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 and 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.
