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T. Thilagavathi, A. Subashini
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- Research Scholar, Assistant Professor, Department of Computer Application, Annamalai University, Annamalai Nagar, Department of Computer Application, Government Arts College, Chidambaram, Tamil Nadu, Tamil Nadu, India, India
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Abstract
nSafe 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 paper is to comprehensively explore predictive models for safe route planning using machine learning techniques.
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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.
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References
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Journal of Mobile Computing, Communications & Mobile Networks
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 4, 2024 | |
| Accepted | February 16, 2024 | |
| Published | April 3, 2024 |
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