Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

Abstract

The urban open public spaces are the areas where people tend to gather together, which may lead to great crowd-gathering risk. This paper proposes a new method to assess the rank and spatial distribution of crowd-gathering risk in open public spaces in a large urban area. Firstly, a crowd density estimation method based on Tencent user density (TUD) data is built for different times in open public spaces. Then, a reasonable crowd density threshold is delimited to detect critical crowd situations in open public spaces and find out the key open public spaces that need to have intensive crowd-gathering prevention. For estimating the crowd-gathering risk in key open public spaces, the quantified risk assessment approach is conducted based on the classical risk theory that simultaneously considers the probability of an accident occurring, the severity of the accident consequence, and the risk aversion factor. A case study of the area within the Outer-ring Road of Shanghai was conducted to determine the feasibility of the new method. The thematic maps that describe the ranks and spatial distribution of crowd-gathering risk were generated. According to the risk maps, the government can determine the crowd control measures in different areas to reduce the crowd-gathering risk and prevent dangerous events.