INTELLIGENT MODEL FOR FINDING THE OPTIMAL ROUTE TO COVER ALL STREETS WITH MOVEMENT CONSTRAINTS

Abstract

In modern cities, optimizing transport systems is essential for ensuring operational efficiency and sustainability. This research aims to develop an intelligent routing model for municipal services, focusing on snowplow deployment in the city of Astana. The model integrates Python libraries for data processing and visualization with a mapping API selected through comparative analysis. The city’s street network was reconstructed using topological and statistical features, such as road lengths and intersection density, which are essential for route optimization. The core routing task was formulated as a Chinese Postman Problem and solved using key algorithms including Dijkstra’s, Edmonds’, and Hierholzer’s. As a result, an optimized path was generated that efficiently covers all streets in the target area with minimal repetition. The solution significantly reduces unnecessary travel and supports more effective municipal service planning. Future work will focus on dynamic routing with real-time data and scaling the model for multiple vehicles and objectives in smart city contexts.