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Smart Freight Estimation Using Artificial Intelligence for Sustainable City Logistics
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Author(s): Aye Thiri Nyunt (Marwadi University, India), Brij Kotak (Marwadi University, India), Ravi Chauhan (Marwadi University, India), Rituraj Jain (Marwadi University, India)and Vedant Kesariya (Marwadi University, India)
Copyright: 2026
Pages: 34
Source title:
Sustainable Solutions for Transportation and Logistics
Source Author(s)/Editor(s): Olcay Polat (Pamukkale University, Turkey)and Görkem Gülhan (Pamukkale University, Turkey)
DOI: 10.4018/979-8-3373-4611-3.ch007
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Abstract
In this chapter, a novel machine learning based framework, RouteEdge for predicting accurate shipment cost in logistics is presented. The model improves on the previous work, by integrating spatial fuel price data, and using shipment parameters like distance, delivery time and region specific costs to take into account non linear dependencies in real world logistics scenarios, and captures it using Random Forest, Gradient Boosting, and XGBoost. KDTree is used for spatial diesel pricing, the system also provides interactive Streamlit based interface of user inputs and shows predicted prognosis in real time. Finally, we have tested Gradient Boosting through robust analysis and it turned out to be the most accurate model with an R2 of 97.70%. The RouteEdge product gives you data driven insights that help you plan out logistics and optimize the associated cost in a more scalable way.
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