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Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed. [ABSTRACT FROM AUTHOR]

The fog computing environment expands the capabilities of cloud computing by moving computing, storage, and networking services closer to IoT devices. These resource‐constrained IoT devices often face challenges like high task failure rates and extended execution latency due to data traffic congestion. Distributing IoT services through task offloading across different layers of computing paradigms enhances QoS (Quality of Service) parameters. This endeavor aims to allocate custom workflow‐based real‐time tasks or jobs for processing across various cloud/fog/edge layers, optimizing QoS factors like makespan, energy consumption, and cost. In the fog computing environment, challenges arise due to uncertainties related to job execution locations and the ability to predict future user requirements. Fuzzy logic offers low‐complexity solutions for handling unpredictable and rapidly changing conditions. This paper proposes a hybrid fog‐cloud‐based computing architecture and an intelligent fuzzy logic‐based computation offloading approach. This approach effectively allocates workloads among edge, fog, and cloud layers, resulting in improvements in makespan time (7.51%), energy consumption (4.63%), and cost (13.60%). The proposed method selects suitable processing units or compute nodes for job execution, utilizing heterogeneous resources. Simulation results demonstrate that the proposed methodology outperforms current state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Understanding the impact of the built environment on traffic congestion can provide reliable references for alleviating traffic congestion. Previous research has explored traffic congestion evolution but often overlooks two key aspects: the frequency of congestion state updates and the spatial-temporal impact of built environment features. Based on data from Hefei, China, we propose a novel geographically and temporally weighted regression (GTWR) model that integrates temporal variables and spatial grids into the traditional GWR model. The empirical results show that public facilities most significantly impact congestion during morning peak hours, scenic spots during afternoon peaks, and motorcycle services in the evening. The study also reveals the rules for the spatiotemporal impact of the built environment on traffic congestion. Finally, the comparison of models shows that the GTWR model outperforms the OLS and GWR models. The findings can guide traffic managers in creating targeted strategies to enhance transportation system efficiency. [ABSTRACT FROM AUTHOR]

The United States has emerged as a prominent actor in the international fight against transnational terrorism in the Sahel region of Africa since 2002, exerting significant political influence on regional affairs. Under the leadership of the United States, a coalition of Sahel countries has collaboratively established security programs, notably including the Trans-Saharan Counterterrorism Partnership (TSCTP). However, the U.S. counterterrorism strategy in the Sahel region has not only proven ineffective in containing the deterioration of regional security, but also has exacerbated the vulnerability to coups in recipient countries. The observed deterioration can be attributed to various driving factors, such as deviations in the willingness and ability to involve and the security traffic jam. We contend that the U.S.-Sahel counterterrorism partnership is fundamentally misaligned and plagued by a securitization problem. Firstly, a significant impediment to international cooperation lies in the United States' pursuit of consolidating its dominant role in Sahel security affairs, which usually overlooks the pragmatic principle of 'African solutions to African problems'. In the counterterrorism process, there is a misalignment between U.S. national interests and those of Sahel states. Furthermore, the United States' extensive reliance on military strategies indicates a significant securitization of non-violent civil affairs in certain Sahel states, inadvertently creating an opportunity for genuine terrorist organizations to recruit members. In addition, U.S. interference in the internal affairs of Sahel states heightens the risk of coups, thereby undermining the legitimacy of U.S. leadership in the region. Currently, with both the withdrawal of the United Nations Stabilization Mission in Mali (MINUSMA) and France withdrawing their security forces from the Sahel region, there has a significant weakening effect on regional security capabilities. Therefore, it is imperative for the international community to collaborate with African nations and adopt more proactive measures to safeguard regional security and maintain order. [ABSTRACT FROM AUTHOR]

In fifth-generation (5G)-based vehicular networks, vehicles share information about the road's condition to make driving safer and alleviate traffic congestion. Given the public nature of the medium, addressing issues of confidentiality and security is of paramount importance. Existing systems are vulnerable to a distributed denial-of-service (DDoS) attack from an attacker or high traffic in a dense area because of the time-consuming and complex operations based on bilinear pair and elliptic curve cryptographies used. In order to protect vehicle-to-vehicle communication in 5G-based vehicular networks from DDoS attacks, a strategy based on the Chebyshev polynomial is proposed in this study. The semi-group and chaotic features of the Chebyshev polynomial are utilised in our proposal. Our solutions also meet all applicable standards for security and privacy, such as node authentication, message integrity, preserving identity privacy, traceability, unlinkability, and resistance to attacks. Finally, our innovation decreases the computational burden to produce a message signature by 66.67% and the burden to verify a signature by 33.33%. However, the size of the message-signature tuple is affected by the communication expenses of our work by 5.00%. [ABSTRACT FROM AUTHOR]

In order to solve these problems, this paper introduced the grey system theory (GST) method in the real-time application of intelligent traffic signal optimization (ITSO). In this paper, the deep Q-network (DQN) algorithm was used to realize the dynamic signal light setting of real-time traffic conditions, which can improve the overall operating efficiency of the traffic system, and the PPO (Proximal Policy Optimization) algorithm was used to solve the problem of the lack of real-time performance of the traditional traffic signal optimization methods. By comparing the traffic congestion index of S city before and after the application of the GST method, the paper found that the average one week before the application was 60.1%, but it dropped to 26.6% after the application. In the experimental test of average speed comparison, the speed after applying the GST method was generally higher than the value before application, and the overall speed increase was about 20 km/h. This paper emphasizes the importance of evaluating the robustness of the GST method, particularly in its ability to manage unexpected scenarios. The research concentrates on assessing four critical indicators: outlier handling, noise tolerance, handling missing data, and nonlinear coping ability. [ABSTRACT FROM AUTHOR]

Macroscopic fundamental diagram (MFD)-based perimeter control has a potential to improve traffic throughput and relieve the overall congestion. In practice, not only the MFD but also the traffic demand suffers from a variety of inherent uncertainties. Hence, this paper aims to contrive a robust control technique to address the perimeter traffic flow control issue in the oversaturated region. To this end, an integral sliding mode control is proposed to drive the traffic state to the desirable condition. The developed strategy deals with practical issues such as finite time stability and range of uncertainty in both MFD and traffic demand. Theoretical analysis verifies that the developed integral sliding mode control approach can guarantee the finite time convergence of the traffic state to the desired one. The performance of the developed scheme is attested by considering various traffic scenarios with uncertain MFDs and traffic demand, in which a comparative performance study with the proportional and integral control method is conducted. It is indicated that the developed scheme can alleviate the congestion in the urban network and improve the throughput of the urban network. Practical Applications: The research presented in this article proposes a novel perimeter control technique designed to enhance traffic flow management in densely populated urban road networks, particularly during peak congestion times. By leveraging the macroscopic fundamental diagram (MFD) and integral sliding mode control theory, the method significantly boosts traffic throughput and alleviates the impact of congestion. This advancement can be seamlessly integrated into current urban traffic management systems, leading to more efficient and convenient urban commutes. Additionally, it addresses the rise in vehicle emissions due to heavy traffic, aiding in the reduction of pollution and supporting sustainable development initiatives. The technique also curtails vehicle fuel consumption and cuts down on costs associated with congestion-related delays, thereby fostering resource optimization and economic growth within urban environments. Moreover, the method is designed to be resilient, taking into account the variability of traffic demand and ensuring the robustness of the traffic control strategy. Overall, the article demonstrates the practical application of advanced control technologies to address real-world traffic challenges, providing a scalable and robust solution adaptable to realistic situations with the uncertainties of urban traffic demand. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [ABSTRACT FROM AUTHOR]

The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers' travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations into classes with and without congestion. We find that this classification improves velocity forecasting, showing that using congestion as an attribute reduces the MAE by at least 6.15% for different machine and deep learning models including random forests, multi-layer perceptrons and recurrent neural networks. We propose a random forest model that identifies the future congestion state from past traffic velocity and volume data and then use it to build new short-term velocity forecasting models. These models reduce the MAE prediction error up to 3.37% over the best models that do not consider congestion. This improvement represents overcoming a 53.75% of the error due to not precisely knowing the future congestion state. [ABSTRACT FROM AUTHOR]

The stability of the transportation system refers to the structural stability of the system. When the system structure is unstable, local or global bifurcation phenomena will occur, which is one of the main reasons for nonlinear traffic phenomena such as congestion. To truly understand the internal mechanism of the formation of these phenomena, it is necessary to analyze the bifurcation of traffic flow. In this paper, the Hopf bifurcation control of a modified viscous macroscopic traffic flow model is studied by using the linear state feedback method, which changes the characteristics of the bifurcation phenomenon of the dynamic system and obtains the required dynamic behavior of the system. First, we can convert the original traffic model into the nonlinear ordinary differential form suitable for bifurcation analysis, solve the equilibrium point of the system, and carry out phase plane analysis. Then, the linear state feedback term is added and the corresponding controlled system is generated, the existence and type of Hopf bifurcation and the existence of saddle node bifurcation are proved. Numerical simulation results show that the analysis and control of Hopf bifurcation in the traffic model are well realized in this paper. [ABSTRACT FROM AUTHOR]

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

In order to reduce overtourism and traffic congestion, local authorities may have to divert recreational vehicle traffic to off-site service areas. The problem that will arise is how best to accommodate different types of users with opposing preferences in the same area, some wanting to be as close as possible to the major site to be visited, others seeking peace and quiet. We have represented their specific attitudes using a two-stage decision-making process via a conjunctive model followed by a compensatory model. We then propose to model three strategies, seeking either to optimise customer attractiveness, or profit, or space occupation, in order to define a location, capacity and price for this shared area. Using a realistic data set, the results show that economic performance follows a concave curve as a function of the population mix. Moreover, only the strategy of maximising attractiveness suggests always mixing users in the same area. [ABSTRACT FROM AUTHOR]

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions. [ABSTRACT FROM AUTHOR]

The traffic environment in tunnel entrance area is complex, with prominent issues related to safety and efficiency. However, existing models find it difficult to accurately capture the traffic situation in this area. Therefore, this paper establishes a vehicle dynamics model for the entrance area of a tunnel based on the social force model and artificial potential field. This model can characterize the deceleration behavior of vehicles at the entrance of the tunnel and the interaction between vehicles. By considering the special effects of the tunnel environment, the simulation experiments successfully reproduce the congestion phenomenon in the entrance of the tunnel. Based on this model, this paper proposes a coordinated driving model under vehicle to vehicle (V2V) communication environment, and the simulation results show that this model can effectively alleviate the congestion in the entrance area of the tunnel and improve traffic efficiency. [ABSTRACT FROM AUTHOR]

In the fast modernised world, usage of vehicles increased day by day, leads to vehicle traffic and congestions. Traditional way of monitoring traffic is less efficient and requires man power. Besides, safety of traffic controllers is the major concern in the manual monitoring. For that reason, effective prediction of vehicular traffic and monitoring the level of congestion is significant in VANET (Vehicular Adhoc Network) for mitigating the delays and danger of accidents. In order to make clear prediction of vehicles and the collision free path in the network, numerous existing algorithms have been provided for prediction and classification. However, the conventional techniques have faced complications in satisfying the accuracy while making predictions of path images. The accuracy in prediction along with the faster computational time have been addressed as a drawback which hinders in making appropriate travel path decisions. In order to overcome such challenges, proposed system employed Yolo v5 algorithm, GGA (Greedy based Genetic Algorithm) and Deep CNN (Convolutional Neural Network) with LSTM (Long Short Term Memory) for traffic monitoring and congestion control in VANET. The Yolo v5 algorithm is utilised for the vehicle prediction mechanism, for the capability of detection with high speed and accuracy. GGA is utilised for the feature selection for the capability of handling numerous features in data and to increase the computational speed. The Deep CNN with LSTM is utilised for Classification for the capability of handling larger datasets and to enhance the accuracy. Though, DCNN is the effective algorithm for classification, it has few limitations like slow processing time. To resolve this, DCNN is utilised with LSTM for enhancing the speed in the projected system. Vehicle detection dataset is used in the proposed system. Further, the experimental evaluations and comparative analysis exhibits the efficiency of the system in terms of accuracy, precision, recall and F1 score when compared to several existing algorithms. The proposed model holds the potential in efficient vehicular congestion management with expected accuracy and it is intended to contribute in to the path identification for vehicles in VANET applications. [ABSTRACT FROM AUTHOR]

This study employs a regression discontinuity design to systematically examine the governance effect of bike-sharing on urban traffic congestion, utilizing city-level data from Beijing, Shanghai, and Wuhan in China between 2016 and 2018. We discover that the introduction of bike-sharing services significantly mitigates traffic congestion in the short term. Our heterogeneity analysis reveals that the initial deployment of shared bicycles primarily alleviates urban congestion, while additional deployments have a limited impact. Further, mechanism test analysis demonstrates that bike-sharing leads to increased metro ridership in these cities, effectively explaining the reduction in road congestion. This study underscores the pivotal role of bike-sharing services in easing urban traffic congestion and provides vital policy insights for enhancing traffic management strategies in Chinese cities. [ABSTRACT FROM AUTHOR]

AbstractEmissions from internal combustion engine vehicles on highways are the major contributors to global warming in the United States. Transportation sector pavement-related emissions come from gasolines and diesel use in vehicles from pavement-vehicle interaction, which is affected by pavement conditions, and by the trucking of new pavement materials and demolition. The objective of this study was to develop a framework for determining the fuel use resulting in environmental impacts caused by construction work zones (CWZs) on a range of vehicles and to produce initial calculations of these impacts by modeling traffic closure conditions for highway maintenance and rehabilitation activities. The study included two common highway categories—freeways/multi-lane highways and two-lane highways. The framework was demonstrated using three CWZ operations under different traffic congestion levels. In the simulation results for a freeway with a CWZ and heavy congestion, fuel consumption increased by 85% and the carbon-dioxide equivalent emissions increased by 86%. Changing CWZ traffic congestion from heavy (average speed 5 mph) to medium (average speed 25 mph for a freeway section) reduced fuel consumption by 40% on a freeway. This study also included use of a pilot car in a CWZ on a two-lane road typical of lower traffic volume state highways and county roads to compare with the drive cycles in MOVES used for the scenarios. The pilot-car operation scenario results indicate that a one-lane closure with pilot-car operation on a two-lane road might consume 13% excess fuel because of idling time and the slow movement of vehicles following the pilot car. [ABSTRACT FROM AUTHOR]

This paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor. The methodology is benchmarked in a case study corridor in San Mateo, CA, in VISSIM with two optimization scenarios, namely, optimal metering and optimal signal control, and two simulation scenarios with a preset metering plan and no metering. The numerical results show that integrated traffic signal and ramp metering control reduces delays, stops, and travel times of the corridor by up to 33.1%, 36%, and 16.4%, respectively, compared to existing benchmark conditions. With appropriate weights prioritizing freeway or arterial street operations, the integrated control balances traffic congestion between the arterial street and the freeway. [ABSTRACT FROM AUTHOR]

Jaundice is a symptom of high blood bilirubin levels affecting about 80% of neonates. In neonates fed with breast milk, jaundice is particularly prevalent and severe, which is likely multifactorial. With the development of genomics and metagenomics, a deeper understanding of the neonatal gut microbiota has been achieved. We find there are accumulating evidence to indicate the importance of the gut microbiota in the mechanism of jaundice. In this paper, we present new comprehensive insight into the relationship between the microbiota and jaundice. In the new perspective, the gut is a crucial crossroad of bilirubin excretion, and bacteria colonizing the gut could play different roles in the excretion of bilirubin, including Escherichia coli as the main traffic jam causers, some Clostridium and Bacteroides strains as the traffic police, and most probiotic Bifidobacterium and Lactobacillus strains as bystanders with no effect or only a secondary indirect effect on the metabolism of bilirubin. This insight could explain why breast milk jaundice causes a longer duration of blood bilirubin and why most probiotics have limited effects on neonatal jaundice. With the encouragement of breastmilk feeding, our perspective could guide the development of new therapy methods to prevent this side effect of breastfeeding. [ABSTRACT FROM AUTHOR]

The increased usage of navigation technologies has caused conflicts in local traffic management, resulting in congested residential areas among other challenges for residents. This paper uses content analysis to investigate such negative social externalities within local communities and neighbourhoods. Through a corpus of 90 news articles about traffic incidents caused by navigation technologies, we identified negative traffic and safety-related externalities, including congestion, damage, pollution, and accidents. We also report on countermeasures by local communities and governments, including street closures, speed limit reduction, and turn bans. Based on our results, we discuss the implications for designing mobile navigation technologies that reduce negative social externalities. [ABSTRACT FROM AUTHOR]

With the rapid growth of population and vehicles, issues such as traffic congestion are becoming increasingly apparent. Parking guidance and information (PGI) systems are becoming more critical, with one of the most important tasks being the prediction of traffic flow in parking lots. Predicting parking traffic can effectively improve parking efficiency and alleviate traffic congestion, traffic accidents, and other problems. However, due to the complex characteristics of parking spatio-temporal data, high levels of noise, and the intricate influence of external factors, there are three challenges to predicting parking traffic in a city effectively: (1) how to better model the nonlinear, asymmetric, and complex spatial relationships among parking lots; (2) how to model the temporal autocorrelation of parking flow more accurately for each parking lot, whether periodic or aperiodic; and (3) how to model the correlation between external influences, such as holiday weekends, POIs (points of interest), and weather factors. In this context, this paper proposes a parking lot traffic prediction model based on the fusion of multifaceted spatio-temporal features (MFF-STGCN). The model consists of a feature embedding module, a spatio-temporal attention mechanism module, and a spatio-temporal convolution module. The feature embedding module embeds external features such as weekend holidays, geographic POIs, and weather features into the time series, the spatio-temporal attention mechanism module captures the dynamic spatio-temporal correlation of parking traffic, and the spatio-temporal convolution module captures the spatio-temporal features by using graph convolution and gated recursion units. Finally, the outputs of adjacent time series, daily series, and weekly series are weighted and fused to obtain the final prediction results, thus predicting the parking lot traffic flow more accurately and effectively. Results on real datasets demonstrate that the proposed model enhances prediction performance. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is one of the challenges in the development of an Intelligent Transportation System (ITS). Accurate traffic flow prediction helps to alleviate urban traffic congestion and improve urban traffic efficiency, which is crucial for promoting the synergistic development of smart transportation and smart cities. With the development of deep learning, many deep neural networks have been proposed to address this problem. However, due to the complexity of traffic maps and external factors, such as sports events, these models cannot perform well in long-term prediction. In order to enhance the accuracy and robustness of the model on long-term time series prediction, a Graph Attention Informer (GAT-Informer) structure is proposed by combining the graph attention layer and informer layer to capture the intrinsic features and external factors in spatial–temporal correlation. The external factors are represented as sports events impact factors. The GAT-Informer model was tested on real-world data collected in London, and the experimental results showed that our model has better performance in long-term traffic flow prediction compared to other baseline models. [ABSTRACT FROM AUTHOR]

The rise in mixed traffic has resulted in increased traffic congestion and accidents across China. The primary objective of this study was to accurately evaluate the operational efficiency and safety of signalized intersections in a mixed-traffic environment and to perform a spatial analysis of intersection traffic conflicts, as well as to apply physics theories to solve practical problems in traffic. The mixed nature of mixed traffic is a form of chaos. Through the matching analysis of mixed-traffic characteristics and entropy, entropy can be used effectively describe the mixed state of a signalized intersection and quantify traffic chaos and disorder. The results of this study show that the entropy flow in a motor-vehicle traffic environment is 0.3807, and the evaluation grade is C. Meanwhile, the entropy flow of a mixed motor vehicle–nonmotor vehicle–pedestrian traffic environment is 0.4084 and the evaluation grade is D, indicating an increase in confusion at intersections in this type of traffic environment compared to in a motor-vehicle traffic environment. The proposed model provides a scientific and systematic method for evaluating the signalized intersection operation in mixed-traffic environments, offering a reliable theoretical foundation for improving intersection travel efficiency and safety. [ABSTRACT FROM AUTHOR]

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%. [ABSTRACT FROM AUTHOR]

Particulate matter is one of the most dangerous air pollutants, especially in urban areas, due to its significant adverse health effects. Traditionally, air quality monitoring relies on fixed reference stations, which often have a low temporal and spatial resolution. To address this limitation, a low-cost, portable air quality monitoring device with a rapid measurement response was used to assess particulate matter concentration levels in the afternoon hours in central Thessaloniki, Greece. This approach enabled the identification of local hotspots directly related to human activities. Statistical analysis and spatial mapping were employed, and data collected were categorized using k-means clustering. The findings of the study suggest that data acquired via portable low-cost sensors can describe the local variability of PM2.5 concentrations. The results indicate that local activities, such as increased human accumulation, traffic congestion at traffic lights, market working hours, together with meteorological parameters, can significantly impact air quality in specific urban locations. They also highlight the differences between data recorded in colder and warmer periods, with the concentrations of PM2.5 in the first period being 3.7 μg/m3 greater on average than in the second. These differences are also identified via the k-means clustering method, which suggest that higher concentrations appear mostly during the colder period of the study. [ABSTRACT FROM AUTHOR]

Recently, learning classifier systems (LCS) have been used for a variety of Internet of Things (IoT) devices and multi-cloud services, including cloud-based centralized control of physical robots and actuators in continuous-time environments. Performance analysis of sensors, navigation of humanoid robots and intelligent control of rescue systems. In these systems, we can run evolutionary or intuitive algorithms on cloud servers to search the space of rules and simultaneously other learning processes to assign how to interact with the environment to the rules in the classification. Also, the problem of continuous congestion, traffic reduction, network routing and predicting traffic conditions in wireless networks is the main challenge facing these systems in real environments. Usually such systems are non-Markovian. Therefore, they need memory to save system states. This paper presents a framework for XCS-based memory-based LCS. In addition to identifying optimal rules in overlapping modes, the XCS architecture is equipped with a memory. Memory stores the most efficient classifier rules. These rules reduce the steps to reach the goal. In the first proposed method, only those rules that affect the moving motion are kept in memory. As the number of rules increases, some of them are deleted according to memory space. In the second proposed method, some features are added to the rules of this memory and the performance of the memory is optimized using evolutionary algorithms, which are used to remove the less used rules. The relative success of the proposed LCS architecture in solving well-known maze problems compared to the conventional XCS architecture confirms its efficiency in increasing the number of successes and reducing the steps to reach the goals. The results of this research are suggested as a suitable solution for reducing routing time, reducing network load in the problem of congestion and traffic in solving problems related to wireless networks. [ABSTRACT FROM AUTHOR]

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles. [ABSTRACT FROM AUTHOR]

To alleviate the performance degeneration of the cooperative adaptive cruising control (CACC) of connected and automated vehicles (CAVs) with low market penetration rates (MPRs), it is necessary to resolve the performance degeneration of CAVs operating at weak mixed-platoon intensities for any-MPR CAVs. A car following model (PMCC) for CAVs is proposed, accounting for human reaction time, communication delay, traffic information memory, and vehicle-to-vehicle (V2V) communication. Linear stability analysis was conducted to validate the stability of mixed platoons at weak platoon intensities for different CAV following models. Numerical simulations were conducted to simulate weak-intensity mixed platoons for different CAV following models operating at high and low equilibrium velocities. The results suggest that PMCC exhibits wider stability equilibrium intervals under different theoretical time headway scenariosand performs well at dissipating the speed fluctuation propagation and minimizing the speed fluctuation amplitude. [ABSTRACT FROM AUTHOR]

This study proposes a multilayered deep neural network (MLDNN) and a congestion index (CI) based on traffic density factor to forecast traffic congestion directly. Data were collected in Delhi city from a selected location using video cameras during peak hours of weekdays from Monday to Sunday to test the proposed model. Collected data were categorized in a matrix format in the intervals of five-minutes. The input matrix was divided into a number of intervals to train, validate, and test the MLDNN and baseline models, including support vector regression, multi-layer perceptron neural network, gated recurrent unit (GRU) neural network, long short-term memory (LSTM) neural network, convolutional neural network (CNN), CNN-GRU neural network, and CNN-LSTM neural network. Results of the study show that the MLDNN and proposed CI can be applied to predict traffic congestion successfully in heterogeneous traffic. [ABSTRACT FROM AUTHOR]

Classifying vehicles in real time was necessary to manage and plan road traffic and avoid frequent traffic jams, traffic violations, and fatal traffic accidents. However, detecting vehicles at night presents a significant challenge, requiring the classification algorithm to be tested under diverse conditions, such as rainy weather, cloudy weather, low illumination, and others, which makes identifying vehicles a complicated task. This paper detected and classifiess vehicle through YOLO-v2, ResNet50, and an optimally configured Long Short-Term Memory (LSTM). But figuring out the best hyperparameters by trial and error took longer and was more complicated. The research resolved the computational time and complexity by involving Oppositional-based Wild Horse Optimization (OWHO) techniques to identify the optimal hyperparameters for LSTM. The result showed that the proposed technique was better, with an average accuracy of 97.38% in classifying vehicles, which was better than other techniques. [ABSTRACT FROM AUTHOR]

The employment of Business Information Modeling (BIM) may assist the resident engineering department in reaching its long-term strategic goals. The suggested computer software was created and tested using the first package of new projects for roads, bridges, and tunnels aimed at solving the traffic congestion crisis taking place in the capital city of Iraq, Baghdad. In the context of the current study, questionnaires and personal interviews with specialists in the field of road, tunnel, and overpass construction were employed. The aim of implementing BIM is to assess the introduced program's efficiency, verify its performance, and identify any errors, defects, or challenges that users may encounter during its application. The computer program underwent successful testing at several Resident Engineer's Offices (REOs) within the initial package of new projects aimed at addressing traffic congestion issues in Baghdad through independent verification checks. Initially, the program was utilized with the assistance of a user guide to facilitate its operation and provide a detailed description of each program feature which can provide effective decisionmaking and enhance REOs' computer skills. [ABSTRACT FROM AUTHOR]

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

Traffic congestion has caused great concern among policymakers in large cities around the world. In contrast with constantly increasing transport supply, individual-based active travel demand management (ATDM) has been proposed as a more efficient policy alternative for combating congestion. However, how individuals make routing choices during commuting in response to ATDM incentives is still largely unknown, given the lack of individual travel data. Using a desensitized one-week travel trajectory data set involving 5641 personal electric vehicles, we examine the major influencing factors of commuting route stability during working days and make suggestions on targeting the most responsive commuters. We first filter family-used vehicles by clustering vehicle usage patterns through employing the Gaussian mixture model and interpret drivers' route choice behaviors during morning peak hours. To look for factors affecting route stability, we develop a generalized additive model and find that route stability is significantly associated with road network density of origins and destinations, departure time, travel duration, commuting distance, reliability of the expressway, and volatility of the congested sections by which the routes passed. The empirical results may contribute to the understanding of individual route choices, and help urban managers develop more refined ATDM policies to alleviate traffic congestion in the future. [ABSTRACT FROM AUTHOR]

Congestion is one of the problems that often occurs in several areas, such as the Damage Circle Line Intersection. This research aims to identify the causes of traffic problems, the characteristics of traffic movements, and the level of service in the area. The research method was carried out by collecting related data from the results of literature studies, agency surveys, and observations. There are several transportation problems at the Damage Circle Line Intersection. The Damage Circle Line Intersection has transportation problems because of the road capacity and vehicle and other commonly known as conflict points. In addition, the traffic light is not functioning properly, and the level of road service is quite low, indicating that road service is quite high, while the travel speed is low. This study concludes that the traffic problem at the Dramaga Ring Road Intersection is triggered by several factors, including the intersection between the flow of vehicle traffic and others which is commonly known as a conflict point. In addition, the traffic lights on site are not working properly, and the area is a congested commercial area. [ABSTRACT FROM AUTHOR]

The rapid growth of urban traffic has intensified daily congestion, affecting both traffic flow and parking. Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems. In an effort to comprehensively assess the latest developments in parking prediction, we curated a dataset of 639 articles spanning from 2010 to the present, using the Scopus database. Initially, we performed a bibliometric analysis utilizing VOSviewer software. These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression. Subsequently, we categorized advancements in three focal areas: behavior prediction, demand prediction, and parking space prediction. A comprehensive overview of the present research status and future directions was then provided. The findings underscore the substantial progress achieved in current parking prediction models, achieved through diverse avenues like multi-source data integration, multi-variable feature extraction, nonlinear relationship modeling, deep learning techniques application, and ensemble model utilization. These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios. Prospective research should explore avenues such as processing unstructured parking datasets, developing predictive models for small-scale data, mitigating noise interference in parking data, and harnessing potent platform fusion techniques. This study's significance transcends guiding and catalyzing advancement in academic and practical domains; it holds paramount relevance across academic research, technological innovation, decision-making support, business applications, and policy formulation. [ABSTRACT FROM AUTHOR]

Due to the limited bandwidth and transmission congestion of the vehicle platoon's communication, it is inevitable to induce time delay, which significantly degrades the control performance of the vehicle platoon, even resulting in instability. This paper focuses on analyzing the internal stability under generic communication topologies and presents a method of computing the exact time delay margin (ETDM). The proposed method can offer a necessary and sufficient internal stability condition with no conservatism. Firstly, to reduce the analytical complexity and computational burden elegantly, we decompose the closed-loop platoon dynamics into a set of individual subsystems via similarity transformation and matrix factorization. This decomposition approach is applicable for any general communication topology. Secondly, an explicit formula is deduced to compute the ETDM by surveying the characteristic roots' distribution of all these individual subsystems. It is further demonstrated that only the positive purely imaginary roots need to be considered to compute the ETDM. Finally, simulations are conducted to demonstrate the effectiveness of the theoretical claims. [ABSTRACT FROM AUTHOR]

The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs). [ABSTRACT FROM AUTHOR]

The increasing number of private cars, public transportation vehicles, and pedestrians, as well as the absence of adequate space for these ground amenities, are one of the primary causes of traffic congestion and accidents in the Kathmandu Valley. Investigations have indicated that the Kathmandu Valley has the greatest traffic accidents despite the heavy presence of the government and its agencies there. Most teens and young adults suffer injuries while using motor vehicles. The study's primary objective is to foresee and prevent such complications by planning for sufficient subsurface infrastructure (a cut‐and‐cover rectangular tunnel) for the Kathmandu Valley's transportation network. The overlying pressure, lateral earth pressure, live load, uplift pressure, and live surcharge are some of the forces acting on the tunnel, creating unique stress and moment zones. The tunnel meets the following geometric requirements: (a) Each of the tunnel's two cells has a clear span of 10 m and a clear height of 5.5 m. The side walls, inner walls, top slab, and bottom slab are all 700 mm thick. Soil has built up to a height of 4 m over the tunnel's roof. The analytical method is used in the tunnel segment's analysis. Furthermore, the designed tunnel has been evaluated for stability, considering the deflection and shear resistance. The analysis indicates that the tunnel meets the stability requirements. This implies that the structure is capable of withstanding the applied forces without excessive deflection. Non‐linear dynamic time history analyses of the El Centro earthquake and the Gorkha earthquake were computed. From the El Centro earthquake, the maximum displacement was 23.63 mm at 10.59 s, and from the Gorkha earthquake, the maximum displacement was 16 mm at 0.19 s for the modeled structures. [ABSTRACT FROM AUTHOR]

Structural models of traffic congestion, such as the bottleneck model, are used to answer important, policy‐relevant questions. However, existing models typically assume that no travelers are inframarginal regarding when to travel; that is, given equilibrium travel times, no travelers strictly prefer their ex ante departure time to all others. In this article, I address this shortcoming by incorporating inframarginal travelers into these models. This change significantly improves these models' ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts. [ABSTRACT FROM AUTHOR]

Data transmission from sensor nodes is the biggest problem for IoT networks. Overusing communication power shortens node lifespans. Thus, network issues including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Routing protocols are essential for delivering data between organizations. Information gathering and consolidation require data aggregation to minimize traffic congestion, operating expenses, energy usage, and network lifespan. IoT data aggregation makes route planning dependable, energy-efficient, and difficult. Disjoint & Scalable Multipath Routing (D &SMR) is a new routing system developed using NS2 simulation. The method estimates delivery success using decision trees and neural networks. We evaluate characteristics such as (D &SMR) routing scheme predictability, node popularity, power consumption, speed, and location while training the model. Simulation results show that (D &SMR) outperforms a reliable routing system in terms of delivery success, lost messages, overhead, and hop count. The proposed hybrid routing method involves cluster construction and intra- and inter-cluster routing. The study found that (D &SMR) beats previous research in network resilience, packet transmission efficiency, end-to-end latency, and energy usage. [ABSTRACT FROM AUTHOR]

The venture is intended to foster a thickness based unique traffic light framework. The sign timing changes consequently on detecting the traffic thickness at the intersection. Gridlock is an extreme issue in many significant urban communities across the world and it has turned into a bad dream for the workers in these urban areas. Traditional traffic signal framework depends on fixed time idea allocated to each side of the intersection which can't be changed according to fluctuating traffic thickness. Intersection timings apportioned are fixed. Some of the time higher traffic thickness at one side of the intersection requests longer green time when contrasted with standard allocated time. The item identification in the traffic light is handled and changed over into test system then, at that point, its limit is determined in view of which the shape has been attracted request to ascertain the quantity of vehicles present nearby. In the wake of working out the quantity of vehicles we will came to realize in which side the thickness is high in light of which signs will be distributed for a specific side. On account of its high recognition rate, CNN can be used to realize various computer vision tasks. Tensor Flow is used to implement CNN. In the German data sets, we are able to identify the circular symbol with more than 98.2% accuracy. [ABSTRACT FROM AUTHOR]

We investigate the formation of dust traffic jams in polar-aligning circumbinary discs. We use 3D smoothed particle hydrodynamical simulations of both gas and dust to model an initially highly misaligned circumbinary disc around an eccentric binary. As the circumbinary disc evolves to a polar configuration (perpendicular to the binary orbital plane), the difference in the precession between the gas and dust produces dust traffic jams, which become dense dust rings. We find the formation of dust rings exists for different Stokes number, binary eccentricity, and initial disc tilt. Dust rings are only produced while the circumbinary disc is misaligned to the binary orbital plane. When the disc becomes polar aligned, the dust rings are still present and long-lived. Once these dust rings are formed, they drift inward. The drift time-scale depends on the Stokes number. The lower the Stokes number, the faster the dust ring drifts near the inner edge of the disc. The dust rings will have an increased mid-plane dust-to-go ratio, which may be a favourable environment for the steaming instability to operate. [ABSTRACT FROM AUTHOR]

Ride-hailing services have grown rapidly, presenting challenges such as increased traffic congestion, inefficient driver workload distribution, and environmental concerns like higher fuel consumption and emissions. This study develops a non-linear ride-hailing assignment model addressing these issues by considering service level, driver workload, and fuel consumption. A piecewise linear method was employed to handle a non-linear programming model, and the method was modified to function autonomously without operator intervention. The model's performance was evaluated using a publicly accessible dataset of taxi trips in Manhattan, focusing on indicators such as passenger waiting time, driver workload distribution, and fuel consumption. Numerical simulations demonstrated significant improvements: a 15% reduction in average passenger waiting time, a 20% improvement in balancing driver workloads, and a 10% decrease in overall fuel consumption, contributing to reduced emissions and environmental impact. The modified piecewise linear method proved effective in optimizing ride-hailing assignments, providing a more efficient and sustainable solution. The model also showed robustness in handling large datasets, ensuring scalability and applicability to various urban settings. These findings highlight the model's potential to enhance operational efficiency and promote sustainability in ride-hailing services. By integrating considerations for service level, driver workload, and fuel consumption, the model offers a holistic approach to addressing the key challenges faced by the ride-hailing industry. This study provides valuable insights for future ride-hailing development and implementations of ride-hailing systems, promoting practices that are both efficient and environmentally friendly. [ABSTRACT FROM AUTHOR]

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system. [ABSTRACT FROM AUTHOR]

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. [ABSTRACT FROM AUTHOR]

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method's effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types. [ABSTRACT FROM AUTHOR]