MyWay: Location prediction via mobility profiling

Mobile applications are required to operate in highly dynamic pervasive computing environments of dynamic nature and predict the location of mobile users in order to act proactively. We focus on the location prediction and propose a new model/framework. Our model is used for the classification of the spatial trajectories through the adoption of Machine Learning (ML) techniques. Predicting location is treated as a classification problem through supervised learning. We perform the performance assessment of our model through synthetic and real-world data. We monitor the important metrics of prediction accuracy and training sample size.

IEEE Access

Location prediction plays an important role in modeling human mobility. Existing studies focused on developing a prediction model which is based solely on the past mobility of only the person of interest (POI), rather than including information on the mobility of her/his companions. In fact, people frequently move in a group, and thus, using mobility data of a person's companions can enhance accuracy when predicting that person's future locations. Motivated by this, we propose a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person's and his/her companions' mobility. The framework first determines the POI's companions, then predicts future locations based on mobility information for both the POI and selected companions. Two companion selection methods are proposed in this work. The first method uses spatial closeness (SC) to determine the companions of the POI by measuring the similarity of the individuals' geographic distributions. The second method builds person ID embedding (PIE) vectors, and cosine similarity is used to select the POI's companions. To mitigate the curse of dimensionality, the framework also uses a stacked autoencoder in which the encoder compresses a high-dimensional input feature (e.g., location, time, and person ID) into a low-dimensional latent vector. For the second phase of the framework, a bidirectional recurrent neural network (BRNN)-based multi-output model is proposed to predict a person's future locations in the next several time slots. To train the BRNN model, weighted loss is used, which takes into account the importance of each future time slot to predict the POI's locations accurately. Experiments are conducted on two largescale Wi-Fi trace datasets, demonstrating that the proposed model can effectively predict human future locations.

International Journal of Electrical and Computer Engineering (IJECE), 2018

For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and location based services are some of them fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.

The Journal of CIEES, 2022

Individual and group mobility is an essential information for numerous segments of technology (including transport and logistics), society, and economy. The ability of telecommunications devices, such as smartphones, to collect accurate and reliable data on personal mobility with the embedded sensors, inspires research in personal mobility. We confirm the ability of suitably defined indicators to compare sets of trajectories, and identify outliers/differences among the individual ones. Furthermore, we demonstrate development of a machine learning (ML) regression predictive model based on experimental data collected on the real urban environment of the city of Krapina, Croatia, suitable for utilisation in personal mobility analysis, and traffic and transport planning and optimisation.

Recently, location-based applications and services for mobile users have attracted significant attention. In this context, one challenging problem is predicting the future location of a mobile user given his or her current location and associated metadata. Solving this problem enables many interesting applications such as location-aware mobile advertisements, traffic warnings, etc. In this paper, we present an approach based on user-specific decision trees learned from each user's history. The classification tree is built ...

Over the past few years, predicting the future location of mobile ob-jects has become an important and challenging problem. With the widespread use of mobile devices, applications of location prediction include location-based ser-vices, resource allocation, smooth handoffs in cellular networks, animal migra-tion research, and weather forecasting. Most current techniques try to predict the next location of moving objects such as vehicles, people or animals, based on their movement history alone. However, ignoring the dynamic nature of mobile behavior and trying to repeatedly exploit the same common patterns, may yield wrong results, at least part of the time. Analyzing movement in its context and choosing the best movement pattern by the current situation, can reduce some of the errors and improve prediction accuracy. In this paper, we present a context-aware location prediction algorithm that utilizes various types of context infor-mation to predict future location of vehicles. We use five contextual features re-lated to either the object environment or its current movement data: current loca-tion; object velocity; day of the week; weather conditions; and traffic congestion in the area. Our algorithm incorporates these context features into the trajectory-clustering phase as well as in the location prediction phase. We evaluate our al-gorithm using two real-world GPS trajectory datasets. The experimental results demonstrate that the context-aware approach can significantly improve the accu-racy of location predictions.

Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, 2014

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.

Lecture Notes in Computer Science, 2017

We provide a novel algorithm for the discovery of mobility patterns and prediction of users' destination locations, both in terms of geographic coordinates and semantic meaning. We did not use any semantic data voluntarily provided by a user, and there was no sharing of data among the users. An advantage of our algorithm is that it allows a trade-off between prediction accuracy and information. Experimental validation was conducted on a GPS dataset collected in the Microsoft Research Asia GeoLife project by 168 users in a period of over five years.

International Journal of Wireless Communications and Network Technologies, 2019

Faster development in cellular networks provides faster and seamless services to mobile users. Mobility management is an important issue in the area of mobile communication. Within the cellular networks, movement track of mobile user is provided by the location management [1]. Location of mobile station has great attention and has potential for application and services to improve location-based services like prediction of the next location. Several researchers are worked to develop methods and algorithms which increase the positioning accuracy and prediction rate. One of the powerful features of modern Smartphone's is their ability to provide us with real-time, location based information such as weather updates, traffic, time to reach and so on..In this paper,a proposed system that can predict a user's next location using their location history, current location and the current time, has been developed.

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. Firstly, we propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. Secondly, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Thirdly, we propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Fourthly, we assume the existence of a pattern base and try to compare the movement pattern of a certain user with stored information in order to predict future locations. Our findings, compared with other schemes, are very promising for the location prediction problem and the adoption of proactive context-aware applications and services.