Vehicular Density Research Articles

The Shared Experience Actor–Critic (SEAC) Approach for Allocating Radio Resources and Mitigating Resource Collisions in 5G-NR-V2X Mode 2 Under Aperiodic Traffic Conditions

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns.

Enhancing Traffic Control with AI Blockchain and Dynamic Computation Techniques

The rapid urbanization and increasing vehicular density in modern cities have led to significant challenges in traffic management and control. As urban areas continue to expand, the demand for more efficient and intelligent traffic control systems has become increasingly critical. This paper presents a novel approach to enhancing traffic management by integrating Artificial Intelligence (AI), Blockchain technology, and Dynamic Computation Techniques. AI is utilized to analyze and predict traffic patterns, enabling real-time adjustments to traffic signals and flow management. Blockchain provides a secure, transparent, and decentralized platform for data sharing and coordination among various stakeholders, ensuring data integrity and trust. The incorporation of Dynamic Computation Techniques allows for flexible and scalable processing of complex traffic data, facilitating rapid decision-making and adaptation to changing conditions. This multidisciplinary approach not only improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems. The findings highlight the transformative potential of combining AI, Blockchain, and advanced computation methods in the field of traffic control.

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Graph Theoretical Models for Enhancing Highway Connectivity and Safety in Vehicular Networks

Vehicular networks play a crucial role in modern transportation systems, significantly impacting connectivity and safety on highways. This paper explores the application of graph theoretical models to enhance both connectivity and safety in vehicular networks. Graph theory, a branch of discrete mathematics, provides a robust framework for modeling and analyzing complex networks, including those formed by vehicles on highways. Our study begins by defining the vehicular network as a graph where nodes represent vehicles, and edges denote communication links between them. We employ various graph theoretical concepts such as connectivity, centrality, and network flow to evaluate and improve the network's performance. Key metrics, including the degree of nodes, clustering coefficients, and shortest path lengths, are utilized to quantify network connectivity and identify critical nodes and edges that influence overall network efficiency. One of the primary objectives is to ensure uninterrupted connectivity in the presence of dynamic and often unpredictable vehicular movement. To this end, we analyze the network's resilience to node failures and propose strategies to enhance robustness using redundancy and alternative routing paths. By incorporating concepts like k-connectivity and network diameter, we develop models that maintain high levels of connectivity despite the removal or failure of multiple nodes or edges. Safety is addressed through the lens of network stability and reliability. We investigate the impact of vehicular density, speed, and communication range on the network's ability to sustain reliable communication channels. Techniques such as dynamic topology management and adaptive power control are proposed to mitigate the risks associated with network fragmentation and communication delays. Furthermore, we introduce optimization algorithms that leverage graph partitioning and community detection to improve the management of vehicular clusters, facilitating efficient data dissemination and reducing the likelihood of congestion-related incidents. The proposed models are validated through simulations that mimic real-world highway conditions, demonstrating significant improvements in both connectivity and safety metrics. In conclusion, the application of graph theoretical models offers a promising approach to enhancing highway connectivity and safety in vehicular networks

Markov-reward based estimation of the idle-time in vehicular networks to improve multimetric routing protocols

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as Tidle. This current study focuses on modeling and analyzing the influence of Tidle on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute Tidle. This equation provides a quick evaluation of Tidle, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our Tidle evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our Tidle metric. Simulation results demonstrate that integrating Tidle as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

A cellular automaton model for mixed traffic flow considering the size of CAV platoon

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed.

“Characterization of residential proximity to sources of environmental carcinogens in clusters of Acute Lymphoblastic Leukemia in San Luis Potosi, Mexico”

BackgroundAcute Lymphoblastic Leukemia (ALL) is the most prevalent neoplasia in children and teenagers in Mexico. Although epidemiological data supports that children's residence close to emissions from vehicular traffic or industrial processes increases the risk of ALL; and the IARC states that benzene, PAHs, and PM 2.5 are well-known environmental carcinogens, there is a gap in linking these carcinogenic hazards with the sources and their distribution from scenario perspective. AimTo identify ALL clusters in the population under 19 years of age and characterize the environment at the neighborhood level by integrating information on sources of carcinogenic exposure using spatial analysis techniques in the Metropolitan Area of San Luis Potosi, Mexico. MethodsUsing the Kernel Density test, we designed an ecological study to identify ALL clusters from incident cases in the population under 19 years of age. A multicriteria analysis was conducted to characterize the risk at the community level from carcinogenic sources. A hierarchical cluster analysis was performed to characterize risk at the individual level based on carcinogenic source count within 1 km for each ALL case. ResultsEight clusters of carcinogenic sources were located within the five identified ALL clusters. The multicriteria analysis showed high-risk areas (by density of carcinogenic source) within ALL clusters. ConclusionsThis study has a limited source and amount of available data on ALL cases, so selection bias is present as well as the inability to rule out residual confounding factors, since covariates were not included. However, in this study, children living in environments with high vehicular density, gas stations, brick kilns, incinerators, commercial establishments burning biomass, or near industrial zones may be at higher risk for ALL.

Impact of detour on traffic flow in branching Koch curve network with bottleneck

There are various routes with and without detours in city traffic network with a complex connectivity. Branching Koch curve fractal has such complex connectivity with singly, doubly, and multiply connecting links. We consider branching Koch curve as a city traffic network. We study the effect of detour routes (bypasses) on macroscopic traffic flow in branching Koch curve network with a bottleneck. The bottleneck effect depends on whether or not there are detour routes. We propose a mathematical dynamic model for the traffic flow with a bottleneck in the directed Koch curve network. The macroscopic dynamic equations of vehicular densities are derived in the Koch curve network. Vehicular densities and currents are obtained at all roads in the network by solving the density equations numerically. The fundamental diagrams (flow-density relations) are derived. The traffic currents (flows) depend highly on the position of the bottleneck due to the connectivity of the fractal network. It is shown where and how traffic congestion is induced by the bottleneck on the directed fractal network.

Emission performance evaluation of urban passenger car fleet through field investigation data in a megacity.

Continued improvements in living standards and the economic well-being in the megacities have led to a huge surge in vehicular density. The worst environmental outcome of the same has been persistent unsafe urban air quality, thanks to vehicular emission. Further, the existing inspection and maintenance programs, conceived to check such emission remain largely ineffective, particularly in developing countries. This is due to the absence of a thorough assessment of the vehicle's compliance with the in-use emission norms generated through reliable field investigation data. To address this gap, the present comprehensive study collected real-time tailpipe emission data from 2040 cars in Delhi, India. Exhaust emission parameters, namely, CO (carbon monoxide), HC (hydrocarbon), and SE (smoke emission), were recorded from both petrol and diesel-driven cars of private ownership, in collaboration with the emission compliance test centers. The performance of cars was assessed in terms of their compliance with the in-use BS (Bharat Stage) emission norms. The one-of-its-kind study reported the petrol cars to be highly compliant toward the BS IV norm while faring even better toward BS II for both CO and HC emissions (80-90%). The conformance to the HC norm was found to be typically better than that for CO (85-90% versus 75-80%). For the diesel-driven cars, BS III compliance levels were reported relatively better compared to BS IV (90% in the case of the former against 80% in the latter's case). Further, the study puts forward a clear indication that the in-use emission norm and maintenance status of cars have a direct and negative relationship with tailpipe emission parameters. Cars of both overseas and domestic origin have almost equal degrees of compliance with the emission norms (over 80% in any case). The study recommends the incorporation of these two critical vehicular variables, i.e., maintenance status and in-use emission standard in the emission certification policy.

Smart Traffic Management System

With the rapid urbanization and increasing vehicular density in modern cities, the demand for efficient and intelligent traffic management systems has become paramount. This paper presents a comprehensive overview of a Smart Traffic Management System (STMS) designed to optimize traffic flow, enhance safety, and minimize congestion in urban areas. The STMS leverages advanced technologies such as artificial intelligence, sensor networks, and data analytics to create a dynamic and adaptive traffic control infrastructure. The key components of the proposed system include real-time traffic monitoring, predictive analytics, and adaptive signal control. Utilizing a network of sensors, cameras, and other data sources, the STMS continuously gathers information on traffic conditions, identifying patterns and potential issues. This data is then processed through machine learning algorithms to predict traffic trends, enabling proactive management strategies. One of the innovative features of the STMS is its adaptive signal control mechanism. Traditional traffic signal timings are often static and fail to respond promptly to changing conditions. The STMS, however, dynamically adjusts signal timings based on real-time traffic data, optimizing traffic flow and minimizing delays. This adaptability is further enhanced by the integration of vehicle-to-infrastructure (V2I) communication, allowing direct communication between vehicles and the traffic management system. Additionally, the STMS incorporates intelligent decision-making algorithms to prioritize emergency vehicles, public transportation, and other critical services. This ensures a swift and efficient response to emergencies while maintaining overall traffic efficiency. Furthermore, the system includes a user-friendly interface accessible through mobile applications and web platforms. This interface provides real-time traffic updates, alternative route suggestions, and other relevant information to empower commuters with the knowledge needed to make informed travel decisions. The implementation of the Smart Traffic Management System holds the potential to revolutionize urban mobility by significantly reducing travel times, fuel consumption, and environmental impact. This paper discusses the underlying technologies, design considerations, and potential benefits of the STMS, highlighting its role in creating smarter and more sustainable urban transportation networks

Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering

Vehicular ad hoc networks (VANETs) are a rapidly evolving field at the intersection of intelligent transportation systems, emphasizing the need for a stable and scalable VANET topology to accommodate growing vehicular densities. The intricate challenge of route selection calls for advanced clustering protocols to bolster road safety and message routing. This research introduces a novel approach to intelligent clustering routing protocols, leveraging heuristic-based solutions built upon an enhanced ant colony optimizer (ACO) framework. The study unfolds in two stages: the creation of a dynamic search space model and the election of cluster heads (CHs). The innovative dynamic aware transmission range parallel Euclidean distance (DA-TRPED) technique establishes a dynamic search space using the parallel Euclidean distance (PED) concept. This approach evaluates vehicular nodes by estimating PED values, reducing the search process’s complexity. Subsequently, an intelligent cluster head is selected by enhancing the dynamic evaporation factor (DEF) within the ACO technique. The experimental validation of the DA-TRPED technique takes place in NS2 simulations, demonstrating superior performance compared to conventional ACO. This enhancement is evident in metrics such as packet delivery, packet drop, throughput, end-to-end delay, and the lifetime analysis of clustered nodes. The proposed approach holds promise for optimizing VANETs, enhancing their stability and scalability while promoting road safety and efficient message routing.

Air pollution analysis in Northwestern South America: A new Lagrangian framework

This study examines the spatiotemporal variations of PM2.5, PM10, SO2, O3, NO, and NO2 concentrations in Northwestern South America (NWSA). We assess the efficacy of existing policies, identify underlying phenomena, and highlight areas for further research. Significant findings have emerged by analyzing reanalysis and in-situ data, employing the WRF-Chem model, and utilizing a new Lagrangian framework designed to overcome some drawbacks common to analysis of pollution Long-Range Transport. Wildfires in the first half of the year and volcanic activity (for SO2) in July–August contribute to over 90 % of the pollutant's advection, leading to high pollution levels in urban areas. SO2 volcanic emissions contribute to secondary PM, explaining the peak in PM concentrations in Cali in July. In the second half of the year, pollutant behavior varies based on factors such as city characteristics, vehicular-volume, air temperature, wind speed, and boundary layer height, and O3 is influenced by solar radiation and the NO/NO2 ratio. Diurnal variations of PM and NOx correlate with vehicular density, SO2 with industrial activity, and O3 depends on solar radiation. Trend analysis reveals decreasing PM10 levels except in three Cundinamarca cities and Cali suggesting the need to implement/evaluate control plans in those locations. Although data is limited, NO and NO2 levels show an increasing trend due to the rising number of vehicles. SO2 levels are decreasing, except in Cali, potentially influenced by the nearby industrial and polluted city of Yumbo. O3 displays a downward trend in most cities, except Bogotá, due to the NO/NO2 ratio favoring O3 increase. These findings provide a starting point for further research to deepen our understanding of NWSA air pollution. Such investigations are essential before modifying existing policies or enacting new ones. Collaborative efforts at the international, regional, and inter-city levels are crucial for effective air quality management.

Robust backbone network based on hybrid selection of relays for multi-hop data dissemination in VANETs

Relay nodes selection as a key word to ensure efficient multi-hop data dissemination in VANETs has received tremendous attention for many years. However, previous literature often fails to address two critical topics. Firstly, the conception of a reliable relay distribution model to ensure information coverage without generating additional overheads. Secondly, the simultaneous exploit of both existing sender-oriented and receiver-oriented relay selection mechanisms to avoid the trade-off among them and improve the overall dissemination efficiency. In this paper, we propose a Robust BACKbone NETwork based on Hybrid Selection of Relays for multi-hop data dissemination in VANETs (BACKNET-HSR). Its novelty lies in adequately addressing the aforementioned topics. To avoid the chaotic distribution of relays and enhance information coverage, we adjust the relay selection and inhibition rules according to a novel concept of message propagation pattern. The goal is to realize a synergy among the potential relay candidates to build an on-the-fly backbone network parallel to the road lines topology in a distributed manner. With the aim of enhancing the relay selection robustness, we propose a tight coupling of both investigated relay selection mechanisms in one hybrid scheme. The objective is to leverage the low latency feature of the sender-oriented mechanism, and the high reachability feature of the receiver-oriented mechanism. Simulation results show that the proposed scheme disseminates data with low delay and high information coverage, outperforming the existing dissemination protocols in different scenarios and under varying vehicular densities.

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

SummaryVehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)‐based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization‐based Node Clustering (HPDBWOA‐NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA‐NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA‐NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs.

Balancing the efficiency and robustness of traffic operations in signal-free networks

Integration of artificial intelligence and wireless communication technologies in Connected Automated Vehicles (CAVs) enables coordinating the movement of the platoons of CAVs at signal-free intersections. The efficiency of the platoon coordination process can be improved by reducing the spacing between successive platoons to increase capacity; however, such improvement in efficiency can have adverse impacts on the robustness of the coordination process. In this research, we balance the trade-off between the efficiency and robustness of traffic operations in signal-free networks at a macroscopic scale. To this end, we use a rule-based approach to express the process of coordinating CAV platoons at intersections as a set of governing equations that provide an analytical basis to develop a stochastic model for traffic operations. We derive the platoon synchronization success probability for a general distribution of the error in synchronizing the movement of platoons in crossing directions and formulate the expected capacity as a function of the synchronization success probability. We then balance the trade-off between efficiency and robustness at a macroscopic scale by adjusting the average spacing set between successive platoons. In urban networks, adjusting the spacing between successive platoons also changes the vehicular density and consequently the traffic speed. We account for the interrelationship between the traffic speed and inter-platoon spacing in balancing the trade-off between the efficiency and robustness of traffic operations using the concept of the Macroscopic Fundamental Diagram (MFD) and extend the stochastic traffic model to the network level. We evaluate the analytical results of the research using a simulation model. The numerical results of the research show that optimizing the system by adjusting the platoon spacing can improve robustness by 13% at the cost of a 4% reduction from the maximum capacity at the network level.

The role of occupancy and transition rate on traffic flow in a percolation-backbone fractal

The occupancy of the road and transition rate have a significant impact on traffic flow in real traffic dynamics. Thus, the macroscopic traffic flow has been investigated in a percolation-backbone fractal with the consideration of occupancy of the road and transition rate. The traffic flow on the fractal network is presented with the help of a cell-transmission graph. The density equations are derived using the speed-matching model. The fundamental diagrams are obtained for urban-scale traffic networks by solving the density equations numerically. The steady-state vehicular densities and traffic currents are plotted for different cases of occupancy and transition rate. It is found that the traffic flow increases significantly with the increase in occupancy of the road and transition rate.

Successive jamming transitions in traffic flow on directed Sierpinski gasket

We study the macroscopic traffic flow and jamming transitions on directed Sierpinski gasket fractal which is closely connected to complex network. We propose a dynamic model for macroscopic traffic flow on directed Sierpinski gasket. The macroscopic dynamic equations of vehicular densities are derived on the directed network. It is shown where and how traffic jam occurs on directed networks. It is found that multiple transitions occur successively with increasing vehicular density. The successive transition points are derived numerically and analytically.

Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration

This research endeavors to advance the realm of parking space surveillance through a meticulously designed methodology situated within the critical context of urban planning and the dynamic landscape of smart city development. Focused on addressing the challenges posed by escalating urbanization and burgeoning vehicular density, our study introduces a carefully curated dataset comprising images of parking spaces annotated with bounding box masks and occupancy labels. The methodology unfolds across distinct phases, commencing with a comprehensive dataset description that unveils its diversity and intricacies. Feature extraction techniques, harnessing the capabilities of cutting-edge architectures such as AlexNet and ResNet-50, play a pivotal role in enhancing pattern discernment, which is essential for accurate detection. The crux of our approach lies in the integration of Neural Networks with optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the innovative Dipper Throated Optimization (DTO). Results are presented without explicit mention of tables and figures, strategically emphasizing the methodology's effectiveness in enhancing parking space detection accuracy. Notably, Dipper Throated Optimization (DTO) emerges as a key contributor to optimized Neural Network performance, achieving an impressive accuracy of 0.9908. This research contributes significantly to the ongoing discourse on intelligent urban planning and sets a promising trajectory for the future of efficient parking space utilization in modern cities.

Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach

The integration of artificial intelligence and wireless communication technologies in communicant autonomous vehicles (CAVs) enables coordinating the movement of CAV platoons at signal-free intersections. The capacity of signal-free intersections can be significantly improved by adjusting traffic variables at a macroscopic scale; however, the resulting improvement in the capacity does not necessarily have a positive impact on the energy consumption of CAVs at the network level. In this research, we develop an analytical model to enhance energy efficiency by optimizing macroscopic traffic variables in signal-free networks. To this end, we adopt a macroscopic modeling approach to estimate the operational capacity by accounting for the stochasticity resulting from the error in synchronizing the arrival and departure of consecutive platoons in crossing directions at intersections. We also develop a macrolevel analytical model to estimate expected energy loss during the acceleration/deceleration maneuver required for resynchronization at intersections as a function of synchronization success probability. We then maximize energy efficiency by minimizing expected energy loss and maximizing expected capacity in a biobjective optimization framework. We solve the energy efficiency problem using an analytical approach to derive a closed-form solution for the optimal traffic speed and the length of the marginal gap between the passage of consecutive platoons in crossing directions through intersections for a (general) normal distribution of the operational error. Having the closed-form solution of the energy efficiency problem, we balance the trade-off between energy loss and operational capacity at a large scale by extending the analytical model to the network level using the Macroscopic Fundamental Diagram (MFD) concept. The results of our two-ring simulation model indicate the accuracy of the proposed analytical model in estimating the macroscopic relationship between the expected energy loss at intersections and the vehicular density in signal-free networks. Our numerical results also show that optimizing the traffic speed and marginal gap length can improve energy efficiency by 31% at the cost of a 16% decrease in maximum capacity.

FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET

In a continuous data stream vehicular network environment, a Traffic Congestion Detection Service (TCDS) receives many periodic information to update and discover road segments with low speeds and high vehicular density. The use of Cloud computing to support the massive amounts of traffic data received from multiple vehicles significantly increases network traffic. Therefore, we propose FogJam, a Fog service to detect traffic congestion directly at the edge of the vehicular network. In the network edge, FogJam leverages sampling and clustering-based methods to reduce the traffic data stream transmitted by all vehicles on the network links to the Cloud. In the Cloud, FogJam is used as a macro-control of all vehicles geographic positions, acquiring traffic flow data to detect traffic congestion. We evaluate FogJam using OMNeT++, Veins and SUMO simulators. The results suggest that FogJam is highly accurate in detecting traffic congestion at a lower cost, even in a high vehicular density scenario. Furthermore, using clustering-based methods, FogJam is able to, on average, reduce the impact on network usage by approximately 70% compared to the sampling methods, while maintaining an acceptable level of congestion detection accuracy.