Enabling Smart Urban Services with GPS Trajectory Data - Chao Chen Daqing Zhang Yasha Wang Kartoniert (TB)

With the proliferation of GPS devices in daily life trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet a considerable gap still exists between the raw data available and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues large data volumes to process an d sparse GPS data to name but a few. Moreover the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties including drivers passengers travellers urban planners etc.In this book we present our latest findings 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. 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 party involved we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided to demonstrate the effectiveness and efficiency of using trajectory data.Unlike other books which deal with people and goods transportation separately this book also extends smart urban services to goods transportation by introducing the 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 we feel this extension is bot logical and essential. Lastly we discuss the most important scientific problemsand open issues in mining GPS trajectory data.