FLOATING OBSERVER INFORMATION PROCESSING ON THE BASISOF MOBILE BLUETOOTH DATA

The German Aerospace Center (DLR) developed a new traffic monitoring approach (called DYNAMIC) which combines the advantages of Floating Car Data (FCD) and Floating Observer Data (FOD) principles by avoiding their drawbacks. Using DYNAMIC, spatiotemporal traffic data are obtained whereas the number of costly traffic detection infrastructure (e.g. mounted traffic sensors, detection gantries, etc.) is minimised or avoided at all. DYNAMIC is based on detections which are made by Floating Traffic Observers (FTO) using wireless radio-based technologies (e.g. Bluetooth/Wi-Fi) while passing other traffic objects (vehicles, cyclists, pedestrians). The DLR has a long experience with the processing and handling of traffic data like FCD to gain link based and network-wide travel times. The current DLR processing tools that support FCD have been extended to support the processing of Bluetooth data obtained from the floating (DYNAMIC approach) or static detection system. This work describes in detail the newly developed method to process the Bluetooth data and gives the results from conducted field-tests which had been performed to display applicability in a real environment.

[1]  Marek Junghans,et al.  Augmenting the Floating Car Data Approach by Dynamic Indirect Traffic Detection , 2012 .

[2]  C Leitzke.  Reisezeitermittlung im motorisierten Individualverkehr mit Hilfe drahtloser Kommunikationstechnologien , 2012 .

[3]  Dipl.-Ing. Frank Gössel.  Informationsentropische, spektrale und statistische Untersuchungen fahrzeuggenerierter Verkehrsdaten unter besonderer Berücksichtigung der Auswertung und Dimensionierung von FCD-Systemen , 2004 .

[4]  B.S. Kerner,et al.  Traffic state detection with floating car data in road networks , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[5]  Markus Friedrich,et al.  Automatic number plate recognition for the observance of travel behavior , 2008 .

[6]  Alexander Sohr,et al.  Quality of Floating Car Data , 2010 .

[7]  M. Spangler,et al.  Deriving travel times in road networks using Bluetooth-based vehicle re-identification: Experiences from Northern Bavaria , 2010 .

[8]  Marek Junghans,et al.  Conceptual Approach for Determining Penetration Rates for Dynamic Indirect Traffic Detection Based on Bluetooth , 2012 .

[9]  Marek Junghans,et al.  Determining Optimal Fleet Distribution for Dynamic Indirect Traffic Detection Based on Bluetooth , 2012 .

[10]  Stefan Lorkowski,et al.  Erste Mobilitätsdienste auf Basis von "Floating Car Data" , 2003 .

[11]  C Kuehnel.  Verkehrsdatenerfassung mittels Floating Car Observer auf zweistreifigen Landstrassen , 2012 .

[12]  Stefan Lorkowski,et al.  BENEFITS AND LIMITS OF RECENT FLOATING CAR DATA TECHNOLOGY – AN EVALUATION STUDY , 2007 .

[13]  Markus Friedrich,et al.  Generating Trajectories from Mobile Phone Data , 2010 .

[14]  O Czogalla,et al.  Travel time estimation using floating car observers , 2007 .

[15]  Markus Friedrich,et al.  Generating Origin–Destination Matrices from Mobile Phone Trajectories , 2010 .

[16]  Peter Wagner,et al.  A TRAFFIC INFORMATION SYSTEM BY MEANS OF REAL-TIME FLOATING-CAR DATA , 2002 .

[17]  Andreas Leich.  Ein Beitrag zur Realisierung der videobasierten weiträumigen Verkerhsbeobachtung , 2006 .