Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

In this work, we present three classes of methods to extract information from triangulated
mobile phone signals, and describe applications with different goals in spatiotemporal …

Traffic forecasting as a canonical task of multivariate time series forecasting has been a
significant research topic in AI community. To address the spatio-temporal heterogeneity …

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …

Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …

Taxi demand prediction is an important building block to enabling intelligent transportation
systems in a smart city. An accurate prediction model can help the city pre-allocate …

Estimating the travel time of any path (denoted by a sequence of connected road segments)
in a city is of great importance to traffic monitoring, route planning, ridesharing, taxi/Uber …

Traffic speed prediction is known as an important but challenging problem. In this paper, we
propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction …

Traffic speed prediction is a long-standing and critically important topic in the area of
Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging …

Crime has become a major concern in many cities, which calls for the rising demand for
timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the …