The emerging technologies related to mobile data especially CDR data has great potential for mobility and transportation applications. However, it presents some challenges due to its spatio-temporal characteristics and sparseness. Therefore, in this article, we introduced a new model to refine the positioning accuracy of mobile devices using only CDR data and coverage areas locations. The adopted method has three steps: first, we discovered which model of movement (Move, Stay) is associated with the coverage areas where the mobile device was connected using a Kalman filter. Then, simultaneously we estimated the location or the position of the device. Finally, we applied map-matching to bring the positioning to the right road segment. The results are very encouraging; nevertheless, there is some enhancement that can be done at the level of movement models and map matching. For example by introducing more sophisticated movement model based on data-driven modeling and a map matching that uses the movement model type detected by matching "Stay" location to buildings and "Move" model to roads.
 As technology to connect people across the world is advancing, there should be corresponding advancement in taking advantage of data that is generated out of such connection. To that end, next place prediction is an important problem for mobility data. In this paper we propose several models using dynamic Bayesian network (DBN). Idea behind development of these models come from typical daily mobility patterns a user have. Three features (location, day of the week (DoW), and time of the day (ToD)) and their combinations are used to develop these models. Knowing that not all models work well for all situations, we developed three combined models using least entropy, highest probability and ensemble. Extensive performance study is conducted to compare these models over two different mobility data sets: a CDR data and Nokia mobile data which is based on GPS. Results show that least entropy and highest probability DBNs perform the best. Continuous personal position information has been attracting attention in a variety of service and research areas. In recent years, many studies have applied the telecommunication histories of mobile phones (CDRs: call detail records) to position acquisition. Although large-scale and long-term data are accumulated from CDRs through everyday use of mobile phones, the spatial resolution of CDRs is lower than that of existing positioning technologies. Therefore, interpolating spatiotemporal positions of such sparse CDRs in accordance with human behavior models will facilitate services and researches. In this paper, we propose a new method to compensate for CDR drawbacks in tracking positions. We generate as many candidate routes as possible in the spatiotemporal domain using trip patterns interpolated using road and railway networks and select the most likely route from them. Trip patterns are feasible combinations between stay places that are detected from individual location histories in CDRs. The most likely route could be estimated through comparing candidate routes to observed CDRs during a target day. We also show the assessment of our method using CDRs and GPS logs obtained in the experimental survey. Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.