Active Balancing of Bike Sharing Systems: Lecture Notes in Mobility
Autor Jan Brinkmannen Limba Engleză Hardback – 6 feb 2020
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Paperback (1) | 623.26 lei 43-57 zile | |
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Springer International Publishing – 6 feb 2020 | 629.19 lei 43-57 zile |
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Specificații
ISBN-13: 9783030350116
ISBN-10: 3030350118
Ilustrații: XXI, 184 p. 48 illus., 1 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.47 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Mobility
Locul publicării:Cham, Switzerland
ISBN-10: 3030350118
Ilustrații: XXI, 184 p. 48 illus., 1 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.47 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Mobility
Locul publicării:Cham, Switzerland
Cuprins
Introduction.- Bike Sharing Systems.- Optimization Problems.- Dynamic Decision Making.- The Stochastic-Dynamic Multi-Vehicle Inventory Routing Problem for Bike Sharing Systems.- Lookahead Policies.- Dynamic Lookahead Horizons.- Case Studies.- Managerial Implications.- Future Research.
Textul de pe ultima copertă
This book reports on an operational management approach to improving bike-sharing systems by compensating for fluctuating demand patterns. The aim is to redistribute bikes within the system, allowing it to be “actively” balanced. The book describes a mathematical model, as well as data-driven and simulation-based approaches. Further, it shows how these elements can be combined in a decision-making support system for service providers. In closing, the book uses real-world data to evaluate the method developed and demonstrates that it can successfully anticipate changes in demand, thus supporting efficient scheduling of transport vehicles to manually relocate bikes between stations.
Caracteristici
Reports on novel control approaches to deal with the imbalance problem in bike sharing systems Combines mathematical models with data-driven approaches Describes in detail the evaluation of the methods with real-world data