Towards crowd-aware indoor path planning

Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou

Research output: Contribution to journalConference articleResearchpeer-review

Abstract

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people’s routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

Original languageEnglish
JournalProceedings of the VLDB Endowment
Volume14
Issue number8
Pages (from-to)1365-1377
Number of pages13
ISSN2150-8097
DOIs
Publication statusPublished - 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: 16 Aug 202120 Aug 2021

Conference

Conference47th International Conference on Very Large Data Bases, VLDB 2021
CityVirtual, Online
Period16/08/202120/08/2021

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