Abstract
Abstract. The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.
Highlights
The spatial separation of demand and supply in the city generates continuous human movements, which have raised many urban issues, such as traffic congestion, energy consumption, air pollution, and infectious disease (Kwan and Schwanen, 2016)
Population survey and travel survey investigating a certain percentage of people are two traditional ways to gain typical human movement features in the city, such as travel volume, travel distance, and travel time
This study investigates the spatial-temporal variation of urban human movements with spatial-temporal data
Summary
The spatial separation of demand and supply in the city generates continuous human movements, which have raised many urban issues, such as traffic congestion, energy consumption, air pollution, and infectious disease (Kwan and Schwanen, 2016). The integration of geographic information system (GIS), Internet, information and communication technology (ICT) generates more and more human related data, i.e., mobile phone data (Sevtsuk and Ratti, 2010; Becker et al, 2013; Cao et al, 2015), vehicle GPS data (Tu et al, 2010; Luo et al, 2015), smart card data (Kim et al, 2014; Tu et al, 2016) Such useful data have both spatial location (longitude and latitude ) and time stamp, which give us new insights on human movements in the city (Yue et al, 2014; Pan et al, 2013; Li and Li, 2014; Li et al, 2014). Combining with spatial data processing technology, they contribute to much innovative researches of urban planning (Liu et al, 2015), urban transportation (Tu et al, 2010; Wang et al 2012), disaster response (Miyazaki et al, 2015), location based service (Fang et al, 2011; Li et al, 2015), and so on
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