| Title |
Ai Driven Adaptive Scheduling For On-Demand Transportation In Smart Cities |
| ID_Doc |
6928 |
| Authors |
Markovska V.; Ruseva M.; Kabaivanov S. |
| Year |
2023 |
| Published |
Lecture Notes in Intelligent Transportation and Infrastructure, Part F1378 |
| DOI |
http://dx.doi.org/10.1007/978-3-031-23721-8_31 |
| Abstract |
Artificial intelligence algorithms can be used to automate and improve various processes in public transportation. Using a combination of data sources like positioning devices, ticketing and sales notifications and video surveillance we can obtain details on the load and utilization of transportation network segments. These results can be used not only to improve marketing campaigns and increase quality of transportation services, but also to provide better on-demand transportation options with flexible schedules. In this paper we discuss one such automation system that benefits from delay analysis and real-time processing of video streams. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords |
Adaptive transportation schedules; AI in transportation; Monte Carlo simulation; Real-time video processing |
