J. Panadero Martinez, J. Panadero Martinez, A. A. Juan, Y. Li, M. Peyman, F. Xhafa
Transport activities and citizen mobility represent a key sector in global economies, playing an important role in the social and economic development of smart and sustainable cities. New transport services, such as carsharing and ridesharing, are becoming increasingly popular in modern cities. However, all these innovative transport services require the provision of efficient real-time routing plans. We propose the use of agile optimization algorithms to quickly process big data to support real-time decision making. These algorithms allow to process real-time information gathered from IoT systems, re-optimizing a travel plan whenever new data on traffic conditions become available. To quantify the benefits of IoT, we present a numerical example for the waste collection problem in the city of Barcelona, which has been modeled as a Dynamic Team Oriented Problem with Mandatory Visits (DTOP-MV). The Open Data BCN repository has been used in this example to simulate the IoT information.
Palabras clave: Agile optimization, IoT analytics, Smart city, Transport analytics, Dynamic Team Orienteering Problem
Programado
GT09 Heurísticas II. Heurísticas y metaheurísticas
8 de junio de 2022 17:20
A04
