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Title |
Optimizing Clustering and Predictive Modelling for 3-D Road Network Analysis Using Explainable AI |
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Author |
Rotsnarani Sethy, Soumya Ranjan Mahanta, Mrutyunjaya Panda |
| Citation |
Vol. 24 No. 9 pp. 30-40 |
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Abstract |
Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO2 emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. |
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Keywords |
K-Means Clustering, Support Vector Machines (SVM), SHAP Analysis, interpretability, Linear Regression |
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