Hierarchical clustering analysis framework is developed to identify benchmark and critical regions for effective road safety strategies. The regions are grouped based on agglomeration coefficient of mutually exclusive crash causation parameters. Subsequently, regions from groups with lower than a threshold index value are selected as benchmark for the poorly performing critical counterparts. Euclidean distance-based Ward's, median and centroid clustering techniques are explored through a case study of Indian states and Union Territories. As per data between 2006 and 2015, fatal crash percentages of driving under influence of drug and alcohol, excessive speeding, vehicle malfunction and road conditions related crash causation parameters, severity index and its growth rate are assessed based on respective threshold values of 6.35%, 43.28%, 2.42%, 1.79%, 26.7 and 3.1%. These are the national average of respective indices. It demonstrated the unique application of hierarchical clustering analysis in benchmark and critical region identification.

Hierarchical clustering analysis framework of mutually exclusive crash causation parameters for regional road safety strategies.

Automatic vehicle monitoring has turned out to be a very crucial scenario in the current years. It may develop into possibility by executing the following technologies. This project targets to propose a system, which detects speeding vehicles over a specific speed limit and immediately report to concerned authorities. At present, road accidents rates have raised so, there is a necessity for developing a system that detects an over speeding vehicle. The implementation of present Smart Vehicle Overspeeding Detector using Internet of Things determines all the road traffic information automatically with intelligence. The smart vehicles are suitable with overspeeding detector that has capability for recording, storing and information sharing about the vehicle's speed. The system contains GPS module, Radar, Google maps and IoT module. The safe regions are identified automatically using GPS and IoT technologies. Electronic tracking device runs in 12 V lithium batteries with network of GPS sensing and IoT implementation. The battery life of this device is range from 5–10 hours. A smart vehicle overspeeding sensor is employed and is combined with IoT in order to decrease the vehicle's speed at particular places like accident prone zones. If this smart sensor technology is used the safety parameters, then avoidance of accidents may be attained. The system sends the data wirelessly. If the overspeeding vehicle is detected, then the sensor alerts by sounding an alarm. The purpose of the proposed sensor is to decrease high death rates because of accidents in Middle East countries and in Oman.

IoT based framework for Vehicle Over-speed detection

Automated license plate recognition (ALPR) technology is a powerful technology enabling more efficient and effective law enforcement, security, payment collection, and research. A common license plate standard was adopted by the member states of the Mercosur trading bloc (Argentina, Brazil, Paraguay and Uruguay) and consequently requires an upgrade to the ALPR software used by law enforcement and industry. Due to the scarcity of real license plate images, training state-of-the-art supervised detectors is unfeasible unless data augmentation techniques and synthetic training data are used. This paper presents an accurate and efficient automated Mercosur license plate detector using a Convolutional Neural Network (CNN) trained exclusively with synthetic imagery. In order to obtain the synthetic training data, Mercosur license plates were faithfully reproduced. Digital image processing techniques were employed to reduce the domain gap and a CNN with basic image manipulation was used to embed the artificial licensed plates in to realistic contexts. The trained model was then validated on real images captured from a parking lot and a publicly available traffic monitoring video stream. The results of experiments suggest detection accuracy of about 95% and an average running time of 40 milliseconds.

Brazilian Mercosur License Plate Detection: a Deep Learning Approach Relying on Synthetic Imagery

Nowadays road accidents are the major concern in machinery life of humans. Lots of people have lost their lives, properties, family members due to the misjudged act in driving and the curiosity of over-speed. So it is important to define a system to protect the people from unexpected collision not completely at least save their lives. Hence this project is to track the speed of the vehicle and alert the vehicle about speed variations as well as capture its information from number plate. As widely preferred recent technology of AI with machine learning has been used to visualize the clear data from the picture captured by sensing camera placed at appropriate places. Once the data is extracted it will be sent to the control room to track and penalized the vehicle's owner for the violation of traffic rules. This project is makes use of Arduino UNO, machine learning along with IOT. The accurate data that is being collected is recognized through machine learning that is being enabled using IOT is used to segment and recognize the character and sent via Wi-Fi module to the server. This would be an efficient method for detecting and tracking of vehicles of vehicles in the current situation, this will be greatly useful to avoid traffic congestion in urban areas, avoid accidents in hill side turnings, national highways. This system is implemented in Protues software along open CV python that is being worked along with ML.

An Experiment Analysis on Tracking and Detecting the Vehicle Speed using Machine Learning and IOT

Sensor based health monitoring and controlling of vehicle using IoT

The aim of project is to design an automated speed detection system which can detect the speed of vehicles and if over-speeding occurs, extract the license number of vehicle and send it via email to Toll Plaza to charge fine. The system is designed for Punjab license plates which are currently implemented in Pakistan. Doppler effect phenomenon is used for the speed measurement. If over-speeding is detected, a camera automatically captures the picture of the vehicle and Digital Image Processing (DIP) techniques are applied to extract the license number. MATLAB is used for image processing. Afterwards, the extracted license plate number is sent via email to Toll Plaza. The results show that the proposed system successfully detects over speeding, extracts the license plate, has high performance and can be deployed on roads to check for over speeding.

M. Asad Iqbal

Papers

Automated over speeding detection and reporting system