Smart Vehicles for Traffic Management and Systems Using Cloud Computing

2018 ◽  
pp. 178-199
Author(s):  
Ashish Thomas ◽  
Gaurav Singal ◽  
Riti Kushwaha
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Management ◽  
Ad Hoc ◽  
Heavy Traffic ◽  
Lane Changes ◽  
Survey Report ◽  
Stationary Objects ◽  
Control And Management ◽  
Smart Traffic

A vehicular ad hoc network (VANET) is the network of mobile devices as well as stationary objects that can communicate with each other. This technology comprises of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data transmission links. At present, vehicular mobility framework has a lot of limitations, which includes lack of real-time operations, frequent disconnects due to dynamic-restricted topology, tracking vehicle that break rules, lane-changes, exceed speed-limit, etc. These characteristics call for a new type of high class of protocol. This chapter presents a survey report on “smart traffic control” system that incorporates traffic-related parameters to further augment the control and management of vehicular movements on the roadways. This can support efficient management of traffic in the city, and cops can communicate with each other, get real-time, accurate, status update of the traffic, track the vehicular movements, etc. In addition, jam control mechanism can be placed on heavy traffic days to optimize the routes. This system introduces artificial intelligence (AI) that can optimize deployed of cops and find alternate routes for the driver to reach the destination address without much fuel consumption.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<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 Traffic Control System Using Ad-Hoc Wireless sensor Systems

2015 ◽  
Vol 3 (4) ◽  
pp. 333-335
Author(s):  
J. Isaac Henderson ◽  
M. Aravind
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Ad Hoc ◽  
Heavy Traffic ◽  
Wireless Sensor ◽  
Traffic Density ◽  
Data Format ◽  
Infrared Technology ◽  
Traffic Control System ◽  
Signal Time

This paper deals with designing an automatic traffic control  system which works on principle of TRAFFIC DENSITY monitored by  Sensors on each side which provides direct information to microcontroller  which rerforms decision making to allow traffic based on density. The three density zones are low, medium and high. In each zone an ad hoc sensor is placed. Each sensor will check the presence of the vehicle in the zone using infrared technology and then ad hoc sensor sends the data to master ad hoc. To locate the sensor, each sensor of different zone is addressed by user and that address is fed to the master ad hoc sensor. This master ad hoc sensor will arrange the data from various sensors in an 8 bit data format. It then performs the required processing to determine the green signal time for each side. It has an exceptional system for high priority vehicles like ambulance, as it senses the direction of arrival of these vehicles and gives a green corridor. The main advantage over conventional system is that a side with heavy traffic doesn’t have to wait unreasonably while a side with no/less traffic gets an equal  amount of time as that of heavy traffic side which is irascible. This is an improved system based on preference for urgency/density of traffic. This can prove useful in especially Junctions of importance, thereby mediating traffic flow correctly.

scholarly journals Proposed SMART Traffic Control Signal in Brunei Darussalam

2015 ◽  
Vol 15 (2) ◽  
pp. 277
Author(s):  
Bibi Rawiyah Mulung ◽  
Andino Maseleno
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Signal ◽  
Control Signal ◽  
Time Signal ◽  
Signal Control ◽  
Traffic Light ◽  
Brunei Darussalam ◽  
Actuated Signal ◽  
Smart Traffic

This paper presents proposed SMART (Systematic Monitoring of Arterial Road Traffic Signals) traffic control signal in Brunei Darussalam. Traffic congestion due to stops and delays at traffic light signals has much been complained about in Brunei Darussalam as well as across the world during the recent years. There are primarily two types of traffic signal controls in Brunei Darussalam. The most common one is the fixed or pre-timed signal operation traffic light and the other one is the actuated signal operation traffic light. Although the actuated signal control is more efficient than the fixed or pre-fixed signal control in the sense that it provides fewer stops and delays to traffic on the major arteries, the best option for Brunei Darussalam would be to introduce smart traffic control signal. This type of traffic signal uses artificial intelligence to take the appropriate action by adjusting the times in real time to minimise the delay in the intersection while also coordinating with intersections in the neighbourhood. SMART Signal simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time signal performance measures, including arterial travel time, number of stops, queue length, intersection delay, and level of service. In Brunei Darussalam, where we have numerous intersections where several arterial roads are linked to one another, The SMART signal traffic control method should be implemented.

scholarly journals Context-Aware Intelligent Traffic Light Control through Secure Messaging

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mükremin Özkul ◽  
Ilir Capuni ◽  
Elton Domnori
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Heavy Traffic ◽  
Waiting Times ◽  
Traffic Information ◽  
Context Aware ◽  
Traffic Flows ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Simulation Results

In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles.

scholarly journals Improving Performance in Trust Based Multi-Link-Rat Communication for V2x Heterogeneous Vehicular Network

2021 ◽  
pp. 458-464
Author(s):  
G. Baskaran ◽  
G. Pragathi ◽  
S. Prithika ◽  
P. Rajeswari ◽  
B. Rubasri
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Ad Hoc ◽  
Road Traffic ◽  
Vehicle Speed ◽  
V2v Communications ◽  
Real Time Information ◽  
Traffic Detection ◽  
Time Information

The dynamic nature of vehicular networks imposes a lot of challenges in multi hop data transmission as links are vulnerable in their existence due to associated mobility of vehicles. It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Here propose a distributed heterogeneous V2V communications algorithm that allows each vehicle to dynamically select the RAT that is more suitable at each point in time. Multi-link is the capability of a device to communicate using multiple wireless links simultaneously. Multi-RAT is the capability of a device to communicate using different RATs. To propose a Predictive Routing based on Markov Model (PRM) to ensure more reliable and timely data transmissions in VANETs. In the case of accident management, emergency messages may be sent to a pre-determined road rescue site upon the occurrence of an accident, such as a crash on the highway during a snow day and a car spontaneous combustion due to the stored explosives. PRM can facilitate the transmission of real-time information from vehicles to a road traffic controller for more efficient traffic management. Rather than using passive traffic detection through sensors, the real-time reports of traffic data through V2V and V2I can avoid the costs of installing and maintaining a large number of sensors.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals Smart Accident Detection and Switching of Traffic Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 1142-1145
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Traffic Signals ◽  
Traffic Signal ◽  
Rf Receiver ◽  
Mems Sensor ◽  
Traffic Control System ◽  
Traffic Management System ◽  
Smart Traffic ◽  
Accident Detection

An accident is one of the major causes of unnatural and untimely death. This is one of the serious issues throughout the world. Most of the accidents occur due to vehicle factors, improper traffic management, and lack of timely help. With the increase in the number of vehicles, it may be little hard to keep away from such accidents on road. The main objective is to implement the new advancements in saving human lives by detecting the occurrence of the accident in a vehicle and by directing the ambulance to the accident location without time delay. Also by implementing smart traffic control system, the ambulance moves to medical centre in an effective way without any stall in the traffic signals. Automation of accident detection is implemented by sensor-based ambulance management with the smart traffic management system. It consists of Crash sensor and MEMS sensor for detecting the accident in the vehicle and RF transmitter on the ambulance to communicate with the RF Receiver located on the traffic signal. This helps the ambulance to cross the junction switching the signals from Red to Green when the signal is received by the ambulance.

Four-Dimensional Interactive Visualization System for Transportation Management and Traveler Information

2005 ◽  
Vol 1937 (1) ◽  
pp. 152-158 ◽  
Author(s):  
Michael L. Pack ◽  
Phillip Weisberg ◽  
Sujal Bista
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Management ◽  
Prototype System ◽  
Traveler Information ◽  
Visualization System ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Real Time Traffic ◽  
Message Signs

This research developed a system for visualizing four-dimensional (4-D), real-time transportation data for the major road networks of Washington, D.C., Northern Virginia, and the entire state of Maryland. The effort employed a combination of OpenGL and other modeling techniques to develop a scalable, highly interactive 4-D model using available geographic information system (GIS) and transportation infrastructure data in conjunction with real-time traffic management center data. The prototype system interacts with real-time traffic databases to show animations of real-time traffic data (volume and speed) along with incident data (accident locations, lane closures, responding agencies, etc.). A user can “fly” or “drive” through the region to inspect conditions at an infinite number of angles and distances. The program also allows users to monitor the status of and interact with traffic control devices such as dynamic message signs, closed-circuit television feeds, and traffic sensors and even view the location of emergency response vehicles equipped with Global Positioning System transceivers. Because the system uses standard GIS data and relatively standard transportation databases to derive traffic measures, it can be scaled to incorporate other states and agencies.

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