ADVANCED GPS TRACKING SYSTEM FOR PETS

Objectives: To observe the road traffic accidents pattern across the states in India and to conclude whether the expenditure on road and transportation infrastructure is really justified with the traffic safety across the country. To decide the road safety policies across the states in India using backward elimination approach of regression model. Methods/Analysis: This study attempts to show statistical relations between road traffic accidents with predictive variables; number of vehicles registered, total length of roads, total number of road accidents and total expenditure on transportation infrastructure in the states of India. Time series data covering the period 2001 to 2013 was used on yearly road traffic accidents and other predictive variables for all 28 states of India. Findings: In recent decades, road accidents are becoming one of the most growing concerns in India. As mentioned in report of Ministry of Road Transport & Highways Government of India, road accidents impose significant costs which are about 3% GDP for India since 1999 to 2000. In 2004, India was 9th leading cause of death and expected to be 5th leading cause of death by 2030 worldwide. These facts are enough to catch attention of Government agencies and researchers towards safer traffic solutions. The results from the analysis shows few findings like expenditure on transportation infrastructure may not be an important predictive variable for road traffic accidents for pan India situations. Finally, if all the states of India partitioned in different clusters according to the behavior of states, the study states that there exists a strong positive correlation between road traffic accidents, number of vehicles registered and total length of roads. The relationship found to be statistically significant at 5% level of significance for each cluster of states. Applications/Improvements: The results obtained in study can assist transportation departments and Government bodies to implement better strategies across the country to ensure road traffic safety and reduce losses in terms of money and infrastructure costs.Keywords: Policies in India, Regression Analysis, Road Accidents, Transportation, Road Safety

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

Vehicle accidents are major concern today in traffic management systems which result in an increasing number of deaths. India is currently in top position in road accidents around the globe. This leads to a huge number in future with growing population and other causes e.g. bad road conditions, reckless driving, delay in medical help, etc. To overcome these, a proper accident detection and notification system is required. Here comes our android app based on IoT sensors (Accelerometer, Gyroscope) of smartphones used in our daily lives. The app automatically detects an accident from sensory data and sends notification to emergency numbers so that victims receive immediate medical assistance. The app covers most of the solutions in this regard in post-accident scenarios, such as accident detection, sending notifications, and a few which are applicable for alerting people about accidents and handling traffic congestion scenarios.Later includes warning drivers while entering an accident-prone zone, suggesting an alternate route to avoid congestion in the accident occurrence area, etc. The performance is analyzed by delay time, false alarm rates and it outperforms the existing systems in terms of different features. Thus, it is user-friendly, reliable, and cost effective.

Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using long‐short term memory (LSTM) network for automatic detection of freeway accidents. The LSTM‐based framework increases class separability in the encoded feature space while reducing the dimension of data. The experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 min with a true positive rate of 0.71 and a false positive rate of 0.25 which outperforms other competing methods in the same arrangement.

The safety of vehicles is extremely essential for public vehicles. Vehicle accident prevention is challenging one. In order to bring a solution for problem to implement best solution. In our vehicle accident rescue system can be developed through the application oriented ignition control(tracking locking), accident detection with efficient vehicle management system. This technique helps to find out the exact location of the accident and with the help of server an emergency vehicle can be sent to the exact location to reduce the human loss. Identify the vehicle accidental location by using global positioning system (GPS) and global system mobile communication (GSM). The rapid growth of technology and infrastructure has made our lives easier. The advent of technology has also increased the traffic hazards and the road accident take place frequently which causes loss of life. Our paper will provide static solution to this drawback by using “ARDUINO”. This system using arduino controller connected wirelessly transmit the data when the vehicle is damaged due to accident. This is one of the simplest methods to improve sensitivity using arduino controllers.

Manufacturing and sales of vehicles these days has increased due to its high demand. Due to increase in usage of vehicles, an alarming situation has been created for road accidents. Percentage of road accidents is getting increased year to year. Fatality rate due to accidents is also becoming higher. The reason behind the fatality rate with road accidents is not only the precautions taken while driving the vehicle but also the delay in emergency services. Many lives can be saved if the injured person is given a timely treatment. There are many reasons for the delay in timely treatment of injured persons. The reasons are delay in communication to the nearby hospitals and also the heavy traffic which can be the reason for the delay in ambulance service to reach the injured. A smart phone[1] based automatic detection of accident and notification system with IOT (Internet of Things) is proposed which can help in not only detection of accident but also a notification is sent to the nearby hospitals using a smart phone. Here external pressure sensor is integrated to detect the external force on the vehicle body. In this paper a measurement is done with the parameters like speed, accelerometer and change in angle with GPS.

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.

Background. Fatal road traffic accidents constitute a significant public health problem. Aim. The aim of the study was to analyze road traffic accidents in Sana’a city, Yemen, during 2013–2015. Methods. The required information and data of accidents were collected, respectively, from police reported road accident statistics, Sana’a city. 11684 cases of road traffic accidents during period 2013–2015 were studied. Results. 840 people were killed and 9760 people were injured in RTAs during 2013–2015 in Sana’a city. The accident rates per 100000 of the population were 234.8 in 2013, 180.3 in 2014, and 92.2 in 2015. Mortality rates per 100000 of the population were 15.17 in 2013, 12.2 in 2014, and 8.9 in 2015. High speed was the single most important factor responsible for accidents, deaths, and injuries accounting for 38.1% of RTAs followed by drivers’ fault accounting for 18.9% of the total RTAs. The most common age group involved was ≥18 years. Males accounted for 83.1% of killed and injured persons. Most frequent victims of road traffic accidents were vehicles motor (48.9%) followed by pedestrians (38.1%). More than half of RTAs occurred on the straight road. Conclusion. RTAs are a major source of public health concern in Sana’a city, Yemen. Preventive measures to reduce the burden of mortality and morbidity due to road accidents should be introduced.

Road accidents are often caused by short abnormal events, including traffic violations, abrupt change in vehicular motion, driver fatigue, etc. Observing an accident event from the right camera perspective plays a crucial role while detecting accidents. However, it may not be possible to capture such abnormal events from a limited camera perspective. We present a deep learning framework to analyze the accident events recorded from multiple perspectives. First, we estimate feature similarity in videos recorded from multiple perspectives. We then divided the video samples into high and low feature similarity groups. Next, we extract spatio-temporal features from each group using two-branch DCNNs and fuse them using a rank-based weighted average pooling strategy followed by classification. We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. However, our dataset is large and multi-perspective that can be used to validate ITS-related tasks such as accident detection, accident localization, traffic monitoring, etc. The dataset contains 400 accident events with a total of 2000 videos. We provide temporal annotations of all videos. The proposed framework and the dataset have been cross-validated with latest accident detection baselines trained on real-world road accident videos and vice-versa. The sub-optimal detection accuracy obtained using the baselines indicates that the proposed framework and the dataset can be useful for ITS related research. Code and dataset is available at: https://github.com/draxler1/MP-RAD-Dataset-ITS-

Traffic Hazards is one of the major problems facing across the world. One of the major causes of traffic hazards is increase in vehicles and dense population. Reducing traffic hazards is one the major challenges as majority of the deaths across the world are due to road accidents. Hence there is a need to provide better transportation facilities that can reduce the ratio of road accidents and save life's of people. One of the solution that is proposed in this paper is using IR sensors and Arduino Uno technology. The system has two phases-Accident Detection and Accident Prevention. The detection phase is carried out using IR sensors that could detect and alert the people by sending SMS using GSM module that contains predefined numbers and accident location using GPS module. Second Phase, Accident prevention is carried out using IR sensors by warning the driver about the neighboring vehicles when the distance between them is beyond the threshold value. Simulation results and Prototype is presented in this paper.

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

This article describes the potential contribution of alcohol and illicit drugs to increased risk of road accidents in Italy. Blood and urine samples were taken from patients admitted to five Emergency Rooms (ER) in metropolitan Florence, Italy, due to road traffic accidents within 3 hours of the accident. Seven hundred and thirty-five patients were tested to determine if alcohol and illicit drugs (amphetamines, opiates, cocaine, methadone, and cannabis) were present in urine and blood. Approximately 22% of patients were positive for at least one substance at night-time (20:00 to 5:59), and 18% were positive on the weekends (20:00 Friday to 05:59 Monday). Almost 80% of all alcohol-positive patients had a blood-alcohol concentration (BAC) equal to or exceeding the legal limit. Findings indicated that alcohol and illicit drugs were important contributors to traffic accident risk and suggest prevention strategies for alcohol-related road accidents in Italy may need to follow those employed in other countries, including frequent and highly visible enforcement of drunk-driving laws.

Road traffic accidents in Saudi Arabia have become a serious issue because many of these accidents lead to deaths, injuries, and financial losses. Human lives are often lost in road accidents due to the delay in accident detection by medical assistance. In fact, the accident’s location and the driver’s personal information are considered critical information that plays a vital role in preserving human life. Additionally, previous studies have found a limitation in the encryption of sensitive data; in fact, a leak of private information is thought to be one of the challenges that restrict the use of IoT devices. To resolve this problem, this research presents an intelligent security framework, and an Internet-of-Things-based system is proposed for immediate accident detection. Thus, this system requires the highest level of security and privacy to maintain the driver’s privacy. Moreover, the design science research methodology was followed to design and evaluate the artifacts. Thus, the study’s research resulted in the ability to design a secure and effective IoT-based system to detect and report a car accident instantly. In addition, the message is encrypted using Elliptic Curve Integrated Encryption and sent through Message Queuing Telemetry Transport over GSM. The study’s overall results show the flexibility with which the proposed artifact can be used for other purposes related to the IoT security framework to send and encrypt critical information.

Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.

The main factor causing car accidents is speed. Many lives could have been saved if emergency services had been able to gather information about the tragedy and reach in time. The main project's objective is to develop an accident detection system that makes use of several different parts to alert the rescue crew about an accident. An efficient automated collision detection system that instantly alerts emergency services to the accident site is crucial for protecting valuable human life. The recommended approach deals with accident detection and notification. After reading it, it sends the affected vehicle's exact latitude and longitude to the nearest emergency response provider. The project's goal is to locate incidents and alert the rescue team as soon as possible.