Mobile Crowd Location Prediction with Hybrid Features using Ensemble Learning

With the explosive growth of location-based service on mobile devices, predicting users’ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of users’ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the correlation between locations and features. Finally, we apply ensemble methods to predict users’ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict users’ trajectories between two locations. To evaluate the system performance, we use a real-life dataset from the Nokia Mobile Data Challenge. Experiment results unveil interesting findings: (1) For individual predictors, Bayes Networks outperform all others when data quality is good, while J48 delivers the best results when data quality is bad; (2) Ensemble predictors outperform individual predictors in general under all conditions; and (3) Ensemble predictor performance depends on the user movement patterns

Mobile Crowd Location Prediction with Hybrid Features using Ensemble Learning

With the explosive growth of location-based service on mobile devices, predicting users’ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of users’ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the correlation between locations and features. Finally, we apply ensemble methods to predict users’ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict users’ trajectories between two locations. To evaluate the system performance, we use a real-life dataset from the Nokia Mobile Data Challenge. Experiment results unveil interesting findings: (1) For individual predictors, Bayes Networks outperform all others when data quality is good, while J48 delivers the best results when data quality is bad; (2) Ensemble predictors outperform individual predictors in general under all conditions; and (3) Ensemble predictor performance depends on the user movement patterns

Mobile Users Location Prediction with Complex Behavior Understanding

The growing ubiquity of smart-phones equipped with built-in sensors and global positioning system (GPS) has resulted in the collection of large volumes of mobility data without the need of any additional devices. The large size of heterogeneous mobility data gives rise to rapid development of location-based services (LBSs). The predictability of mobile users’ behavior is essential to enhance LBSs. To predict human mobility, many techniques have been proposed. However, existing techniques require good data quality to guarantee optimal performance. In this paper, we proposed a hybrid Markov chain to predict mobile users’ future locations. Our model constantly adapts to available user trace quality to select either the first order or the second order Markov chain. Compared to existing solutions, our model is adaptive to discrete gaps in data trace. In addition, we implemented a proper mechanism to predict congestion in city areas. To help us understanding complex user behaviors, we have also proposed a technique benefiting both temporal and spatial parameters to extract Zone of Interests (ZOIs). To evaluate the algorithms performance, we use a real-life dataset from the Nokia Mobile Data Challenge (MDC) collected around Lake Geneva region from 180 users. Experiment results show that our approach could achieve a good location prediction accuracy as well as area congestion prediction for most of the users

Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain

The prevalence of smartphones equipped with global positioning system (GPS) has enabled researchers to excavate users mobility patterns in the cities. The knowledge of users’ behavior, such as their locations, plays significant role in location-based services (LBSs), resource management, logistic administration and urban planning. To understand complex behavior of humans we utilize spatio-temporal analysis on collected geo-location points to exploit Individual Zone of Interests (I-ZOIs) in urban areas. In addition, we designed a hybrid Markov chain model to forecast future locations of pedestrians. Compared to existing mobility prediction methodologies, our predictor can adapt it’s behavior constantly based on the quality of existing traced data to switch between first-order or second-order Markov chain. Besides, we propose a model to predict city area congestion. More specifically, the model predicts the number of users in a specific area of a city by discovering the regular mobility patterns of a group of users. We conducted comprehensive empirical experiments using a real-life dataset, namely the Mobile Data Challenge (MDC) dataset, which was collected in the city of Lausanne in Switzerland with around 180 participants. We found a satisfactory user future location prediction accuracy of 70−84% and area congestion prediction accuracy of 65−73% for the users

Mobility Prediction-Assisted Over-The-Top Edge Prefetching for Hierarchical VANETs

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based Over-The-Top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption

Intelligent Safety Message Dissemination with Vehicle Trajectory Density Predictions in VANETs

Integration of wireless communication systems and machine learning techniques are generating new applications and services in vehicle ad-hoc networks (VANETs). By analyzing data transmission in vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications, an intelligent transportation system (ITS) can provide better safety applications. This work explores machine learning approaches to estimate vehicle density on predicted trajectories, which is further utilized to provide intelligent safety message dissemination. With our approach, the traffic safety message, such as accident notifications, will only be disseminated to relevant vehicles that are predicted to pass by the accident areas. Depending on the network connectivity, our system adaptively chooses vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or hybrid communications to disseminate a message to relevant vehicles. We evaluate the system by using real-world VANET mobility datasets, and experiment results show that our system outperforms other mechanisms without considering predicted vehicle trajectory density information