Automated Incident Detection Using Real-Time Floating Car Data

Travel Time Estimation Using Floating Car Data

This project explores the use of machine learning techniques to accurately predict travel times in city streets and highways using floating car data (location information of user vehicles on a road network). The aim of this report is twofold, first we present a general architecture of solving this problem, then present and evaluate few techniques on real floating car data gathered over a month ...

Real-time Incident Detection Using Social Media Data

Traffic State Estimation Using Floating Car Data

There is an increasing availability of floating car data both historic, in the form of trajectory datasets and real-time, in the form of continuous data streams. This paves the way for several advanced traffic management services such as current traffic state estimation, congestion and incident detection and prediction of the short-term evolution of traffic flow. In this paper, we present an an...

Measuring real-time traffic data quality based on Floating Car Data

Congestion is a growing and global problem, impacting the majority of people traveling and goods moving. Governments are challenged to keep the traffic flowing, to reduce pollution and decrease the economic damage caused by congestion. Highly accurate traffic information helps governments to reach their goals to become smart and sustainable. Due to the increasing volume of connected navigation ...

Real-time detection of wildlife using NOAA/AVHRR data Study area :(Kayamaki Wildlife Refuge)

Forest fire in recent years has paid great attention to climate change and ecosystems. Remote sensing is a quick and inexpensive way to detect and monitor forest fires on a large scale. The purpose of this study was to identify forest and rangeland fire hazards using NOAA / AVHRR in Kayamaki Wildlife Refuge. For the purpose of this study, the history of the fire-burns occurred in MODIS products...