The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …

Focus on movement data has increased as a consequence of the larger availability of such
data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in …

Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …

Map-matching is the process of aligning a sequence of observed user positions with the
road network on a digital map. It is a fundamental pre-processing step for many applications …

Map-matching algorithms integrate positioning data with spatial road network data (roadway
centrelines) to identify the correct link on which a vehicle is travelling and to determine the …

Vehicles equipped with GPS localizers are an important sensory device for examining
people's movements and activities. Taxis equipped with GPS localizers serve the …

Vehicle tracking data is an essential “raw” material for a broad range of applications such as
traffic management and control, routing, and navigation. An important issue with this data is …

Monitoring, predicting and understanding traffic conditions in a city is an important problem
for city planning and environmental monitoring. GPS-equipped taxis can be viewed as …

Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map
Matching problem. However, the occurrence of the low-sampling-rate trajectories (eg one …

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System
(or a number of other positioning sensors) with a spatial road map with the aim of identifying …