A Sequential Clustering Method for the Taxi-Dispatching Problem Considering Traffic Dynamics

Negin Alisoltani (1) , Mahdi Zargayouna (1) , Ludovic Leclercq (2)

1 COSYS-GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée
2 LICIT UMR TE - Laboratoire d'Ingénierie Circulation Transport

Taxis are an important transportation mode in many cities due to their convenience and accessibility. In the taxi-dispatching problem, sometimes it is more beneficial for the supplier if taxis cruise in the network after serving the first request to pick up the next passenger, while sometimes it is better that they wait in stations for new trip requests. In this article, we propose a rolling horizon scheme that dynamically optimizes taxi dispatching considering the actual traffic conditions. To optimize passenger satisfaction, we define a limitation for passenger waiting time. To be able to apply the method to large-scale networks, we introduce a clustering-based technique that can significantly improve the computation time without harming the solution quality. Finally, we test our method on a real test case considering taxi requests with personal car trips to reproduce actual network loading and unloading congestion during peak hours.

Mots clés: TAXI-DISPATCHING; TRAFFIC CONGESTION; DEMAND CLUSTERING; SEQUENTIAL FUNCTION

Mots clés: TRAFIC ROUTIER; CONGESTION DU TRAFIC; TAXI; HEURISTIQUE; ALGORITHME

Domaines: Modélisation et simulation

Negin Alisoltani, Mahdi Zargayouna, Ludovic Leclercq. A Sequential Clustering Method for the Taxi-Dispatching Problem Considering Traffic Dynamics. IEEE Intelligent Transportation Systems Magazine, 2020, 13p. ⟨10.1109/MITS.2020.3014444⟩. ⟨hal-02947181⟩