Dynamic Routing of Emergency Vehicles with Traffic Management System and Implementation on CupCarbon for Smart Cities

Traffic lights play such important role in traffic management to control the traffic on the road. Situation at traffic light area is getting worse especially in the event of emergency cases. During traffic congestion, it is difficult for emergency vehicle to cross the road which involves many junctions. This situation leads to unsafe conditions which may cause accident. An Automatic Traffic Light Controller for Emergency Vehicle is designed and developed to help emergency vehicle crossing the road at traffic light junction during emergency situation. This project used Peripheral Interface Controller (PIC) to program a priority-based traffic light controller for emergency vehicle. During emergency cases, emergency vehicle like ambulance can trigger the traffic light signal to change from red to green in order to make clearance for its path automatically. Using Radio Frequency (RF) the traffic light operation will turn back to normal when the ambulance finishes crossing the road. Result showed the design is capable to response within the range of 55 meters. This project was successfully designed, implemented and tested.

This paper proposes an internet of things (IoT) based intelligent traffic management system that can aid problematic traffic situations in smart cities by classifying congestions via sensory data, and then controlling traffic lights and creating alternate routes for incoming vehicles to the congested zones in order to relieve or avoid congestions completely. The proposed intelligent traffic management system consists of different subsystems such as Test Operation, Supervisory, Traffic Light, and Pathfinder subsystems. The system is represented by flowcharts with their explanations and its operation with some defined scenarios is validated with the CupCarbon simulation environment.

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.

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

In this study, a proposed intelligent traffic management system is presented making use of the wireless sensor network for improving traffic flow.  By making use of the clustering algorithm, VANET environment is utilized for the proposed system. The components of the proposed system include sensor node hardware, vehicle detection system through magnetometer, and UDP protocol for communication between the nodes. The intersection control agent receives the information about the vehicles and by making use of its algorithm, it dynamically changes the traffic light timings. By making use of the greedy algorithm, the system can be enhanced to a wider area by connecting multiple intersections.

The paper considers issues pertaining to creation of a model for controlling road traffic with the purpose to minimize delays on street and road network, which is proposed as an innovative one while developing an intelligent transport system of the large city that is Minsk. The developed model has a complex structure of algorithmic support. The first-level model has been implemented on the basis of fuzzy logic, for which a program has been developed and conditions have been determined, and operation of traffic light at a real local intersection of Minsk, which is included in the automated traffic management system, has been simulated. Innovation in the first-level model is an approach in determining conditions while detecting a fuzzy set without using a standard algorithm that is an algorithm of local flexible regulation. The paper proposes and investigates a model that works on the basis of operationally obtained parameters of traffic flow intensity at characteristic points (sections) of street and road network. Efficiency of the first-level model has been equal to 8 % due to optimization of a traffic light cycle (reduction of transport delays during passage of stop lines). Results of the simulation using the proposed computer program have made it possible to improve efficiency of traffic management on the studied highway (Logoysky trakt) in Minsk city of Minsk by 15 % due to decrease of delay level in case of unilateral coordination. The algorithm has been already implemented as part of the current automated traffic management system in the city of Minsk and it has shown its efficiency. However this efficiency can be increased if it is used together with an algorithm for searching maximum volume of motion in a cycle with a distributed intensity pulse. It has been planned to take into account this specific feature when increasing possibilities for algorithmization of traffic management.