Yue Wang

We present methods to predict and validate home and work places of users using their mobile network data. Home and work place distribution of a city helps in making urban development decisions. In the literature many methods are presented to predict home and work places using GPS data. Unlike GPS data mobile network data do not provide exact locations of a phone event. This makes accurate prediction of home and work places more difficult for mobile network data. We use a novel criterion that… Show more

We present methods to predict and validate home and work places of users using their mobile network data. Home and work place distribution of a city helps in making urban development decisions. In the literature many methods are presented to predict home and work places using GPS data. Unlike GPS data mobile network data do not provide exact locations of a phone event. This makes accurate prediction of home and work places more difficult for mobile network data. We use a novel criterion that combines an extracted feature from mobile data (i.e., inactivity – no phone event for a given period of time) with a 3rd party data about location category to predict the home location. Results show that the new criterion gives better prediction accuracy than inactivity alone. We predict work place using the idea that one goes to her work place on most of the weekdays but rarely on weekends. Validation of home and work place prediction is not straight forward. We validate our methods using correlation with 3rd party data. Multiple correlations between different statistics are performed to ensure reliability. Validation results show that our proposed methods are about 25% more accurate than existing methods both for the home and work place prediction. Show less

One major problem of using location data collected from mobile cellular networks for mobility modelling is the oscillation phenomenon. An oscillation occurs when a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. For the purpose of mobility modeling, the location data needs to be cleansed to approximate the mobile device’s actual location. However, this constitutes a challenge because the mobile device’s true location is not known.
In… Show more

One major problem of using location data collected from mobile cellular networks for mobility modelling is the oscillation phenomenon. An oscillation occurs when a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. For the purpose of mobility modeling, the location data needs to be cleansed to approximate the mobile device’s actual location. However, this constitutes a challenge because the mobile device’s true location is not known.
In this paper, we study the oscillation resolution problem. We propose an algorithm framework called DECRE (Detect, Expand, Check, REmove) to detect and remove oscillation logs. To make informed decisions DECRE includes four steps: Detect, to identify log sequences that may contain oscillation using a few heuristics based on the concepts of stable period and moving at impossible speed; Expand, to look before and after suspicious records to gain more information; Check, to check whether a cell tower is observed repeatedly (which is a strong indication of oscillation); and REmove, resolving oscillation by selecting a cell tower to approximate the mobile device’s actual location.
Our experimental results on travel diaries show that our oscillation resolution approach is able to remove records that are far from mobile device’s ground-truth locations, improve the quality of the location data, and performs better than an existing method. Our performance study on large scale cell tower data shows that the MapReduce implementation of our approach is able to process 1 Terabyte of cell tower data in a few hours using a small cluster. Show less