The value of large amount of trajectory data has received wide attention in many applications including human behavior analysis, urban transportation planning, and various location-based services (LBS). Nowadays, both scientific and industrial communities are encouraged to collect as much trajectory data as possible, which brings many opportunities and problems, including: 1) the raw data collected by the GPS device requires reorganization and preprocessing to enable further analysis; 2) it is expensive and challenging to store and process such big trajectory data efficiently; and 3) by leveraging effective spatial-temporal queries from the trajectory data, it enables us to discover various knowledge that are difficult to identify intuitively. In this thesis, we propose a comple...[ Read more ]

The value of large amount of trajectory data has received wide attention in many applications including human behavior analysis, urban transportation planning, and various location-based services (LBS). Nowadays, both scientific and industrial communities are encouraged to collect as much trajectory data as possible, which brings many opportunities and problems, including: 1) the raw data collected by the GPS device requires reorganization and preprocessing to enable further analysis; 2) it is expensive and challenging to store and process such big trajectory data efficiently; and 3) by leveraging effective spatial-temporal queries from the trajectory data, it enables us to discover various knowledge that are difficult to identify intuitively. In this thesis, we propose a complete system from the preprocessing of raw trajectory data, to the storage and query processing of the trajectories, and finally the discovery of the hidden knowledge from such data. For each component of the system, we study and solve a specific problem regarding to the component, as: 1) inferring the road type in crowdsourced map services; 2) exploring the use of diverse replicas for big location tracking data; and 3) recommending trajectories for effective and efficient hunting of taxi passengers.

For the road type inference problem, we propose a combined model based on stacked generalization to infer the types of road segments, and conduct eight experiments based on different classifiers to show that our method is much better than the baseline methods. For the use of diverse replicas for big trajectory data, we propose BLOT, a system abstraction which tries to find the optimal set of diverse replicas that suitable for the costs of a set of trajectory queries. Based on our greedy strategy, the experiments show that our solution is effective and efficient. For the taxi hunting route recommendation problem, we introduce the HUNTS system based on the estimations of the benefits of road segments, and make reasonable hunting trajectory recommendations for taxi drivers to make more money.

Due to the low-density, sparsity, and uncertainty of trajectories, handling such data may face many challenges. In this thesis, we will discuss the important challenges and research issues of each aspect, and compare the differences between our methods and the state-of-the-art.