Chao Chen - Enabling Smart Urban Services with GPS Trajectory Data

The structure of main parts in this monograph

In this monograph, we present our latest achievements on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility, concerning interests from the most-involved parties including taxi drivers, passengers to the little-involved parties like urban planners and travellers. The monograph is organized as follows, as shown in Fig.. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each involved party, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Multiple human behavior-related data including check-in data, POI data are also coupled and combined to gain more complete and detailed understanding about human mobility when necessary. Extensive evaluations with real-world datasets are also provided to demonstrate the effectiveness and efficiency of using trajectory data. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data. In summary, this monograph gradually introduces background, related research, new definitions and technologies for solving problems that beginners will encounter when entering this newly exciting research field.

This monograph differs from existing literatures in the following aspects. On the one hand, we focus on analyzing the GPS trajectory data particularly since such data is adequately representative of human behaviors contributed by multiple kinds of urban residents. Besides, we also intend to cover a wider and more complete range, from trajectory data map-matching, data compression, and data protection to data-driven smart urban services. We group the smart urban services into four categories according to the beneficial party, i.e., drivers, passengers, urban planners as well as travellers (as shown in the middle part of Fig. ). Beyond people transportation, we also extend smart urban services to goods transportation by introducing a novel idea of Crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities and their transportation shares the sufficient overlap (e.g., the same city infrastructure), we believe such extension is logical and essential.