| Titre : | Improving ride-hailing order allocation via a ranked-first policy : Case Yassir |
| Auteurs : | Chakib Ighil, Auteur ; Hakim Fourar Laidi, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2025 |
| Format : | 1 fichier PDF (8 Mo) / ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel. Data Science-Intelligence Artificielle : Alger, École Nationale Polytechnique : 2025 Bibliogr. p. 143 - 150 . - Annexe p. 151 - 157 |
| Langues : | Anglais |
| Index. décimale : | PI02725 |
| Tags : | Ride-hailing Order dispatching Driver behavior Driver acceptance prediction Ranked-first policy Matching efficiency |
| Résumé : |
Ride-hailing platforms typically employ the nearest-first matching policy that prioritizes proximity while disregarding driver acceptance behavior, leading to inefficient assignments. This work proposes and evaluates a Ranked-First Policy that integrates acceptance prediction into the dispatching process, using Yassir, Algeria’s leading ride-hailing platform, as a case study.
An empirical analysis of 312,216 dispatch records from Oran, Algeria, revealed systematic patterns in driver acceptance behavior influenced by economic, temporal, spatial, and experiential factors. A comprehensive feature set was engineered to capture these behavioral signals, and an XGBoost model achieved an AUC of 0.785 with 79.2% Hit@1 accuracy, correctly identifying the accepting driver as the top-ranked candidate in most cases. A counterfactual simulation against Yassir’s current ETA-based policy demonstrated substantial operational improvements: first-offer success rate nearly doubled from 43.46% to 79.2%, and average time-to assignment decreased by 72%, from 21.18 to 5.79 seconds. These result confirm that acceptance-aware matching significantly enhances efficiency by reducing rider wait times and optimizing driver allocation. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| PI02725 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Data sciences_Intelligence artificielle | Téléchargeable |
