| Title |
Bicycle Sharing Systems meet AI: forecasting bicycles availability and decision-making |
| ID_Doc |
43318 |
| Authors |
Rozanec, JM; Krivec, T; Kersic, V; Cundric, L; Stojanovic, B; Zeman, M; Bratko, I |
| Title |
Bicycle Sharing Systems meet AI: forecasting bicycles availability and decision-making |
| Year |
2022 |
| Published |
|
| DOI |
|
| Abstract |
With the ubiquitous increase in the number of people in cities, there is a growing need for sustainable transport possibilities. Smart cities should provide environment-friendly ways to travel inside the city. One of the most nature-preserving ways to travel is using bicycles, which is often encouraged by public bicycle sharing systems, which are present in many cities around the globe. While these systems are usually easily accessible, they still lack optimization regarding bicycle availability across stations. This paper contributes towards the optimization of the bicycle sharing system in Ljubljana, Slovenia. We developed classification and regression machine learning models for predicting the emptiness and occupancy across bicycle stations in near future. These predictions allow for the caretakers of the system to intervene on time and provide enough bicycles across all stations. |
| Author Keywords |
Smart city; sustainable transport; bicycle sharing; optimization |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:001237764600040 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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