1. A Sequential Clustering Method for the Taxi-Dispatching Problem Considering Traffic Dynamics
- Author
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Mahdi Zargayouna, Ludovic Leclercq, Negin Alisoltani, Génie des Réseaux de Transport Terrestres et Informatique Avancée (COSYS-GRETTIA ), Université Gustave Eiffel, Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, and EC/H2020/646592/EU/A Multiscale and Multimodal Modelling Approach for Green Urban Traffic Management/MAGnUM_ERC
- Subjects
Scheme (programming language) ,Operations research ,Computer science ,media_common.quotation_subject ,Cruise ,Taxis ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,02 engineering and technology ,ALGORITHME ,11. Sustainability ,0502 economics and business ,TRAFIC ROUTIER ,0202 electrical engineering, electronic engineering, information engineering ,Quality (business) ,Cluster analysis ,TAXI ,media_common ,computer.programming_language ,050210 logistics & transportation ,business.industry ,Mechanical Engineering ,05 social sciences ,HEURISTIQUE ,Mode (statistics) ,DEMAND CLUSTERING ,SEQUENTIAL FUNCTION ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,CONGESTION DU TRAFIC ,Computer Science Applications ,Public transport ,TRAFFIC CONGESTION ,Automotive Engineering ,TAXI-DISPATCHING ,TRIPS architecture ,020201 artificial intelligence & image processing ,business ,computer - Abstract
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.
- Published
- 2020