Introduction
Developing
an explainable AI model for road accident
risk prediction is a revolutionary and fresh research approach in minimizing road accidents using
computer science concepts. This paper presents a survey of different
approaches used in the development of systems and models to reduce road
accidents. This paper also discusses the foundational concepts of
explainable AI applied in the risk prediction. The researcher carries out
a detailed analysis of 20 papers that are related to the development of an AI
model for road accident risk prediction. The researcher utilizes her
survey and study to propose and design her work on the prediction of
risks based on traffic, weather, and road condition data.
INTRODUCTION
Road accidents have long been a constant impediment that shadows urban
transportation. As such, most developing countries face road safeties problem. [1].
It is a major concern that is often caused by either or all of the following factors:
traffic density, adverse weather, and poor road conditions. Despite substantial
differences among the many traffic signal control models developed in the past,
they can be roughly classified by two approaches. The first approach is
developed mainly for unsaturated traffic. A basic assumption of this approach is
that traffic flows more or less at the
design speed or in the uncongested regime of a flow-density relationship.
Another basic assumption is that delay is represented by the results of classical
queuing theory. [2]. The practice of utilizing machine learning models in
these kinds of scenarios to predict accident risks have long been applied.
The role of the models is often likened to “black boxes,” due to how the models
have a limiting adoption in real-world traffic management systems. As such,
there is a need for a predictive model that not only improves accuracy but also
provides interpretable policymakers authorities. results and for transportation
Explainable AI (XAI) addresses this limitation by providing interpretable
outputs that clarify why a prediction was made rather than simply presenting an
outcome. [3]. XAI enables transparency in the internal decision-making process.
[4]. Aside from this, it also ensures that the information is digestible and easily
understood by both technical and non technical people. This kind of
transparency is important especially since it is concerning of road safety and
the decision of every person in the road have consequences that directly affect
human lives, public safety, and policy making. For instance, when an AI system
predicts a high probability of an accident in a particular area, XAI can reveal
whether this risk is due to excessive rainfall, heavy traffic congestion, poor
visibility, or inadequate road design. With the use of traffic, weather, and road
condition data, and then combining these with the advanced XAI techniques,
researchers and practitioners can move beyond simple accident forecasting and
instead shift the focus on interventions and improve the existing AI-driven safety
systems. As such, this allows policymakers, traffic engineers, and law
enforcement agencies with actionable that are both reliable and
explainable, leading to evidence-based decision-making and more effective
accident prevention strategies. This study utilizes various computer
science fields and concepts, such as data science, artificial intelligence, and
human-computer interaction. It is also significant in a way that it helps in
improving the issue of road accidents in urban transportation by addressing the
transparency gap in AI models. It demonstrates how explainability can
move AI beyond theoretical performance metrics and into real-world, safety-critical
domains. This paper is divided into various
sections. Section II presents the general research framework. Section III presents
the different approaches of risk management systems and models.
Section IV presents research that has already been done on AI models for risk
prediction. Section V presents a survey of different approaches of the risk
prediction systems and models.