Estimation of pedestrian density and speed on street network using smartphone spatio-temporal data

Today, movement data are easily collected from a mobile phone and used as a useful data for providing services. However, there are not many space syntax studies using such data. In this study, we aim to quantitatively describe the relation between space syntax indices, potentials from a railway station, land use and street configuration such as width or length of a street, and density and speed of pedestrians using multiple regression analysis and correlation analysis. Then, results of these analyses provide the model of how we walk or move in urban space and can be applied to urban planning, urban development or store opening planning. We used the point data of people flow collected from smartphone passing through Saitama City during a year. Concretely, the data set contains approximately 5,000 anonymous users and 500,000 points per day and each record in the dataset has information such as daily user ID, latitude, longitude, time-stamp, locational accuracy, speed and direction of movement. In this research, we extracted the pedestrian data which were positioned in Omiya, with the accuracy of less than 30 meters’ error, obtained from iOS smartphones. The point data were aggregated at segments between intersections or closer points separately using GIS, and the number of associated points, the average of movement speed and standard deviation of it were given to it. We used the road centerline map around Omiya station and converted curved polylines into simple straight lines. Then, we conducted space syntax analysis using depthmapX and gathered the result of the analysis, other street information, and the aggregated mobile phone data. Also, we calculated the potentials from railway station using gravity model or entropy model. Finally, we analyzed walking characteristics – pedestrian density and walking speed - and spatial configuration – indices of space syntax, potentials, land use and size of a road segment - by multiple regression analysis and correlation analysis and found the relationships between them. Density and speed of movements around Omiya station were modeled and estimated by the space syntax indices, potentials, land use and street configuration.