Identifying Spatial and Temporal Patterns of Urban Activities Using Mobile Phone Data

Document Type : Original Article


1 Ph.D. Candidate/ School of Civil Engineering, Iran University of Science and Technology

2 Iran University of Science and Technology

3 Assistant Professor , Department of Geoscience Engineering, Arak University of Technology


Recently, the use of big data from mobile devices has received considerable attention in transportation studies. The need to do activities is the main inducement for urban trip generation. In addition, urban activities and their patterns vary over space and time. Mobile phone data, as a kind of continuous spatiotemporal data, records the location of people at different times. Therefore, such data is appropriate for urban activity level estimation, and its pattern detection. In the present study, mobile phone data was applied to estimate the density of activities (standardized by area) in Shiraz metropolitan area. To examine the spatial dependency of the variable of interest (density of activities), global and local Moran’s I indices were applied on density of activities aggregated over 321 traffic analyses zone in Shiraz on workdays, semi-workdays, and weekends. The results not only confirmed significant positive spatial autocorrelation of density of activities (P_Value<0.001), but also detected the hotspots in the central parts of study areas. Using exploratory analysis of time series and time-series heterogeneity tests, the study identified the trend of activity level, intensity change by time, and change-point of activity in time series. The study also extracted the start time of activities (8 a.m. for workdays and semi-workdays and 9 a.m. for weekends), mid-day peak (12-14), evening peak of trips (20-22), and the minimum activity time (3-6 a.m.). Results of these analyses could be beneficial for appropriate transportation planning, policy-making, demand management, management of population density at hotspots at any time of the day, as well as urban transportation environmental impacts analysis.