Integrating DBSCAN clustering and Dijkstra's algorithm for enhanced route planning: a case study of Oran, Algeria.
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| Title: | Integrating DBSCAN clustering and Dijkstra's algorithm for enhanced route planning: a case study of Oran, Algeria. |
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| Authors: | Reguieg, Seddik1 (AUTHOR) reguieg.seddik@univ-oran2.dz, Omari, Youcef2 (AUTHOR), Dahane, Miloud3 (AUTHOR), Collart-Dutilleul, Simon4 (AUTHOR), Mami, Mohammed Amine5 (AUTHOR) |
| Source: | RAIRO: Operations Research (2804-7303). 2026, Vol. 60 Issue 4, p2043-2064. 22p. |
| Subjects: | Clustering algorithms, Graph algorithms, Route choice, Transportation engineering, Geospatial data, Transportation planning |
| Geographic Terms: | Algeria, Oran (Algeria) |
| Abstract: | This paper presents a novel approach to optimizing urban transport in Oran, Algeria, by addressing the issue of fragmented connectivity through the integration of clustering techniques with Dijkstra's algorithm. The study begins with the collection and management of geo-referenced data for urban transport lines and bus stops. Using a customized DBSCAN clustering method, bus stops are grouped based on geographic and traffic data, simplifying the network for more efficient analysis. Dijkstra's algorithm is then applied to calculate the shortest paths within the clustered network, optimizing routes and reducing travel times, which improves overall system efficiency. Additionally, a web-based application has been developed for Oran's residents, with initial testing indicating promising usability for practical implementation. This integrated approach provides significant advancements in urban transport planning, offering valuable tools and insights for transport practitioners and decision-makers. [ABSTRACT FROM AUTHOR] |
| Copyright of RAIRO: Operations Research (2804-7303) is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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