A Bike-sharing Optimization Framework Combining Dynamic Rebalancing and User Incentives
| Autor: | Michele Zorzi, Federico Chiariotti, Andrea Zanella, Chiara Pielli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Truck
050210 logistics & transportation Service quality Service (systems architecture) 021103 operations research Operations research Computer science 05 social sciences 0211 other engineering and technologies 02 engineering and technology Usage data Incentive rebalancing Bike sharing Control and Systems Engineering Smart city Component (UML) 0502 economics and business Computer Science (miscellaneous) Key (cryptography) gamification Software |
| Popis: | Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|