Work

A Sensor-Based Simulation Method for Spatiotemporal Event Detection

Year: 2024

Type: article

Abstract: Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi13050141

Authors: Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson, Binghu Huang

Institutions: Texas State University, University of South Carolina, The University of Texas at Austin, Pennsylvania State University, China University of Petroleum, East China

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,932