Extraction of mobility patterns of people in a city helps in urban planning, traffic control, transportation management, etc. We present a demo that shows how to extract interesting individual and collective mobility patterns from mobile phone cellular data. Individual patterns are captured using a next place prediction algorithm that builds different Dynamic Bayesian Network (DBN) models and chooses the best DBN model. Collective patterns are captured by aggregating individual patterns using efficient query processing methods. Each query requires three arguments, i.e., current location cl, current time ct and query time qt. These parameters are varied to capture information at different granularity level resulting in many interesting patterns. Cellular data, although less accurate, irregular and sparser than GPS and less detailed than CDR, is cheap and abundantly available and is used by all sections of people. Demo results show that these shortcomings can be overcome, at least to some extent, by exploiting the underlying repetitive patterns. As part of individual patterns, the system predicts the next place (along with a confidence measure) for a given combination of current location, Time-of-Day and Day-of-week of a user. Collective mobility patterns can start from a planning area or a latlon (with a radius). It visually shows the major destinations along with their intensity. An interesting finding was that planning areas with higher population has higher predictability. The UI system also shows heat maps that show continuous mobility of people. These types of patterns can have significant bearing on urban planning and related applications.

CDR (Call Detail Records) data is more easily available than other network related data (such as GPS data) as most telecommunications service providers (TSPs) maintain such data. By analyzing it one can find mobility patterns of most of the population thus leading to efficient urban planning, disease and traffic control, etc. But its granularity is low as the latitude and longitude (lat-lon) of a cell tower is used as the current location of all mobile phones that are connected to the cell tower at that time. Granularity can range between 10s of metres to several kms depending on population density of a locality. This is one reason why, although there are many existing systems on visualizing mobility of people based on GPS data, there is hardly any existing system for CDR. We develop a Mobility Visualization System (MoVis) for visualizing mobility of people from their CDR records. First of all, given the CDR records of a user, we determine her stay regions (places where she stays for a significant amount of time). Trajectories of phone events (and lat-lon of cell towers) between stay regions are extracted as her trips. Start and end times of a trip are estimated using linear extrapolation. Based on the start and end times, temporal patterns are extracted. Trips with sufficient number of intermediate points are mapped to transport network that consists of train lines, bus routes and expressways. We use Kernel density estimation to visualize the most common path for a given origin and destination. Based on this we create a round-the-clock visualization of mobility of people over the entire city separately for weekdays and weekends. At the end we show the validation results.

Understanding how people use public transport is important for the operation and future planning of the underlying transport networks. We have therefore developed and deployed a traffic measurement system for a key player in the transportation industry to gain insights into crowd behavior for planning purposes. The system has been in operation for several months and reports, at hourly intervals, (1) the crowdedness of subway stations, (2) the flows of people inside interchange stations, and (3) the expected travel time for each possible route in the subway network of Singapore. The core of our system is an efficient algorithm which detects individual subway trips from anonymized real-time data generated by the location based system of Singtel, the country's largest telecommunications company. To assess the accuracy of our system, we engaged an independent market research company to conduct a field study--a manual count of the number of passengers boarding and disembarking at a selected station on three separate days. A strong correlation between the calculations of our algorithm and the manual counts was found. One of our key findings is that travelers do not always choose the route with the shortest travel time in the subway network of Singapore. We have therefore also been developing a mobile app which allows users to plan their trips based on the average travel time between stations.

In this paper we present methods for mapping trajectories of cell tower latitude-longitudes in mobile phone data to transport network. Compared to other forms of mobile phone data such as GPS traces, this form is the most popular and inexpensive but the least accurate and highly irregular. Unlike some earlier works on this type of data where only long trips are studied, our work is based on everyday transportation inside a city. The problems due to inaccuracy in determining position of a user and irregularity of phone events are felt even more intensely for such small distances. Our approach in processing this data takes into account its repetitive nature, i.e., A typical user makes the same trips every week. Moreover, many users follow the same transport route to go from one place to another. First of all, we defined stay region taking into consideration irregularity of phone events. Then we extracted trajectories between each pair of stay regions as trips. Then, we map these trips to existing transportation network consisting of bus services, train lines, expressways, and their combinations such as bus-train-bus. For a given origin and destination, we create a heat map that shows the most commonly used route. A round-the-clock heat map, both for weekdays and weekends, shows people's mobility at different times of a week. We validate our findings by both direct (sample survey) and indirect (comparison with available statistics) methods.

Knowledge about population distribution of planning areas helps in making urban development decisions. Two important criteria are: "where do people live?" and "where do they work?" In this paper we propose methods to find home and workplaces from mobile network data. Home and work places are essential for discovery of mobility profiles of users. Validation of home and workplace prediction is not straight forward. We validate our methods using correlation with external data. Validation results show that even though a single cellular provider has only a portion of the entire population as its users, distribution of home and work places predicted using its mobile network data match that of government statistics. On the basis of this matching, we can have faith in distributions of more difficult statistics extracted from mobile network data which are difficult to obtain from external sources. We implemented an interactive system to show various distributions such as people living and working in different planning areas, and people working in different job sectors such as manufacturing. Interesting relationships are found by calculating joint distributions, e.g., Where do people, living in a planning area, work, and vice versa. Planning areas are ranked by the average distance traveled from home to work. Another interesting fact we extract is balance. Balance of a planning area is high if people live and work there, it is low if people living in a planning area work in other planning areas. We extend these statistics to regions which consist of many planning areas. The goal of this interactive system is to understand location semantics and mobility of users to aid in making urban development decisions. A video recording with subtitles is uploaded in http://www.youtube.com/watch?v=mo-7-DsCymw.

We present methods to predict and validate home and work places of anonymized users using their mobile network data. Knowledge of home and work place of a user is essential in order to find his (and overall population) mobility profiles. There are many methods that predict home and work places using GPS data. But unlike GPS data, mobile network data using GSM do not provide the exact location of a phone event. 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 open source data about location category % (i.e., Streetdirectory.com) to predict 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. We validate our methods by comparing against the ground truth obtained from open source data. Validation results show that our proposed methods are about 25% more accurate than existing methods both for home and work place predictions.

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 five hours using a small cluster.