Survey of Predictive Models for Safe Route Predicting Using Machine Learning Techniques

Year : 2024 | Volume :11 | Issue : 01 | Page : 13-22

T. Thilagavathi

A. Subashini

  1. Research Scholar Department of Computer and Information Science, Annamalai University, Annamalai Nagar Tamil Nadu India
  2. Assistant Professor Department of Computer Application, Government Arts College, Chidambaram Tamil Nadu India


Safe route prediction is essential for the well-being and security of individuals in urban and rural environments. Machine learning techniques leverage historical data, real-time information, and algorithms to estimate the safety levels of different routes. The objective of safe route planning is to minimize risks, including crime-prone areas and accidents, reducing potential harm, property damage, and emotional distress. However, challenges arise from the complex and dynamic nature of urban environments, such as spatial and temporal variability, data availability and quality, and real-time considerations. Advanced machine learning techniques overcome these challenges by employing computational algorithms and statistical modeling. They analyze historical data, adapt to real-time information, and detect hidden relationships among factors to accurately predict safe routes. Machine learning models incorporate diverse data sources like crime records, accident reports, environmental factors, and social indicators to identify safety patterns. By capturing complex interactions and learning from past incidents, these models make informed predictions about route safety. Furthermore, machine learning algorithms continuously improve through feedback and the incorporation of new data, adapting to evolving safety patterns. Machine learning plays a crucial role in safe route planning by analyzing large volumes of data, extracting relevant features, and accurately predicting route safety. It provides an intelligent and data-driven approach to enhance safety while navigating through cities. Various computational algorithms, including decision trees, random forests, support vector machines, artificial neural networks, ensemble methods, and reinforcement learning, contribute to the analysis of data, extraction of patterns, and accurate predictions for safe route planning. The objective of the survey is to comprehensively explore predictive models for safe route planning using machine learning techniques.

Keywords: Spatial-temporal variability in safe route prediction, machine learning techniques for route safety, data-driven approaches for route safety, crime-prone area detection in route planning

[This article belongs to Journal of Mobile Computing, Communications & Mobile Networks(jomccmn)]

How to cite this article: T. Thilagavathi, A. Subashini. Survey of Predictive Models for Safe Route Predicting Using Machine Learning Techniques. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(01):13-22.
How to cite this URL: T. Thilagavathi, A. Subashini. Survey of Predictive Models for Safe Route Predicting Using Machine Learning Techniques. Journal of Mobile Computing, Communications & Mobile Networks. 2024; 11(01):13-22. Available from:


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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 4, 2024
Accepted February 16, 2024
Published April 3, 2024