Editorial

Information fusion, data mining, and knowledge discovery

Intelligent transportation system (ITS) infrastructures contain sensors, data processing, and communication technologies that assist in improving passenger safety, reducing travel time and fuel consumption, and decreasing incident detection time. Multisource data from Bluetooth® and IP-based (cellular and Wi-Fi) communications, global positioning system (GPS) devices, cell phones, probe vehicles, license plate readers, infrastructure-based traffic-flow sensors, and in the future, connected vehicles enable multisource data fusion to be exploited to produce an enhanced interpretation of the monitored or observed situation. This occurs by decreasing the uncertainty present in individual source data. Although demonstrated for more than two decades, data fusion (DF) is still an emergent field as related to day-to-day traffic management operations. Data fusion techniques applied to date include Bayesian inference, Dempster-Shafer evidential reasoning, artificial neural networks, fuzzy logic, and Kalman filtering. This paper provides a survey of ITS DF applications, including ramp metering, pedestrian crossing, automatic incident detection, travel time prediction, adaptive signal control, and crash analysis and prevention, and indicates directions for future research. The encouraging results so far should not conceal the challenges that remain before widespread operational deployment of DF in transportation management occurs.

In intelligent transportation systems (ITS), transportation infrastructure is complimented with information and communication technologies with the objectives of attaining improved passenger safety, reduced transportation time and fuel consumption and vehicle wear and tear. With the advent of modern communication and computational devices and inexpensive sensors it is possible to collect and process data from a number of sources. Data fusion (DF) is collection of techniques by which information from multiple sources are combined in order to reach a better inference. DF is an inevitable tool for ITS. This paper provides a survey of how DF is used in different areas of ITS.