The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target lin...

Object tracking is crucial to surveillance systems, which provide target information including position, size, and velocity. This paper presents a data association process combining two primary components of visual features and spatiotemporal prediction. In addition, the change perception and the visual distinguishability are utilized to adaptively combine the two primary components. The propos...

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations.Spatiotemporal models arise whenever data are collected across bothtime and space. Therefore such models have to be analyzed in termsof thei...

Support Vector Machines for Spatiotemporal Tornado Prediction INDRA ADRIANTO, THEODORE B. TRAFALIS, and VALLIAPPA LAKSHMANAN School of Industrial Engineering, University of Oklahoma, 202 West Boyd, Room 124, Norman, OK 73019, USA Phone: (405) 325-3721, Fax: (405) 325-7555 Emails: [email protected]; [email protected] Cooperative Institute of Mesoscale Meteorological Studies (CIMMS) University of Ok...

Although there is broad agreement that top-down expectations can facilitate lexical-semantic processing, the mechanisms driving these effects are still unclear. In particular, while previous electroencephalography (EEG) research has demonstrated a reduction in the N400 response to words in a supportive context, it is often challenging to dissociate facilitation due to bottom-up spreading activa...

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done u...

AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...