Name
Title | ||
|---|---|---|
Incorporating LSTM Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem |
Abstract | ||
|---|---|---|
AbstractThe smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help parking officers monitor parking violations. The traveling officer problem was customized to formulate a path-finding problem that aims to maximize the probability of catching overstayed cars before they leave. One of the challenges is to extract effective features from the big spatio-temporal data and provide a data-driven solution to replace conventional solutions such as a simple rule-based system or single optimization methods. In this article, we propose a seamless end-to-end learning and optimization framework that combines the long short-term memory auto-encoder neural network, clustering, and path-finding methods to solve the traveling officer problem. Our extensive comparison experiments on a large-scale real-world dataset have shown that our proposed solution outperforms any other single-step or optimization methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3380966 | ACM Transactions on Spatial Algorithms and Systems |
Keywords | DocType | Volume |
Optimization, cluster analysis, parking system, spatio-temporal data | Journal | 6 |
Issue | ISSN | Citations |
3 | 2374-0353 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Shao | 1 | 39 | 11.16 |
| Siyu Tan | 2 | 7 | 1.12 |
| Sichen Zhao | 3 | 3 | 0.72 |
| Kyle Kai Qin | 4 | 3 | 0.72 |
| Xinhong Hei | 5 | 30 | 8.63 |
| Jeffrey Chan | 6 | 1 | 2.05 |
| f salim | 7 | 40 | 10.93 |