The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS.

Vehicle tailgating or simply tailgating is a hazardous driving habit. Tailgating occurs when a vehicle moves very close behind another one while not leaving adequate separation distance in case the vehicle in front stops unexpectedly; this separation distance is technically called “Assured Clear Distance Ahead” (ACDA) or Safe Driving Distance. Advancements in Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV) have made it of tremendous significance to have an intelligent approach for connected vehicles to avoid tailgating; this paper proposes a new Internet of Vehicles (IoV) based technique that enables connected vehicles to determine ACDA or Safe Driving Distance and Safe Driving Speed to avoid a forward collision. The technique assumes two cases: In the first case, the vehicle has Autonomous Emergency Braking (AEB) system, while in the second case, the vehicle has no AEB. Safe Driving Distance and Safe Driving Speed are calculated under several variables. Experimental results show that Safe Driving Distance and Safe Driving Speed depend on several parameters such as weight of the vehicle, tires status, length of the vehicle, speed of the vehicle, type of road (snowy asphalt, wet asphalt, or dry asphalt or icy road) and the weather condition (clear or foggy). The study found that the technique is effective in calculating Safe Driving Distance, thereby resulting in forward collision avoidance by connected vehicles and maximizing road utilization by dynamically enforcing the minimum required safe separating gap as a function of the current values of the affecting parameters, including the speed of the surrounding vehicles, the road condition, and the weather condition.

Carotid arteries are the major blood vessels that carry oxygenated blood to the brain and face. Carotid artery disease is characterized by the narrowing of the carotid arteries due to plaque buildup at the arterial walls, leading to major consequences such as brain stroke and death. Hemodynamics such as wall shear stress (WSS) and velocity distribution can be employed to investigate the severity and location of stenosis in the carotid arteries. Furthermore, the potential of stenosis in other regions along the carotid arteries is affected by local variations in hemodynamics. To investigate other potential high-risk regions, a comprehensive blood flow model was utilized through a reconstructed three-dimensional patient-specific geometry with realistic boundary conditions. The developed model was numerically simulated and validated. The results indicated that hemodynamics can be successfully used to investigate the degree of stenosis severity and location. Two different locations along the carotid artery were examined. At location 1 (near the artery bifurcation) of the common carotid artery, the WSS increased from 11 Pa in the case of 42% stenosis to 27 Pa in the case of 61% stenosis and 263 Pa for 84% stenosis. Such values significantly decreased at location 2 (far from the artery bifurcation) because of the increasing distance from the artery bifurcation. In addition, the maximum value of the oscillatory shear index (OSI) downstream of the stenosis throat reached 0.5, and the outlet mass flow rate changed significantly with varying stenosis location. Carotid artery stenosis affects the artery bifurcation, which appears to be at high risk because of thrombus and arterial wall rupture, which are clearly indicated by the WSS and OSI values in this region. The current findings support the efficient use of a computational fluid dynamics approach for the diagnosis and prediction of carotid artery stenosis.

Cardiovascular system abnormalities can result in serious health complications. By using the fluid–structure interaction (FSI) procedure, a comprehensive realistic approach can be employed to accurately investigate blood flow coupled with arterial wall response. The hemodynamics was investigated in both the coronary and carotid arteries based on the arterial wall response. The hemodynamics was estimated based on the numerical simulation of a comprehensive three-dimensional non-Newtonian blood flow model in elastic and rigid arteries. For stenotic right coronary artery (RCA), it was found that the maximum value of wall shear stress (WSS) for the FSI case is higher than the rigid wall. On the other hand, for the stenotic carotid artery (CA), it was found that the maximum value of WSS for the FSI case is lower than the rigid wall. Moreover, at the peak systole of the cardiac cycle (0.38 s), the maximum percentage of arterial wall deformation was found to be 1.9%. On the other hand, for the stenotic carotid artery, the maximum percentage of arterial wall deformation was found to be 0.46%. A comparison between FSI results and those obtained by rigid wall arteries is carried out. Findings indicate slight differences in results for large-diameter arteries such as the carotid artery. Accordingly, the rigid wall assumption is plausible in flow modeling for relatively large diameters such as the carotid artery. Additionally, the FSI approach is essential in flow modeling in small diameters.

Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.

The number of road accidents increases and causes many problems. Many people die and injured. In addition, that causes many economic, social and psychological problems that have negative impact to the development of the world. The main cause of the majority of these accidents is due to the violation of the traffic rules: driving with high speeds, crossing a red-light signal, not keeping sufficient distance with the front vehicle in the highways, driving in the wrong reverse direction, etc. As the number of roads and streets are very large and the total length of these roads is very long, there is no way to fully monitor all of them all the time by Traffic Patrol or camera systems.This paper proposes a system to automatically and autonomously detect and record the traffic violations without the help of the human being. We propose the development of a device (on-board unit) that will be placed inside the vehicle and be secured so that the vehicle's driver cannot stop it. This on-board unit detects and records the major traffic violations committed by a vehicle in real time at any time in any place. In addition, we propose an automatic reporting system to send the committed traffic violations to the traffic authority systems.