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