Community-Aware Multi-Task Transportation Demand Prediction

Context-aware taxi demand hotspots prediction

In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms are compared and demonstrate...

Multi-task Gaussian Process Prediction

In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a shared covariance function on input-dependent features and a “free-form” covariance matrix over tasks. This allows for good flexibility when modelling inter-task dependencies while avoiding the need for large amounts of data for training. We show that under the assumption...

Demand Prediction with Multi-Stage Neural Processing

In many technical issues, the processes of interest could be precisely modelled if only all the relevant information were available. On the other hand, detailed modelling is frequently not feasible due to the cost of acquiring appropriate data. The paper discusses the way self-organising maps and multilayer perceptrons can be used to develop two-stage algorithm for autonomous construction of pr...

Transportation on Demand

Transportation Demand Management

Rolling out of bed, José checks his CommuterAlert traveler information system while his coffee brews. The regional commuter assistance program was offering the system through a local automobile dealer when José purchased the family's vehicle. CommuterAlert is predicting that his 10-mile commute on I-275, which normally takes 29 minutes, will now take 46 to 52 minutes. In his printer are identif...