Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Abstract

Obtaining the distribution of home and work locations is essential for city planning, as it defines the structure and mobility pattern of a city. With the development of telecommunication networks, mobile network data, having the advantages of large coverage and strong followability, have produced large amounts of information about human activities. Thus, it has become a popular research subject for human position detection. In this study, we proposed a new method to detect home and work locations based on the extraction of focal points in traces, identifying an individual’s working and resting hours, and analyzing the characteristics of city grids using mobile phone cellular signaling data (CSD). At the individual level, we validated the algorithm on ground-truth volunteer data and achieved a small deviation of under 500 and 565 m for home and work location detection 85% of the time. At the aggregate level, we tested it on a city-wide anonymized CSD set and found a high Pearson correlation between our result and the census data of 0.93. Compared to existing studies, this study improved the granularity and location accuracy of home and work location detection, as well as validated the method using both individually labeled ground-truth data and aggregate data for the first time. Applying the algorithm in a city, we captured the population distribution, commuting patterns, and job-housing balance of the city and demonstrated the potential in using mobile network data for urban planning and policy formulation.

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

“…Over the past decade, there has been an increase in the usage of mobile crowd sensing (MCS) for urban planning in Smart Cities [7]. Yang et al analyzed mobile phone traces to identify particular points of interest (POIs), including home and work [2]. The authors demonstrate that mobile trajectories may accurately identify user interests at a fine-grained level.…”

“…Then, using various machine learning (ML) techniques, individual trajectories can be pooled and analyzed to estimate PoIs related to a population's social patterns. The accuracy and viability of using ubiquitous technology as the foundation for creating extensive sensing frameworks that can support Smart City planning have been the subject of related research [2,3]. These frameworks frequently use planned PoIs by depending on offline data analysis, but not a persistent identification of points of interest (PoIs) throughout time, such as those connected to seasonal events or evolving group interests.…”

Activity location recognition from mobile phone data using improved HAC and Bi‐LSTM

“…Several far-away outliers may be clustered together, causing the resulting cluster to stray away [28]. Some other clustering algorithms based on trace density were used, such as DBSCAN [33][34][35] and ST-CFSFDP (space-time clustering by fast search and find of density peaks) [36]. However, these methods are difficult to control the spatial range of traces at activity locations.…”

A Travel Mode Identification Framework Based on Cellular Signaling Data

“…As an ancillary product used by mobile operators for communication billing [11], CSD has natural advantages such as large sample size, wide coverage of the population and long collecting duration [12], which can make up for the limitation of GPS data mentioned above. In recent years, CSD has been adopted in many traffic research, such as population density analysis [13], Home and workplace identification [14][15][16], mobility pattern analysis [17][18][19], commuting analysis [20], and can be used widely in mobile applications [21]. However, CSD has two major defects [9]: low positioning accuracy and unstable collection frequency, which lead to a big difference in data quality between CSD and GPS.…”