Abstract
The limiting conditions of traffic in cities, together with the complex and dynamic traffic flows, require an efficient and systematic management and information provision for the traffic participants, with the goal to achieve better utilisation of traffic resources and preserve sustainable mobility. In that context, it is important to identify the traffic flow location features, which requires data and information. This paper presents the application of mobile vehicles for the collection of real time traffic flow data. Such data have become an important source of traffic data, since they can be collected in a simple and cost-efficient way, enabling higher coverage than the conventional approaches, despite the reliability issues. The term referring to that type of data collection, commonly used in scientific and professional literature is FCD (Floating Car Data) and “Probe vehicle”. The efficiency presentation of applying this extensive data source for retrieving necessary parameters and information related to the achievement of sustainable mobility is the final objective of this paper. A description of modern technologies that serve as a basis for probe vehicle data collection has been provided: a geographical information system (GIS), global navigation satellite system (GNSS) and related wireless communication. Within the key technologies review, the development possibilities of data collection by mobile sensors have also been presented.
Highlights
The traffic flow data collection is necessary in traffic engineering and for the efficient management system functioning and for the series of other ITS (Intelligent Transport Systems) applications, setting real time requirements
The terms used in Anglo-Saxon scientific and professional literature referring to this type of data collection are FCD (Floating Car Data) and Probe Vehicle
The application of the FCD implies an integration of the satellite positioning, wireless communication technologies, geographical information system and computer data processing
Summary
Keith Oswald, “Comparison of parametric and nonparametric models for traffic flow forecasting“, Transp. 2005 IEEE Intelligent Transportation Systems, 2005., 2005, str. “Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data in Machine Learning: ECML 2004“, Springer, sv. “A Bayesian network approach to time series forecasting of short-term traffic flows“, u Proceedings. Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749), 2004, str. “An Application of Neural Network on Traffic Speed Prediction Under Adverse Weather Condition“, researchgate.net, str. Widhalm, “Assessing traffic performance using position density of sparse FCD“, u IEEE Conference on Intelligent Transporta‐ tion Systems, Proceedings, ITSC, 2012, str. Li, “Identifying Urban Traffic Congestion Pattern from Historical Floating Car Data“, Procedia – Soc. Behav. “Detection of urban traffic patterns from Floating Car Data (FCD)“, Transp.
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