Work

Searching rooms with top-k passenger flows using indoor trajectories

Year: 2024

Type: article

Abstract: In a wide variety of applications, such as indoor position selection for advertising and setting rents of different shops in a shopping mall, it is better to get the passenger flow of each room. In the indoor space, the positions of users are commonly captured by the indoor positioning system consisting of static positioning devices. And the sequence of all tracking events with the same user ordered by the corresponding time is the indoor trajectory of this user. Thus, in this paper, we define and study two essential queries named Rooms with top- k passenger flows at a Timestamp query (R k T for short) and Rooms with top- k passenger flows within a time Interval query (R k I for short), i.e., how to search rooms with top- k passenger flows at a timestamp and within a time interval in the past using indoor trajectories, respectively. For the indoor positioning system, there are only limited static positioning devices deployed in the indoor space on account of the cost. And the detection ranges of these static positioning devices only cover a small part of the indoor space. When a user is in the undetected state, there is uncertainty in its position combined with the quite complex indoor topology. Such uncertainty brings great challenges to determining the passenger flow in each room. Considering the distribution of static positioning devices, we propose a new method about how to reasonably infer where a user is in the undetected state and the corresponding probability based on its indoor trajectory and the complex indoor topology. In order to quickly retrieve the set of indoor trajectories, we propose a full Binary tree indexing indoor trajectories divided by Time intervals (BiT for short), which is built on the given set of indoor trajectories. Based on the index BiT, we propose PAT Algorithm and PAI Algorithm to efficiently process R k T and R k I queries, respectively. Extensive experiment results demonstrate superior performance of PAT Algorithm and PAI Algorithm.

Source: Discover Computing

DOI: 10.1007/s10791-024-09457-2

Authors: Hongbo Yin, Donghua Yang, Kaiqi Zhang, Hong Gao, Jianzhong Li

Institutions: Harbin Institute of Technology, Zhejiang Normal University, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology

Language: English

Topic: Data Management and Algorithms

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,520