of forming a tree of goals by a deductive method has been developed. Originality. A physical and informational approach is proposed to study the efficiency and reliability of transport systems at the conceptual and operational levels. Practical value. The functions of the criteria that reflect the target assignments of the processes and optimal managerial decisions concerning transport systems are analyzed. The ideal case with an effective solution is considered when additional information about the importance of partial goals is used when considering the extreme values of all partial criteria and in solving multicriteria problems. The set of principles of exact and complete formulation of partial goals in solving the problem of increasing the reliability and efficiency of transport systems is given.

City activity fluxes are reminiscent of stakeholder mobility in the urban sphere service and functions. Such mobility affects income and work assiduity though often creating accidents, material damage and pollution of varied forms of urban transportation that has lately witnessed the adaption to motorbikes as an alternatively favoured means of movement to that car. Salvaging economic depression and affordability has given biking an edge of success of recent which raises the recurrent question of its spatio-temporal and technical sustainability in in the freight sector. This paper evaluates the commercialisation of bike riding despite levied negativism perceptions in providing an alternative to the major urban problem of traffic congestion and its contribution to peter the pitfalls of the congestion. To assess the urban congestion intensity, traffic flow count was conducted during the rainy and dry season of spatio-temporal of vehicles (from the city centre to the suburbs). Findings agree to the fact that vehicular traffic congestion with increasing number of bikes was found to be higher along the N-E than the N-W road axis in direct conformity with sampled urban congestion triggers for Bamenda related to road infrastructural traits and vehicular numbers. Though motor bikes initially were a panacea, the sum effect of congestion reveals the urgent need for urban mass transport systems that should be complemented by a holistic urban transport governance involving the creation of deviation routes and enforcing traffic congestion rules.

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.

Estimation of the value of time for automated driving using revealed and stated preference methods

Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the “greedy heuristic” is presented for reasonable computation time. To validate the model’s performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows.

Recently, the number of cars on the road has been growing due to the increase in car manufacturing in parallel with customer services provided to help the new driver to buy cars at affordable prices. On the other hand, we find that the infrastructure of big cities cannot support this number of cars and with the disorganization of parking places in the city this problem will lead us to have serious problem in the city which involves the increase of drivers requests to find the nearest parking places to avoid traffic congestion in those areas. In parallel today we talk about the concept of smart cities and how can we use the evolution of the Internet of Things (IoT) to improve the quality of the smart city. Several efforts have been made on the Internet of Things to improve the reliability and the productivity of public infrastructure. Many problems have been handled and controlled by the IoT such as vehicle traffic congestion, road safety and the inefficient use of car parking spaces. This work introduces a novel technique based on a distributed cloud architecture of IoT to manage the parking systems combined with a distributed swarm intelligence technique using the Ant System algorithm to improve the process of finding the nearest car parking in the minimum time based on the state of traffic on this road. This prototype will help drivers to find the nearest car parking and improve the exploitation of the available car parking in the city.

Vehicle traffic congestion is an increasing concern in metropolitan areas, with negative health, environment and economical implications. In recent times, computational intelligence (CI), a set of ...

This paper makes the first step towards mining citywide traffic congestion correlation by utilizing traffic related information from social media. Traffic congestion correlation mining, namely studying which road segments close to each other are highly likely to occur congestion simultaneously, is especially important to help many real applications, such as traffic prediction, traffic control, and urban transportation planning. Traditional traffic data collected from various sensors and other equipments are costly to obtain and hard to scale up to cover a entire city. With the rising popularity of social media, it is common for the public transportation systems and governments to share real time traffic information with the public through social media. It provides us great opportunities to study the traffic conditions of a city with the rich and easily available online data. However, it is also very difficult to use social media data to mine the citywide traffic congestion correlation due to the following major challenges: (1) Social media data like tweets in Twitter are usually noisy and hard to process, especially those tweets posted by individuals. (2) There lacks a method to study the citywide traffic congestion correlation. In this paper, instead of crawling all the traffic related tweets of a city, we only focus on utilizing the tweets posted by some particular organizations or governments. Tweets posted by them are more accurate and formal, thus it is much easier for traffic information extraction. We regard the traffic congestion correlation mining task as a spatio-temporal frequent pattern mining problem by considering each tweet reporting the traffic congestion of a particular road segment as a spatio-temporal item. A spatio-temporal frequent pattern mining algorithm TC_Apriori is also proposed to discover the road segment co-occurrence patterns in congestion. We use the tweets reporting the traffic information of Chicago to evaluate the proposed approach, and the results show that the proposed approach can effectively discover the road segment co-occurrence patterns in congestion.

In large and highly populated downtown areas, traffic congestion is becoming a challenging problem. This work proposes an Efficient road COngestion DEtection protocol (ECODE). This protocol intends to detect road segments that are suffering high traffic congestion using cooperative vehicular communication. It uses multi-hop communication and geocast principles to gather and analyze vehicles' basic data per road segment. Each vehicle can evaluate and report its road segment's congestion level per direction efficiently. This protocol is discussed and reported on its performance compared to previous techniques in this field, using an extensive set of scenarios and experiments implemented on NS-2.

In recent times, traffic congestion has emerged as a persistent problem, critically affecting travel time, fuel consumption, and commuter stress, while also contributing to poor air quality and a reduced quality of life. This global problem is especially acute in Indian megacities, which have dense populations, massive vehicular fleet, and limited road space. Although public transport is considered a promising solution to traffic congestion and related emissions, the effectiveness and adoption of road public transport depend on the characteristics of the service. This study examines commuter satisfaction with public transport services in three Indian megacities (Bengaluru, Chennai, and Hyderabad), highlighting key factors influencing user perceptions. An online survey with 2,274 respondents, divided by location, gender and age group, was conducted from January to April 2023, assessing 12 service attributes. The present study shows varied perception levels of service attributes in the three selected cities. Principal component analysis (PCA) was performed to reduce the dimensionality of the large dataset and confirm the robustness of the data, with high internal consistency across demographic variables using XLSTAT. Key factors identified in the study include ease of maintenance, accessibility, and affordability, with each city having its own strengths and weaknesses. The insights and findings of the current study provide valuable guidance for targeted improvements in public transportation systems to enhance commuter satisfaction and address the widespread problem of vehicular pollution and traffic congestion in Indian megacities.

Vehicle-to-vehicle (VTV) communications are of interest for applications within traffic safety and congestion avoidance, but the development of suitable communications systems requires accurate models for VTV propagation channels. This paper presents a new wideband MIMO (multiple-input- multiple-output) channel model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in rural environments in Lund, Sweden. The measured channel characteristics, in particular the non-stationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contributions from discrete scatterers are tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a deterministic part and a stochastic part. The paper gives a full model parameterization and the model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from measurements.

To develop an Edge-AI based IoT framework that can accurately predict real-time urban traffic flow and proactively identify congestion using lightweight edge models supported by cloud-based deep learning. Real-time traffic flow prediction plays a critical role in intelligent transportation systems, smart city development, and effective congestion control. Although cloud-based models can deliver high prediction accuracy, they are often constrained by latency, high bandwidth usage, and reliance on uninterrupted network connectivity, which limits their practicality for edge-level deployment. Edge nodes struggle to run complex models due to limited processing power, and variations in IoT sensor data can negatively impact prediction accuracy and congestion identification. To mitigate these challenges, the system employs an LSTM model for high-accuracy cloud prediction alongside a compact Conv1D model optimized for fast, real-time inference on edge devices. The approach delivers precise traffic prediction with the LSTM model and supports rapid, energy-efficient real-time processing on edge devices using the lightweight Conv1D model. The findings indicate that the LSTM model performs well particularly on the PEMS-BAY dataset with an RMSE of 0.6120 and MAE of 0.2082—but its large parameter count limits its suitability for edge devices. In comparison, the lightweight Conv1D model achieves similar accuracy with an RMSE of 0.6167 and MAE of 0.4196 while using only 1,969 parameters, making it far more efficient and fully compatible with TFLite INT8 for real-time edge deployment.