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Fluid flow throttling is common in industrial and building services engineering. Similar tunnel throttling of vehicular flow is caused by the abrupt number reduction of roadway lane, as the tunnel has a lower lane number than in the roadway normal segment. To predict the effects of tunnel throttling of annular freeway vehicular flow, a three-lane continuum model is developed. Lane III of the tunnel is completely blocked due to the need of tunnel rehabilitation, etc. There exists mandatory net lane-changing rate from lane III to lane II just upstream of the tunnel entrance, which is described by a model of random number generated through a golden section analysis. The net-changing rate between adjacent lanes is modeled using a lane-changing time expressed explicitly in algebraic form. This paper assumes that the annular freeway has a total length of 100 km, a two-lane tunnel of length 2 km with a speed limit of 80 km/h. The free flow speeds on lanes I, II and III are assumed to be 110, 100 and 90 km/h respectively. Based on the three-lane continuum model, numerical simulations of vehicular flows on the annular freeway with such a tunnel are conducted with a reliable numerical method of 3rd-order accuracy. Numerical results reveal that the vehicular flow has a smaller threshold of traffic jam formation in comparison with the case without tunnel throttling. Vehicle fuel consumption can be estimated by interpolation with time averaged grid traffic speed and an assumed curve of vehicle performance. The vehicle fuel consumption is lane number dependent, distributes with initial density concavely, ranging from 5.56 to 8.00 L. Tunnel throttling leads to an earlier traffic jam formation in comparison with the case without tunnel throttling. [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]

Traffic forecasting is a crucial task of an Intelligent Transportation System (ITS), which remains very challenging as it is affected by the complexity and depth of the road network. Although the decision‐makers focus on the accuracy of the top‐level roads, the forecasts on the lower levels also improve the overall performance of ITS. In such a situation, a hierarchical forecasting strategy is more appropriate as well as a more accurate prediction methods to reach an efficient forecast. In this paper, we present a deep learning (DL) approach for hierarchical forecasting of traffic flow by exploring the hierarchical structure of the road network. The proposed approach is considered an improved variation on the top‐down strategy for the reconciliation process. We propose a model based on two deep neural network components to generate a coherent forecast for the total number of road segments. We use N‐BEATS, a pure deep learning forecasting method, at the highest levels for traffic time series, then disaggregate these downwards to get coherent forecasts for each series of the hierarchy using a combination of CNN and LSTM. Experiments were carried out using Beijing road traffic dataset to demonstrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

In this paper, considering the cost of base station, coverage, call quality, and other practical factors, a multi-objective optimal site planning scheme is proposed. Firstly, based on practical needs, mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives, coverage objectives, and quality objectives. Then, a multi-objective optimization model was established by combining threshold and traffic volume constraints. In order to reduce the time complexity of optimization, a non-dominated sorting genetic algorithm (NSGA) is used to solve the multi-objective optimization problem of site planning. Finally, a strategy for clustering and optimizing weak coverage areas was proposed. In order to avoid redundant neighborhood retrieval during cluster expansion, the Fast Density-Based Spatial Clustering of Applications with Noise (FDBSCAN) clustering method was adopted. With different sub-objectives as the main objectives, this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations, as well as relevant site planning maps, and provided three planning schemes for different main objectives. The simulation results show that the traffic coverage of the three station planning schemes is above 90%. The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points. [ABSTRACT FROM AUTHOR]

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Improving traffic flow efficiency has become more important due to the growing number of vehicles in urban areas, and this requires optimizing traffic control systems. In order to reduce traffic, a smart traffic control system using infrared sharp sensors to count vehicles at intersections is proposed in this paper. The system uses sensors to measure distances to a fixed barrier, accurately counting vehicles while ignoring pedestrians. It was originally designed for four-way intersections but is adaptable to various junction types. Each lane's unique count is controlled by a separate Arduino Nano Board, and the data is saved in microcontrollers. Following the aggregation and processing of these counts, an Arduino Mega Board modifies the traffic light timings in response to actual vehicle flow. [ABSTRACT FROM AUTHOR]

Introduction: The rise of autonomous vehicles has brought about a transformative shift in transportation, witnessing the coexistence of human-driven and autonomous vehicles on highways in the United States, Europe, and China. This coexistence poses challenges to traffic operations, particularly in intricate scenarios like highway ramps. The interaction between autonomous truck platoons, displaying heightened maneuverability, and human-driven vehicles has emerged as a critical concern. Consequently, this research aims to propose and investigate three avoidance modes (overall, gap and cross) employed by truck platoons, evaluating their comprehensive impact on human-driven vehicles. Methods: Multiple scenarios are simulated utilizing the Simulation of Urban Mobility (SUMO) software, collecting data on three distinctive avoidance modes concerning Travel Time (TT) and Time to Collision (TTC). Employing principles of game theory, a comprehensive assessment is undertaken to evaluate the traffic efficiency and safety of each mode. Comparative analyses against a noavoidance baseline are conducted, offering a holistic evaluation of each mode's applicability across diverse scenarios. Results: The findings highlight the commendable performance of gap mode and overall mode in enhancing traffic efficiency, while cross mode excels in fortifying traffic safety. Overall, the gap mode emerges as the optimal choice among the three. Discussion: This study introduces a game-theoretic approach to managing humanmachine mixed traffic flow, establishing a foundational framework for theoretical research in decision-making for emerging mixed traffic environments. It considers safety and efficiency perspectives across different types of traffic entities. The insights gained contribute to the evolving discourse on the integration of autonomous vehicles into existing traffic systems, addressing the intricate challenges posed by the coexistence of various vehicle types on highways. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency. [ABSTRACT FROM AUTHOR]

Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion. [ABSTRACT FROM AUTHOR]

The simulation of traffic flow on networks requires knowledge on the behavior across traffic intersections. For macroscopic models based on hyperbolic conservation laws, there exist nowadays many ad‐hoc models describing this behavior. Based on real‐world car trajectory data we propose a new class of data‐driven models with the requirements of being consistent to networked hyperbolic traffic flow models. To this end, the new models combine artificial neural networks with a parametrization of the solution space to the half‐Riemann problem at the junction. A method for deriving density and flux corresponding to the traffic close to the junction for data‐driven models is presented. The model parameters are fitted to obtain suitable boundary conditions for macroscopic first‐ and second‐order traffic flow models. The prediction of various models are compared considering also existing coupling rules at the junction. Numerical results imposing the data‐fitted coupling models on a traffic network are presented exhibiting accurate predictions of the new models. [ABSTRACT FROM AUTHOR]

A dedicated bus lane is designed to give priority to buses over private cars on the road. However, this approach might waste road traffic capacity when bus demand is low and private car demand is high. Thus, it is essential to utilize bus lanes more effectively. Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. However, these highly rely on transportation infrastructure and emerging technologies. Hence, we propose a simple method of a partially space‐shared bus lane, which allows private cars to merge into the bus lane at specific locations. Lane‐changing decision is modeled as a result of speed difference between adjacent lanes. Traffic flow is simulated by the LWR model in the Lagrangian coordinate system. Two scenarios are set up—dedicated bus lane and partially space‐shared bus lane—to evaluate the partially space‐shared system. The only optimization variable is the location that allows private cars to conduct lane‐changing. We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space‐shared bus lane reduces private‐car delays and total delays of the system. The buses would experience an increase of delays, inevitably. But the bus delay increase could be limited in a small range, which maintains the bus priority. In addition, a multiple lane‐changing position strategy is better than the single position one. This approach is suitable for traffic situations with low bus demand and high private car demand. Our work is expected to contribute to the design of future urban bus lanes and improve overall traffic operations. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic procedural model to understand the day-to-day evolution of road traffic and risky mode choice behaviour. We embed a modified Bureau of Public Roads function and a queueing model into a dynamic traffic flow model to estimate information on travel time and in-vehicle crowding. Then, the estimated information is fed into a risky mode choice model, with the joint consideration of intra- and inter-individual risk attitude heterogeneity. In order to reflect what may be happening in the real market, we establish a close loop with individuals' mode choice behaviours shaped by their travel experiences and historical information, the induced impact on traffic flow, and more importantly, the feedback and interplay between them. Our model delivers realistic and robust results, in which risk attitudes play the most prominent role. [ABSTRACT FROM AUTHOR]

In recent times, elevated urban linkages (EULs) have emerged as crucial components of urban development, cutting through the cities' urban fabric to accommodate the rapid expansion of urban cores and to facilitate traffic flow. However, these massive infrastructures have inadvertently given rise to urban lost spaces—vacant, inefficient areas within the urban fabric. Recognizing these lost spaces as untapped public assets, the paper explores sustainable development strategies to reclaim them into the urban fabric, transforming them into vibrant, multi-functional public spaces. The paper aims to achieve a set of urban design guidelines for rehabilitation design interventions which will be deduced into a comprehensive framework. This framework encompasses criteria for selecting appropriate approaches and types of rehabilitation design interventions, as well as design principles ensuring the desirability of the reclaimed space. The paper applies this framework to assess the El Sawy Cultural Wheel in Egypt, revealing it as a successful example of a reclaimed space. While positive aspects of rehabilitation design interventions are evident in terms of the approach and type of intervention in this case study, there are identified areas with shortcomings in fully implementing the design principles, if enhanced, could elevate the overall quality of the reclaimed space. [ABSTRACT FROM AUTHOR]

The sensor network is an important part of the real-time monitoring system of traffic flow in tourist areas. In this study, we built a traffic flow prediction model based on machine learning to assist the construction and optimization of a sensor network. Using the sensor network, we developed a real-time monitoring system of traffic flow in tourist areas. Factors affecting traffic flow in the study area (Hongze Lake, China) were determined through interviews with 50 tourists. Using the scores of the factors, machine learning models such as random forest, decision tree, and support vector machine were constructed to predict traffic flow, and the result was used to design an appropriate sensor network. The predictions of three machine learning methods were compared to build a traffic flow prediction model. After comparing the predicted results with the tested ones, the performance of the prediction model was validated. Referring to such results, we selected the area near a square and bridges and the intersections of roads as key areas to install sensors for monitoring traffic flow. The system is being implemented in the study area to renovate the area to promote further growth of the tourism industry. [ABSTRACT FROM AUTHOR]

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape. [ABSTRACT FROM AUTHOR]

Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, this paper presents a novel Twin Delayed Deep Deterministic Policy Gradient with Dual Buffer (TD3_DB) for traffic signal control. In the proposed framework, two experience buffers are used to store important samples and normal samples, separately, and the proportion of the two buffers is adjusted adaptively. In addition, lane pressure, describing the dynamic feature of lane traffic flow, is used for the state design of the TD3 agent, which enhances the perception of the agent toward intersections. Comprehensive experiments on different traffic flow modes has shown, the dual experience replay scheme can improve the sample utilization, and the proposed TD3_DB performs better than other methods such as original TD3, Proximal Policy Optimization (PPO), etc., effectively reducing vehicle queue length and waiting time. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model. The MFSTBiSGAT model leverages graph attention networks to extract dynamic spatial features from complex road networks, and utilizes bidirectional long short-term memory networks to capture temporal correlations from both past and future time perspectives. Additionally, spatial and temporal information enhancement layers are employed to comprehensively capture traffic flow patterns. The model aims to directly extract original temporal features from traffic flow data, and utilizes the Spearman function to extract hidden spatial matrices of road networks for deeper insights into spatiotemporal characteristics. Historical traffic speed and lane occupancy data are integrated into the prediction model to reduce forecasting errors and enhance robustness. Experimental results on two real-world traffic datasets demonstrate that MFSTBiSGAT successfully extracts and captures spatiotemporal correlations in traffic networks, significantly improving prediction accuracy. [ABSTRACT FROM AUTHOR]

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections. [ABSTRACT FROM AUTHOR]

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver's intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management. [ABSTRACT FROM AUTHOR]

With the exponential growth of encrypted communication over the internet, research into systems capable of analyzing large volumes of encrypted traffic is essential. This study focuses on evaluating the performance of two prominent tools, ssldump and tshark, in decrypting and inspecting encrypted network traffic, assuming an environment where decryption keys are available. The performance of ssldump and tshark was assessed using various metrics, including execution time, and the ability to handle different file sizes and session counts. The results showed that tshark exhibited faster processing speeds for smaller file sizes and a higher number of sessions, while ssldump demonstrated better performance for larger file sizes and fewer sessions. However, notable performance differences were not observed based solely on the type of cipher suite or encryption method used. To enhance performance, the study proposes the session-based split and conquer (SSC) technique for automating parallelization using a multi-process approach. SSC shows up to a 39× improvement in performance, depending on system capabilities and workload. [ABSTRACT FROM AUTHOR]

With the rapid expansion and ubiquitous presence of the Internet of Things (IoT), the proliferation of IoT devices has reached unprecedented levels, heightening concerns about IoT security. Intrusion detection based on deep learning has become a crucial approach for safeguarding IoT ecosystems. However, challenges remain in IoT intrusion detection research, including inadequate feature representation at the classifier level and poor correlation among extracted traffic features, leading to diminished classification accuracy. To address these issues, we propose a novel transformer-based IoT intrusion detection model, MBConv-ViT (MobileNet Convolution and Vision Transformer), which enhances the correlation of extracted features by fusing local and global features. By leveraging the high correlation of traffic flow, our model can identify subtle differences in IoT traffic flow, thereby achieving precise classification of attack traffic. Experiments based on the open datasets TON-IoT and Bot-IoT demonstrate that the accuracy of the MBConv-ViT model, respectively, 97.14% and 99.99%, is more effective than several existing typical models. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors. [ABSTRACT FROM AUTHOR]

Summary: Vehicular connectivity is a significant key contribution in vehicular ad‐hoc networks (VANETs). The inherent high dynamicity of vehicles is a serious challenge that affects automotive connectivity and disrupts inter‐vehicle communications stability. Vehicle‐to‐everything (V2X) communication enables vehicles to connect to any other networks and facilitates the evolutionary trend of automotive innovations toward autonomous internet‐enabled intelligent vehicles. In this article, we provide a systematic literature review (SLR) for vehicular connectivity and V2X communications by investigating 58 research articles from 2018 to 2023 in VANETs. We propose six fundamental research questions (RQs) and a taxonomy to systematize the study of the baseline articles and answer the crucial RQs. This SLR can be helpful for researchers as a complete study focusing on V2X analysis, making it possible to achieve Technical Aspects and New challenges. [ABSTRACT FROM AUTHOR]

The economic impact of inefficient traffic control systems is significant owing to prolonged commute durations, and increased energy consumption. Traffic signal control systems (TSCSs) significantly influence traffic flows at intersections. Therefore, adaptive TSCSs (ATSCSs) that can adjust to traffic conditions in real‐time have been proposed as more efficient alternatives. However, the expensive implementation of these systems highlights the need for judicious investments in appropriate technologies and infrastructure. Therefore, a comprehensive technology roadmap should be built that guides the future development of traffic control and the infusion of technologies to address traffic needs. Additionally, as ATSCSs are developed and managed by local governments, the perspective of a government‐led technology roadmap is required to guide the roadmap development and implementation. Although studies have explored technology roadmaps across numerous sectors, the viewpoint of roadmap development guided by government entities is frequently neglected despite the role of these entities in shaping technological policies, underwriting research and development initiatives, and driving nationwide innovation strategies. In this study, a comprehensive framework is proposed for developing technology roadmaps tailored for systems and technologies led by governmental entities. This framework has been adapted from the Advanced Technology Roadmap Architecture (ATRA) and brought original adjustments thereby addressing the research gap. The study also presents strategic recommendations for the ATSCS implementation in South Korea, integrating systems engineering principles for a holistic approach to technological advancements. The framework can be replicated to serve as a guide for governments seeking to implement effective and efficient technology roadmaps for public infrastructure systems. [ABSTRACT FROM AUTHOR]

During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short‐term prediction model combined with a proportional‐integral‐derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real‐time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller's decision to initiate a "Merge" or "No Merge" guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real‐world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas. [ABSTRACT FROM AUTHOR]

Signalized intersections that are pretimed are commonly used in rapidly developing cities with diverse traffic patterns, including those found in Indonesia. These intersections are a common source of congestion and delays in road networks, particularly when there is non-lane based (NLB) traffic in urban areas. Accurate estimation of both the base saturation flow rate and capacity is essential for this type of facility, as an error in the prediction of the base saturation flow rate can result in significant bias in capacity evaluation and design at signalized intersections. The estimation of capacity at signalized intersections is critical for ensuring optimal signal timings, minimizing delay, and reducing congestion. Heterogeneous traffic, which refers to the presence of various types of vehicles with distinct static and dynamic characteristics, is a common phenomenon. To address this issue, this paper presents a modeling approach for the SFR that takes into account heterogeneous traffic and NLB movements. Indonesia, being an archipelago with 34 provinces, served as the focus of this study, which specifically concentrated on Banda Aceh, the capital province of Aceh province. Employing primary and observed data collected at a specific, predetermined signalized timing, this study aimed to investigate the impact of intersection geometry and heterogeneous traffic composition on the SFR. By adopting the modeling approach for NLB movements, the study formulated the BSFR model. To estimate the scale parameters of the BSFR, the multiple linear regression approach was utilized. The analysis results reveal that the existing BSFR based on the IHCM formula gives a substantially biased estimation because the PCEs from the Indonesian Highway Capacity Manual (IHCM) are underestimated. This source of error could be partially due to the heterogeneous (varied vehicle composition) traffic flow with NLB movements, unlike that observed under the prevailing conditions of IHCM 1997. The empirical results show that the existing IHCM should be improved to avoid overestimation, particularly for effective approach width (We) greater than 4.5 m. A comparison of the BSFR prediction model between IHCM's PCEs and new PCEs shows that the BSFR is predicted more accurately in the latter case. This finding demonstrates that the existing IHCM can be adjusted in two ways: adjusting PCEs or calibrating the BSFR formula. The proposed models can also enhance the accuracy of BSFR prediction, leading to better signalized intersection capacity estimation, providing support for designing traffic operation, alleviating traffic congestion, and reducing congestion delay within the city. [ABSTRACT FROM AUTHOR]

Slow or failed detection of low-salience vertical obstacles and associated wires is one of today's leading causes of fatal helicopter accidents. The risk of collisions with such obstacles is likely to increase as advanced aerial mobility and broadening drone activity promises to increase the density of air traffic at low altitudes, while growing demand for electricity and communication will expand the number of vertical structures. The current see-and-avoid detection paradigm relies on pilots to spend much of their visual attention looking outside for obstacles. This method is inadequate in low-visibility conditions, cluttered environments, and given the need for pilots to engage in multiple competing visual tasks. With the expected growing number of hazards and an increased traffic volume, the current approach to collision avoidance will become even less tenable. A human-in-the-loop helicopter simulator study was conducted to assess the effectiveness of sensor visualizations (image intensification or thermal imaging) and graphic augmentations (a bounding box around a tower and a circle surrounding the base of the tower) for supporting fast and reliable detection of vertical structures. Graphic augmentations resulted in faster tower detection time when ambient visibility and illumination were reduced close to the limit for visual flight. Bounding boxes around towers were detected first in all conditions but tended to mask the obstacle they were meant to highlight. Sensor visualization affected tower detection time only at night, where night vision goggles were more effective than the infrared thermal sensor. [ABSTRACT FROM AUTHOR]

Taxila intersection is facing severe traffic issues includingcongestion, delays, increased travel times and inadequate infrastructure. The aim of the study is to highlight the real problems at Taxila intersection related to road users, vehicles and road infrastructure. Traffic data was collected during peak hours through traffic observing video cameras at the unsignalized intersection. Subsequently, traffic violation measurements and their comparisons between morning and evening peak hours, as well as between weekend and week days were developed. The study also identifies the involvement in traffic violations by different drivers, age groups and vehicle types. Comparing morning and evening peak hours reveals variations in traffic patterns and congestion levels throughout the day, helping to create customized plans. Similarly, investigating disparities between weekends and weekdays uncovers unique factors influencing traffic conditions on different days. Results showed that highest percentage of violations at the intersection resulted from 'illegal crossing of road by pedestrian' followed by 'illegal parking' at the intersection. While the lowest percentage of violation observed was 'wrong way driving' at the intersection. In conclusion, this research provides a comprehensive understanding of the traffic issues at the Taxila intersection and offers evidence-based strategies to improve traffic flow, mitigate congestion, and enhance road safety in the area. [ABSTRACT FROM AUTHOR]

Traffic flow is a result of the connection between the derived demand and land use. The derived demand varies in space and time, this study explores how traffic congestion is correlated with land use patterns. Historically, statistical models were used to predict and analyze these patterns. The methodology of this study is to investigate this interaction by statistical methods such as linear regression modeling. This analysis was performed using various land use types that could influence the demand. From the regression analysis, the best influence variables that affect the model are land use variables. The strong statistical parameter is commercial land use, which affects traffic volume, and causes the highest traffic congestion. In addition, correlation values are negative, meaning that as commercial land use increases, traffic flow increases and road capacity decreases. When modeling with the commercial land use variable, we conclude the value of R-Squared = 0.87 and that the relationship is an inverse strong relationship between traffic volumes and commercial land use. Mostly, land use govern traffic demand. [ABSTRACT FROM AUTHOR]

With a full fleet of autonomous vehicles (AV) in the future, the concept of signal-free intersections has attracted transportation researchers. Multiple studies have investigated different methods and protocols to facilitate vehicle decision-making at intersections. Most of these methods have relied on strong centralized or inter-vehicle communications. This research aims to develop a new protocol for managing vehicle movement at four-leg intersections assuming a fully automated fleet. The main concept of the proposed protocol is to maintain a continuous movement of vehicles entering the intersection, without stopping in queues, by controlling their sequence of movements. In the methodology a dynamic occupancy grid (DOG) approach is applied by initially dividing the intersection into dynamic grids (i.e. cells). The cells are in a virtual movement, emanating away from each lane-group without overlapping, like a gear machine. Each vehicle sits on a specific cell to traverse the intersection safely at a predetermined time. In other words, vehicles approaching an intersection must register their speed and position by certain sensors, and in return receive the appropriate acceleration and speed to finally be allocated to the suitable moving grids. The efficiency of the applied protocol was demonstrated by a practical example that presented a higher intersection capacity value exceeding the ideal saturation flow rate in some cases. This reflects the efficiency of the implemented protocol and its applicability to different low to high traffic flows. Moreover, the protocol shows more flexibility in dealing with different weather, geometric and traffic conditions. [ABSTRACT FROM AUTHOR]

We study a generalized Follow-the-Leader model where the driver considers the position of an arbitrary but finite number of vehicles ahead, as well as the position of the vehicle directly behind the driver. It is proved that this model converges to the classical Lighthill–Whitham–Richards model for traffic flow when traffic becomes dense. This also underscores the robustness of the Lighthill–Whitham–Richards model. [ABSTRACT FROM AUTHOR]

Traffic accidents are prone to occur in mixed-traffic flows of nonmotorized and motorized vehicles. Particularly, electric two-wheeled vehicles face risks when they move laterally to the motor vehicle lane to avoid obstacles. To decrease accident risks caused by lateral offset in mixed-traffic flows, this study proposed a lateral offset detection method based on deep learning technology. A vehicle lateral offset risk recognition model of You Only Look Once Version 8 (YOLOv8) was established using actual traffic scenes in Xuzhou as a dataset. The model was trained by loading the preprocessed and labelled dataset, selecting the model architecture suitable for target detection in traffic scenes, setting training parameters, and executing. Then, the training results were analyzed based on images, such as labelled images, training result plots, and model curves, and the accuracy and reliability of the model were evaluated using standard metrics, such as precision, recall, and average precision. Results demonstrate that the offset model of YOLOv8 can detect the lateral offset risk of vehicles from traffic surveillance videos. The proposed model has robustness in category judgment. The detection precision of lateral offset in the model is 0.638, the recall rate is 0.626, the mean average precision calculated at an intersection over union (IoU) threshold of 0.50 (mAP50) is 0.679, and the mean average precision calculated at an IoU threshold of 50%-95% (mAP50-95) is 0.358. The study provides an important reference for the construction and performance evaluation of lateral movement models of vehicles in mixed-traffic flows. [ABSTRACT FROM AUTHOR]

The dualism of the theories of soliton solutions and solutions to functional differential equations of pointwise type is discussed. We describe the foundations underlying the formalism of this dualism, the central element of which is the concept of a soliton bouquet, as well as a dual pair "function–operator." Within the framework of this approach, it is possible to describe the entire space of soliton solutions with a given characteristic and their asymptotics in both space and time. As an example, the model of traffic flow on the Manhattan lattice is used to describe the whole family of bounded soliton solutions. [ABSTRACT FROM AUTHOR]

This study explores the significant role of three-wheelers (3W) on urban road traffic, focusing on the reduction in average vehicle speeds and its consequential impact on the Level of Service (LOS) of the road. Data collected from various traffic volumes reveal significant speed reductions ranging from 4 % to 35 % due to the movement of 3Ws on the road. To model this phenomenon, Artificial Neural Network (ANN) is employed. The resulting reductions in speed, induced by the 3W, trigger LOS degradation, leading to congestion and delays. A noteworthy finding is that the traffic flow naturally gravitates towards the LOS C, underscoring the substantial role played by the 3W in diminishing the overall LOS of the road. This research offers critical insights into the dynamics of urban traffic influenced by the 3W, providing a valuable foundation for traffic management and urban planning efforts. [ABSTRACT FROM AUTHOR]

Vehicular emissions are considered one of the major anthropogenic sources of greenhouse gases and poor air quality in metropolitan cities. This study aims to see the correlation of CO 2 , CH 4 , and CO through monitoring over a period from December 2020 to October 2021 covering three seasons' winter, summer, and monsoon at two different traffic locations of Delhi having different traffic volumes, road patterns, and traffic management. The annual average morning concentration of CO 2 , CH 4 and CO was found (533 ± 105), (7.3 ± 3.1), (10.7 ± 3.0) ppm at Najafgarh and (480 ± 70), (5.2 ± 1.8), (7.8 ± 2.8) ppm at Rajendra Place, respectively. A relationship between concentration of all three gases and meteorological parameters such as temperature, humidity, wind speed and wind direction has also been investigated using Pearson correlation coefficient and pollution rose diagram. A comparable pattern in concentration was observed for all three gases in spatial (location) and temporal (diurnal) distribution. The concentration trend of CO 2 in different seasons is winter > summer > monsoon, while in the case of CH 4 winter = summer > monsoon but not any seasonal trend was noted in CO case. It is observed that CO 2 has a good relation with CO (a tracer for vehicular emission) in terms of diurnal variation, whereas, CH 4 does not represent a relation with CO and CO 2 diurnally, suggesting that vehicles are the source of CO 2 but not much contributing to other greenhouse gases like CH 4. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANET) facilitate vehicle to everything (V2X) communication between vehicles and road side units (RSU) to exchange safety and alert messages required for the successful implementation of VANET applications. However, VANET is exposed to Sybil attacks, where malicious vehicles generate multiple false identities to seize control of network resources or create non-existent traffic situations. While several Sybil attack detection mechanisms based on deterministic and learning methods exist, their performance is restricted to particular scenarios. This work proposes a deep learning-based Sybil Attack Detection mechanism. It identifies the Sybil nodes by detecting the associativity between senders at a time instant using common vehicle characteristics, including Euclidean distance, speed, flow, and computed similarity between senders by using the dynamic time warping (DTW) method. Among the five applied deep learning models, the proposed solution achieves improved detection performance through convolutional neural network (CNN) and a combination of CNN with long short-term memory (LSTM) models in varying network scenarios. [ABSTRACT FROM AUTHOR]

Travel time reliability is known to be a critical issue in the contexts of both travellers' choices and decisions and freight transportation. The temporal variability of travel time is known as reliability and is affected by numerous factors. Traffic volume, incidents and inclement weather are among the most profound factors, and their effects have been the subject of many studies. The work reported in this article is unique due to the simultaneous implementation of a genetic algorithm (GA) with multiple machine learning (ML) methods. A GA can eliminate overfitting, which is a common problem in ML models. The numerical results showed that the performance of the K-nearest neighbours method was significantly enhanced when a GA was imposed on it. In terms of the stability ratio, a 12% decrease was observed; the mean squared errors for the training set and the testing set decreased, but the reductions were not significant. To further illustrate the advantages of GA implementation, the numbers of predictions with a mean absolute percentage error greater than 0.05 were compared and a notable reduction was found. Sensitivity analysis was carried out to determine how the planning time index responds to fluctuations of independent variables. [ABSTRACT FROM AUTHOR]

Appropriate traffic cooperation at intersections plays a crucial part in modern intelligent transportation systems. To enhance traffic efficiency at intersections, this paper establishes a cooperative motion optimization strategy that adjusts the trajectories of autonomous vehicles (AVs) based on risk degree. Initially, AVs are presumed to select any exit lanes, thereby optimizing spatial resources. Trajectories are generated for each possible lane. Subsequently, a motion optimization algorithm predicated on risk degree is introduced, which takes into account the trajectories and motion states of AVs. The risk degree serves to prevent collisions between conflicting AVs. A cooperative motion optimization strategy is then formulated, incorporating car-following behavior, traffic signals, and conflict resolution as constraints. Specifically, the movement of all vehicles at the intersection is modified to achieve safer and more efficient traffic flow. The strategy is validated through a simulation using SUMO. The results indicate a 20.51% and 11.59% improvement in traffic efficiency in two typical scenarios when compared to a First-Come-First-Serve approach. Moreover, numerical experiments reveal significant enhancements in the stability of optimized AV acceleration. [ABSTRACT FROM AUTHOR]

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications. [ABSTRACT FROM AUTHOR]

Overloaded vehicle traffic accelerates road degradation, leading to early failures and high maintenance expenses. Identifying critical roads in areas like Baghdad, Iraq, is crucial for effective maintenance planning. Road maintenance involves systematic management to uphold road quality and functionality. However, the absence of weighing stations and awareness about critical roads in Baghdad exacerbated maintenance costs. This study aims to present a robust framework for identifying critical roads that necessitate weighing stations and subsequently determining the optimal locations for these stations in Baghdad. Through statistical analysis of responses collected from 61 road maintenance engineers, 22 influential factors for road criticality and weighing station location were identified. Some key factors emerged as crucial in identifying critical roads, including truck traffic volume, maintenance and construction costs, geopolitical importance and lane count. Furthermore, significant considerations for weighing station placement were identified, such as proximity to industrial hubs and drainage structures. Validation of the model through case studies affirmed its efficacy. Policy implications include the need for feasibility studies before weighing station implementation, data-driven decision-making, road criticality assessment and strategic weighing station placement. This research contributes to improved road management practices, aiding in decision-making for transportation infrastructure enhancement in developing countries like Iraq. [ABSTRACT FROM AUTHOR]

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

Vehicular flow rate is an essential measure commonly collected by inductive-loop detectors for transportation agencies to evaluate freeways and highways. Loop detectors are typically located in urban areas due to installation and maintenance costs, and do not provide large spatial coverage. Crowdsourced data provide large spatial coverage, but typically do not capture vehicular flow rates. Therefore, a dynamically weighted ensemble (DWE) comprised of XGBoost and neural network models is proposed to expand the spatial coverage of vehicular flow rates by estimating flow rates for the Phoenix, AZ, metropolitan area using crowdsourced data. The model is evaluated using K-fold cross-validation methods, achieving a cross-validated mean absolute percent error of 21.74%, outperforming all other comparison models. The trained model is then used to estimate vehicular flow rates along highways and freeways throughout the state of Arizona. The proposed method provides transportation professionals with a transferable, cost-effective solution for large-scale flow rate estimation. [ABSTRACT FROM AUTHOR]

Despite the needs of assessing the time-varying queue length for incident traffic management, such imperative information has not yet been available in practice, due partly to the lack of reliable sensor data on many highways plagued by nonrecurrent congestion. Hence, this study first presents an incident queue impact assessment system that allows highway agencies to perform reliable estimate of the queue distance during the incident clearance period with available detector data, and then discusses two alternative models for supporting approximating such impacts on roadway networks without traffic sensors. The proposed system’s major model, grounded in the classical shockwave theory, has augmented the formulations for queue propagation dynamics with the impacts from incoming drivers’ perceptions and responses to the progressively constrained traffic conditions. To cope with the lack of real-time traffic flow information, the first alternative model integrates the increasing available speed data from probe vehicles or other sources with lane blockage patterns to approximate the discharge flow rate of the incident. The second alternative model centers on the methodology and calibration methods for constructing a set of speed– flow rate relationships for different times of day with archived traffic data to approximate the arriving flow rate. With such information, responsible agencies can then proceed the approximation of incident queue variation length and take necessary actions. Performance evaluation of the proposed system with both field incident data and simulated scenarios has confirmed its promising properties, especially for highway networks without reliable traffic surveillance systems. [ABSTRACT FROM AUTHOR]

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person's long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning. [ABSTRACT FROM AUTHOR]

Symmetry in traffic patterns is a fundamental aspect of intelligent transportation systems, aiming to precisely predict traffic flow in real time despite the complex interplay of spatial and temporal factors. This paper presents a novel method of traffic forecasting that incorporates parameters related to road symmetry into a Graph Convolution Network model. Our model is crafted to dynamically adjust to real-time changes in road conditions, including the presence of symmetric and asymmetric road layouts, which substantially influence traffic flow. We have developed a GCN model that not only accounts for standard traffic flow metrics but also integrates a matrix representing road symmetry. The model undergoes training and validation on the METR-LA dataset, showcasing a significant enhancement in prediction accuracy. In the comparative analysis of state-of-the-art methods, our model demonstrated a significant enhancement in performance, achieving 30.68% improvement in Mean Squared Error (MSE) and a 24.28% improvement in Mean Absolute Error (MAE) over the best-performing method. The implications of our research are profound for urban planners, traffic management systems, and navigation service providers, as it offers a more dependable tool for forecasting traffic conditions, aiding in road design, and refining route planning strategies based on the symmetry of road networks. [ABSTRACT FROM AUTHOR]

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed. [ABSTRACT FROM AUTHOR]

Consumer demand for automobiles is changing because of the vehicle's dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet's frame rate from 71.2 to 89.17 and VGG19's frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations. [ABSTRACT FROM AUTHOR]

The increasing development and implementation of Intelligent Transportation System have led to a growing focus on traffic flow prediction. To make these predictions, a large amount of historical and real-time traffic data is collected from various sources such as sensors, cameras, GPS and social media. Predicting traffic flow helps reduce congestion and promotes safer and more cost-effective travel. However, traditional models that use basic networks are not effective in today's scenarios due to the exponential increase in the number of vehicles. Federated Learning provides an opportunity to use learning techniques while addressing privacy concerns when data is spread across different devices or entities. This research explores the use of Federated Learning combined with deep learning methods to analyze and predict traffic flow based on available historical data. Different Federated aggregation techniques and deep learning models are also compared in this work. [ABSTRACT FROM AUTHOR]

Short term forecasting is essential and challenging in time series data analysis for traffic flow research. A novel deep learning architecture on short-term traffic flow prediction was presented in this work. In conventional model-driven prediction method, a critical deviation in prediction accuracy was occurred in face of large fluctuations in traffic flow, while machine and deep learning-based approaches performed well in accuracy study than conventional regression-based models. Moreover, a fusion attention mechanism bidirectional long short-term memory model (ATT-BiLSTM) was proposed due to its bidirectional LSTM (BiLSTM) and attention mechanism units. The model not only dealt with forward and backward dependencies in time series data, but also integrated the attention mechanism to improve the ability on key information representation. The BiLSTM layer was exploited to capture bidirectional temporal and spatial features dependencies from historical data. The proposed model was also trained and validated using freeway toll datasets from Humen Bridge. The results showed that compared with ARIMA and SVR models, the indicators of the proposed model have been significantly improved. The ablation experiments were conducted to evaluate the role of the attention mechanism module. Compared with BiLSTM, CNN and 1DCNN-ATT-BiLSTM models, the MAE, RMSE and MAPE indexes of proposed model were reduced by 0.6–5.9%, 1.6–4.7% and 0.6–22.8%, respectively. More accurate predictions were obtained by the proposed model. The research results are of great significance to improve the level of traffic management. [ABSTRACT FROM AUTHOR]