scientific papers

Spatio-temporal Clustering Methods

November 8, 2016

by Jožef Stefan Institute, Ljubljana, Slovenia

The tracking of a person, animal or vehicle generates a vast amount of spatio-temporal data, that have to be processed and analysed in a specific way. This paper presents existing spatio-temporal clustering algorithms, suitable for such data, and compares their running time and noise sensitivity, the quality of the results, and the ability to discover clusters according to the non-spatial, spatial and temporal values of the objects.

Incident Detection Using Data from Social Media
Curious Cat – Mobile, Context-Aware Conversational Crowdsourcing Knowledge Acquisition
A Big Data Architecture for Traffic Forecasting Using Multi-Source Information
Exploring the Links between Persuasion, Personality and Mobility Types in Personalised Mobility Applications
Towards System-Aware Routes
Proactive Charging Schemes for Freight Transport: Dynamic Toll Discounts as a Tool to Reduce National Road Traffic
Big Data Processing and Storage Framework for ITS: A Case Study on Dynamic Tolling
Personalised Persuasion for Sustainable Mobility
Persuasive Technologies for Sustainable Urban Mobility
Estimating the State of Battery Charge in an Intelligent Mobile Home
Near Real-Time Transportation Mode Detection Based on Accelerometer Readings
Big Data Harmonization for Intelligent Mobility: a Dynamic Toll-charging Scenario
An architecture for big data processing on intelligent transportation systems. An application scenario on highway traffic flows
Memory Priming and User Preferences
Smart Cargo for Multimodal Freight Transport: When “Cloud” becomes “Fog”
Travel Time Prediction on Highways
Curious Cat2 – Conversational Crowd-Based and Context-Aware Knowledge Acquisition Chat Bot
TensiStrength – Stress and Relaxation Magnitude Detection for Social Media Texts
Proactive recommendations for Intelligent Mobility - An approach based on real-time big data processing
Personalized Persuasion Services for Route Planning Applications
Understanding Personal Mobility Patterns for Proactive Recommendations