A Cyber‐ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure

Abstract

Traffic data is commonly collected from widely deployed sensors in urban areas. This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. This research, taking into consideration the significant increase in the amount of traffic data and the complexity of data analysis, focuses mainly on the challenge of solving data-intensive and computation-intensive problems. As a solution to the problems, this paper proposes a Cyber-ITS framework to perform data analysis on Cyber Infrastructure (CI), by nature parallel-computing hardware and software systems, in the context of ITS. The techniques of the framework include data representation, domain decomposition, resource allocation, and parallel processing. All these techniques are based on data-driven and application-oriented models and are organized as a component-and-workflow-based model in order to achieve technical interoperability and data reusability. A case study of the Cyber-ITS framework is presented later based on a traffic state estimation application that uses the fusion of massive Sydney Coordinated Adaptive Traffic System (SCATS) data and GPS data. The results prove that the Cyber-ITS-based implementation can achieve a high accuracy rate of traffic state estimation and provide a significant computational speedup for the data fusion by parallel computing.

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

“…This theory is inspired by spatial computational domain, which is designed to represent twodimensional computational surface in GIScience [25]. Comparatively, the ITS computational domain is defined as a highdimensional data space consisting of a large amount of cell tuples projected to a three-dimensional spatio-temporal domain [26].…”

A Novel Vehicle Stationary Detection Utilizing Map Matching and IMU Sensors

“…Over the time, the sources of the navigation systems have evolved from the sun and stars to the modern day navigation sensors [ 1 – 3 ]. Modern navigation has been utilizing the various sensors to locate the position of a vehicle for fleet management, location based service, asset tracking, intelligent vehicle control, and so forth [ 4 ]. GPS and INS are popular navigation devices of the modern days.…”

Mining related information of traffic flows on lanes by k-medoids

“…And the processing of heterogeneous and massive traffic data that derived from different kinds of sensors is a critical part of ITS. Original traffic data cannot be completely processed by traditional mathematic methods, Moreover these are artificial intelligent (AI) methods and multi-sensor heterogeneous data representation [1,2] to process these traffic data. In the actual situation, the algorithms of data mining are used to process traffic data.…”