phoenixabhi2002 / roadguard-test

RoadGuard

Final year B.Tech project.

Team Members:

ENG20CT0002 - Abhijeet Sahoo
ENG20CT0018 - Prajjwal Mishra
ENG20CT0024 - Sathvik P
ENG20CT0027 - Shubham Raj Kashyap

Abstract:

According to the latest statistics disseminated by the World Health Organization (WHO), the global incidence of traffic accidents constitutes a profoundly disconcerting issue. These catastrophic events claim the lives of a staggering 1.2 million individuals annually, while an additional 50 million people suffer injuries. This grim toll translates to an approximate daily average of 3,300 fatalities and 137,000 injuries, posing a dire threat to both human lives and property safety. In tandem with these human costs, the direct economic repercussions of these accidents amount to a staggering $43 billion. Within the realm of traffic safety research, road accident prediction stands as a paramount focal point. The manifestation of road traffic accidents is intricately influenced by multifarious factors, encompassing the geometric attributes of road infrastructure, traffic patterns, driver characteristics, and the environmental conditions of the road. A multitude of studies have been conducted to prognosticate accident frequencies and scrutinize the underlying traits of traffic accidents. These investigations encompass endeavors such as the identification of hazardous locations or hotspots, analyses of accident injury severities, and the exploration of accident duration patterns. Some inquiries delve into the underlying mechanisms governing accidents, with consideration of additional factors like weather and road lighting conditions. Among the notable contributions to this domain, Lee et al. have pioneered the development of a probabilistic model that establishes a connection between significant crash precursors and alterations in crash likelihood. Furthermore, Abdel has constructed a previous crash prediction model through the adept utilization of the matched case-control logistic regression technique. Remarkably, a specific approach tailored to empower traffic authorities to forecast accident-prone areas at precise times remains conspicuously absent. The prediction of traffic accidents assumes a pivotal role in the comprehensive planning and administration of traffic systems, a task exacerbated by the inherent randomness and nonlinearity inherent in the elements governing traffic accidents. These encompass the intricate interplay of human behavior, vehicular dynamics, road infrastructure, climatic variables, and other intricate factors. The conventional linear analytical methods have proven inadequate due to limitations such as insufficient data and susceptibility to noise interference, ultimately yielding unsatisfactory predictive outcomes. Given these impediments, the conventional Backpropagation (BP) neural network emerges as an imperfect solution plagued by issues such as convergence to local minima, protracted training periods, and suboptimal accuracy. In contrast, the proposed model, whose details are beyond the scope of this abstract, demonstrably outperforms the BP network with an impressive 7.8% increase in predictive accuracy. This innovative approach presents a promising avenue for advancing our understanding of and response to the complex challenge of traffic accident prediction.

Keywords: Traffic accidents,Road accident prediction,Probabilistic model,Logistic regression,Neural network .

Problem Statement:

Road traffic injuries and fatalities constitute a critical global public health crisis, with an alarming 1.35 million deaths and 20~50 million nonfatal injuries reported annually by the World Health Organization (WHO). In India, the situation is dire, with over 1.5 lakh lives lost in road accidents in 2022 alone. The primary cause of these casualties is attributed to overspeeding vehicles. The Ministry of Road Transport and Highways' 2022 report revealed a 0.46% increase in road accidents compared to the previous year. Addressing this escalating problem is paramount to ensuring the safety and well-being of the public, necessitating urgent and comprehensive interventions in traffic management and safety protocols.

Proposed Solution:

An ML powered web app which predicts accidents severity based on the current conditions. It will be trained with 1.6 million accident records over 2005-2015. More data means greater accuracy. The purpose of such a model is to be able to predict which conditions will be more prone to accidents, and therefore take preventive measures. We will even try to locate more precisely future accidents in order to provide faster care and precaution service. According to the predicted severity, a message will be sent to the traffic police to take preventive measures.

Literature survey:

Project Architecture:

Model Flowchart:

Road Map:

Introduction Video:

about_problems_of_road_V1.mp4