Augmenting the Floating Car Data Approach by Dynamic Indirect Traffic Detection
| Autor: | Sten Ruppe, Marek Junghans, Christian Troppenz, Mathias Haberjahn |
|---|---|
| Přispěvatelé: | Papaioannou, Panos |
| Rok vydání: | 2012 |
| Předmět: |
Engineering
Data collection business.industry Bluetooth ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Real-time computing Floating car data Verkehrsmanagement Floating Car Data Traffic and Transportation Management law.invention Road transport Floating Car Observer law Public transport Embedded system Dynamic Traffic Detection General Materials Science business |
| Zdroj: | Procedia - Social and Behavioral Sciences. 48:1525-1534 |
| ISSN: | 1877-0428 |
| Popis: | An established approach for the mobile determination of traffic parameters is Floating Car Data (FCD) also known as Probe Vehicle Data. Floating cars are equipped with modules for positioning and transmitting the data to a processing unit. There, the data are processed to derive travel times, spatio-temporal traffic information, etc. The advantage of FCD is that there is no costly stationary infrastructure needed. The drawback is that only a fraction of the real traffic can be used as data base for the generation of reliable traffic information. Furthermore, FCD focuses only on road transport, i.e. pedestrians and cyclists are not detected. In this paper a new approach for an efficient and low-cost large-scale traffic monitoring is presented, which augments the FCD principle and enables the detection of vehicles, pedestrians, cyclists and passengers of public transport to achieve spatio-temporal traffic data by a considerably increase of the underlying database. Since all detections are made indirectly by traffic observers while passing other traffic objects, the new approach closes the gap between FCD and the Floating Car Observer (FCO) principle. The novel approach is based on a method for anonymous positioning by indirect detection of traffic objects (cars, cyclists, pedestrians) using radio-based Bluetooth/Wi-Fi technologies. This is advantageous, since many traffic participants use devices with activated Bluetooth/Wi-Fi functionality (e.g., mobile phones, headsets). Example: a car, which is equipped with specific Bluetooth/Wifi-receivers, detects all traffic objects, which are in the detection area, by their Bluetooth/WiFi identification number. This identification number is augmented by the time stamps and positions of the detecting objects. The measured data is processed to trajectories, travel times, traffic states, origindestination matrices and other traffic parameters. © 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of TRA 2012 |
| Databáze: | OpenAIRE |
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