Prediction of Traffic Incident Locations with a Geohash-Based Model Using Machine Learning Algorithms

This paper presents a novel geohash-based approach for predicting traffic incident locations using machine learning algorithms. The study utilized a three-stage model for predicting the locations of traffic incidents, which encompassed accidents, breakdowns, and other incidents. In the model, firstl...

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Bibliographic Details
Published inApplied sciences Vol. 14; no. 2; p. 725
Main Authors Ulu, Mesut, Kilic, Erdal, Türkan, Yusuf Sait
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
Accidents
Algorithms
Analysis
Artificial intelligence
Breakdowns
Clustering
Data analysis
Data mining
Geographic information systems
Geography
geohash
Geospatial data
Machine learning
prediction
Software
Spatial analysis
Statistical analysis
traffic accident
Traffic accidents & safety
Traffic congestion
Traffic flow
traffic incident location
Vehicles
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Summary:This paper presents a novel geohash-based approach for predicting traffic incident locations using machine learning algorithms. The study utilized a three-stage model for predicting the locations of traffic incidents, which encompassed accidents, breakdowns, and other incidents. In the model, firstly, ArcGIS was used to convert the coordinates of traffic incidents into geohash areas, leading to the definition of incident locations. Secondly, variables affecting traffic incidents were extracted, and a dataset was created by utilizing the values of these variables in geohash fields. Finally, machine learning algorithms such as decision tree (DT), k-nearest neighbor (k-NN), random forest (RF), and support vector machine (SVM) algorithms were used to predict the geohash region of traffic incidents. After conducting hyperparameter optimization, we evaluated the efficacy of various machine learning algorithms in predicting the location of traffic incidents using different evaluation metrics. Our findings indicate that the RF, SVM, and DT models performed the best, with accuracy percentages of 91%, 88%, and 87%, respectively. The findings of the research revealed that traffic incident locations can be successfully predicted with the geohash-based forecasting model. The results offer traffic managers and emergency responders new perspectives on how to manage traffic incidents more effectively and improve drivers’ safety.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020725