| Titre : |
Improving ride-hailing order allocation via a ranked-first policy : Case Yassir |
| Type de document : |
document électronique |
| Auteurs : |
Chakib Ighil, Auteur ; Hakim Fourar Laidi, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (8 Mo) |
| Présentation : |
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 (eng) |
| Mots-clés : |
Ride-hailing
Order dispatching
Driver behavior
Driver acceptance prediction
Ranked-first policy
Matching efficiency |
| Index. décimale : |
PI02725 |
| 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. |
Improving ride-hailing order allocation via a ranked-first policy : Case Yassir [document électronique] / Chakib Ighil, Auteur ; Hakim Fourar Laidi, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (8 Mo) : ill. 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 ( eng)
| Mots-clés : |
Ride-hailing
Order dispatching
Driver behavior
Driver acceptance prediction
Ranked-first policy
Matching efficiency |
| Index. décimale : |
PI02725 |
| 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. |
|
PI00100 
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