Determination and prediction of traffic conditions using an extended FCO approach

Abstract

Cooperative Intelligent Transport Systems (C-ITS) for traffic control and traffic guidance depend on knowledge of the current and, in particular, of the future traffic conditions. The estimation methods for determining traffic conditions require very precise and large amounts of traffic data. Conventional traffic data acquisition with detectors on the infrastructure side cannot meet these requirements for known reasons. One way of increasing the database is to use vehicle-generated data. This paper presents an approach to the determination of traffic conditions which includes not only the vehicle's own data but also traffic-relevant data from the surrounding area. This so-called extended Floating Car Observer (FCO) approach can identify vehicles in the surrounding area, and thus detect them at several points in a road network. Systems for the identification of vehicles that are already known from stationary recognition, such as the recognition of Bluetooth MAC addresses or automatic number plate recognition (ANPR), could also be used in a vehicle for mobile identification. Identification can also be done with information from C-ITS. In the communication of these systems, messages such as cooperative awareness messages (CAM) are transmitted with an identifier, and thus enable identification. This article first determines the number of FCO vehicles needed to obtain results that are just as statistically good as the results of the standard FCD calculation. In addition, this paper describes a calculation method that can derive traffic parameters from the data of identification.

Original document

The different versions of the original document can be found in:

• https://zenodo.org/record/1483453 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://dx.doi.org/10.5281/zenodo.1483452,

https://zenodo.org/record/1483453 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

• http://dx.doi.org/10.5281/zenodo.1483453,

https://zenodo.org/record/1483453 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode

DOIS: 10.5281/zenodo.1483453 10.5281/zenodo.1483452