Bluetooth Detection as a Low Cost Data Source for Freeway Management

Abstract

As traffic demand is worldwide steadily growing, the need to operate and manage transportation networks in a more efficient and resilient way is becoming more and more important. For this purpose, the usage of low-cost ITS sensors is a promising way to gather the necessary data that is required for a suitable traffic control. Having said this, the usage of stationary low-cost Bluetooth detectors next to freeways has proven to be an appropriate method for such data collection by detecting Bluetooth-enabled devices with activated radio interface on board Objectives & Methodology Results & Conclusions Main References The developed approach aims at: • Determination of valid travel times on the freeway using a dynamic and adaptive filter algorithm. • Fast and reliable determination of the traffic state and incidents with their spatial and temporal behavior (start and end time, incident location and length, affected lanes etc.) in the whole freeway network. • Using this information for a dynamic rerouting in the freeway network targeting at a fast and reliable dynamic net control 1.

Full text

www.postersession.comwww.postersession.com
www.postersession.com
Bluetooth Detection as a Low Cost Data
Source for Freeway Management
International Seminar on Integrated Road
Transport and Mobility
Cape Town
November 5-7, 2018
Martin Margreiter, Technical University of Munich, Germany
Fritz Busch, Technical University of Munich, Germany
martin.margreiter@tum.de
As traffic demand is worldwide steadily growing, the need to
operate and manage transportation networks in a more efficient
and resilient way is becoming more and more important. For this
purpose, the usage of low-cost ITS sensors is a promising way to
gather the necessary data that is required for a suitable traffic
control. Having said this, the usage of stationary low-cost
Bluetooth detectors next to freeways has proven to be an
appropriate method for such data collection by detecting
Bluetooth-enabled devices with activated radio interface on board
Objectives & Methodology
Results & Conclusions
Main References
The developed approach aims at:
•Determination of valid travel times on the freeway using a
dynamic and adaptive filter algorithm.
•Fast and reliable determination of the traffic state and incidents
with their spatial and temporal behavior (start and end time,
incident location and length, affected lanes etc.) in the whole
freeway network.
•Using this information for a dynamic re-routing in the freeway
network targeting at a fast and reliable dynamic net control
1. Margreiter, M. (2016), “Automatic Incident Detection Based on
Bluetooth Detection in Northern Bavaria”, International Symposium on
Enhancing Highway Performance, Berlin, Germany.
2. Margreiter, M. (2016), “Fast and Reliable Determination of the Traffic
State Using Bluetooth Detection on German Freeways”, World
Conference on Transport Research, Shanghai, China.
3. Margreiter, M., Spangler, M., Zeh, T. & Carstensen, C. (2015),
“Bluetooth Measured Travel Times for Dynamic Re-Routing”, 3rd
Annual International Conference Proceeding on ACE, Volume 2, Global
Science and Technology Forum, Singapore.
Bluetooth detection has proven to be a reliable and cost-efficient
(inexpensive sensor and antenna, only one unit needed for the
whole cross-section, very low energy consumption and therefore
operation possible with solar power, no maintenance needed, easy
and inexpensive installation next to the roadway) source for real-
time travel time data including traffic state and incident detection.
The incident detection quality (false alarms, reaction time etc.) is
slightly better than current methods based on local detection.
Introduction & Motivation
Bluetooth detection principle
of passing vehicles.
After the re-
identification of the
device’s MAC-address
a subsequent detector
location, the travel time
of this device –and
therefore the carrying
vehicle –can be
determined between
both locations. Bluetooth scanner coverage area
Bluetooth detection rate (DR) example over several years
Result of the real-time Bluetooth-based incident detection
Bluetooth data shows no disadvantage in quality and detection
speed of incidents in comparison to traditional methods based on
inductive loops or automatic number plate recognition.
The detection rate
(amount of detected
Bluetooth devices as a
share of whole traffic)
is in average between
20 %and 25 %
depending a lot on the
share trucks (higher
equipment rate of
trucks, around factor 5
in comparison to cars)
but also the weekday
and time of the day.
In average, every sixth
vehicle has two
instead of one active
Bluetooth device on
board.
Measurement
years
Measurement
days
Freeway
Location
Detection
rate
2010
55 A7
AK Feuchtw/Crailsh (N)
20.7 %
2011
–2014 1380 A3
AK Nürnberg (W)
21.2 %
2011
–2014 1380 A7
AK
Feuchtw/Crailsh (N) 34.6 %
2011
–2014 1380 A7
Bottenweiler
35.3 %
2011
–2014 1244 A6
AK Nürnberg
-Ost (W) 22.0%
2011
–2014 1244 A7
AK Biebelried (S)
31.5 %
2014
–2015 261 A3
AK
Nürnberg (W) 24.7 %
2015
–2016 639 A3
Kitzingen
/Schwarza. (O) 23.9 %
2015
–2016 633 A3
AD
Seligenstadt (O) 20.5 %
2015
–2016 624 A7
AS
Wasserlosen 26.8 %
2015
–2016 571 A3
AK Biebelried (O)
18.8 %
2015
–2016 569 A7
AK
Feuchtw/Crailsh (N) 26.9 %
2015
–2016 569 A7
Bottenweiler
26.6 %
2015
–2016 527 A9
AK
Nürnberg (N) 11.7 %
2015
–2016 527 A73
Röthenbach
18.5 %
2015
–2016 514 A6
AK
Nürnberg-Süd (W) 22.0 %
2016
317 A7
AK
Feuchtw/Crailsh (S) 19.8 %
2016
243 A7
AK Biebelried (N)
22.6 %
Mean detection rate:
23.8 %
Mean detection rate weighted by amount of measurement days:
25.6 %
Overall Bluetooth detection rates