The document discusses a low-cost Bluetooth monitoring system for analyzing traffic flows. Bluetooth signals from vehicles are captured by roadside sensors to identify traffic patterns. Data on vehicle IDs, timestamps and locations are stored and used to derive information like travel times, speeds and origin-destination matrices. The system provides traffic data for management and planning while protecting privacy. Fixed sensors deployed in various cities collect long-term data, while mobile sensors allow short-term intensive monitoring.

1. Analyzing traffic flows with low-cost
bluetooth monitoring system and
dedicated data-analysis algorithms
Gianluca Antonacci, PhD
CISMA Srl, NOI techpark, Bolzano
gianluca.antonacci@cisma.it
SFScon, Bolzano 13-14/11/2020

2. - Traffic data represent essential information used by city
administrators,for traffic management & road network planning
- Demand for widely spread and low-cost monitoring traffic systems,
together with interpretation and analysis of traffic flows and patterns
- In this talk we present a solution to tackle this huge challenge: a
Bluetooth-based traffic monitoring system with data-analysis
algorithms
- Technology was firstly developed within the Life+ INTEGREEN
project (2012-2015); where company CISMA was a subcontractor; as
a follow-up we’re now in engineering product and service
Introduction

3. Concept
- Method is based on the presence of bluetooth device in vehicles, typically
navigation / infotainment systems
- On board bluetooth system is typically discoverable; not pairable unless you have
the password
- We want to catch the BT announcement sending a unique identification signal
(MAC address)
- Method is privacy compliant
- By counting the transiting vehicles in many locations within a target city, it is
possible to identify the main traffic patterns

4. Concept
The process works as follows:
1) catch the same bluetooth ID (i.e. MAC address) on two
measurement stations along the same stretch
2) save ID & timestamp
3) hash on the MAC-address to avoid privacy concerns
3) analyze ID couples to derive direction, travel time and speed
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10:13 Bluetooth on board
No bluetooth on board

5. Concept
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internet
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global
database
- Approximately 20% of vehicles have a visible BT on
board (empirically derived through measurement
campaigns)
- This a significant percentage to define travel time and
origin/destination estimate
- Absolute flux can be estimated through calibration (not
very precise, but useful for rapid estimate)

6. Hardware
Modem for real time
data transfer
Acquisition board
Raspberry Pi 3B+
Backup battery 8Ah
autonomy ~8h
External
bluetooth antenna
esterna on USB
port, long range
power = 18 dBm
sensitivity = -86
dBm
AC/DC
transformer
220V → 5V with
fuse & switch

7. Hardware
Fixed installation (on light poles)
- 220V power supply (light pole) with ~8h buffer battery to cover possible
blackouts and 220V AC → 5V DC stabilized transformer
- Raspberry Pi 3B+ based acquisition platform
- USB external high-sensitivity Bluetooth device
- GPRS modem for real-time data transfer on central database (opendatahub)
- 2 kg weight
Mobile installation
- No external power supply needed; 20Ah battery for ~3 days autonomy
- Raspberry Pi Zero low power
- Internal Bluetooth device
- No data transmission, local database
- Small form factor ~0.25 kg,weight, case mountable in 5’ minutes on a pole

8. Application
Hardware characteristics and features
- capture radius approx. 100 m in free field
- average capture time about 2" from when target is visible
- maximum speed for capture about 150 km/h (verified on motorway installation)
- above values may vary depending on possible interferences
- probability of increasing capture as speed decreases; for urban speeds the parameter
is irrelevant
- the system captures BT on and visible, for vehicles typically navigation systems or
wireless hands-free systems; smartphones by default have BT in non-visible mode (i.e.
they can be connected but do not announce themselves) so they are typically not
counted
- fleet coverage (verified with manual counting and inductive loops): ~18% urban area,
~22% on the motorway
- the antenna has isotropic coverage, therefore direction is NOT measured a priori but
derived from timestamps (i.e. lower timestamp is the “entrance” gate and higher
timestamp is the “exit” gate, direction is derived accordingly)

9. Installation
Presently many fixed installations are running
- 22 in Bolzano
- 2 on A22 highway
- 24 in Merano
- 5 in the Province of Trento (ready to go)
You can have a look at raw data at
https://analytics.mobility.bz.it/
(frontend to opendatahub managed by NOI)
- Moreover 29 mobile boxes for 2-days
intensive monitoring

10. Installation
Fixed bluetooth stations in Bolzano

11. Installation
Fixed box (mains power supply required) Mobile box (battery powered)

12. Database
What is gathered by the sensor?
For each capture of passing Bluetooth device, 3 information are gathered and saved in
local DB:
* MAC-address of the device in anonymized form with unique identifier (hash)
* Timestamp, i.e. the instant of capture
* Identification of the measuring station (this information is later necessary to analyse
data collected by the network of sensors)
* For fixed stations data is transferred every 10’ to a central DB (opendatahub, managed
by NOI)
* For mobile stations online transfer is not used in order to save power and data are
downloaded at the end of intensive campaigns and merged in a unique database

13. Database
S2
Each couple of points sharing the same MACaddress in a certain time window
indicates a single transit from station A to B → different elaborations can be
performed:
- O/D matrix by time slots,
- speed (average and distribution) and travel time (average and distribution)
- direction of transit using the capture timestamps

14. Software: data elaboration
Continuous comparison with transit measurement system in one point allows
to derive Bluetooth penetration by time slot and vehicle type, so that this
estimate can be extended on the whole network

15. Software: data elaboration
Example of automatic elaboration & plots
- speed distribution along select stretches
- transit counts (scaled to absolute values, assuming 20% of sample coverage)

16. Software: transit count

17. Software: O/D matrix & travel time

18. Software: O/D matrix & travel time

19. If the sampling network is sufficiently wide, a statistical analysis
can lead to the identification of the distribution, intensity and time
evolution of the traffic flow, and creation of origin-destination
matrices. All these information results of essential importance
both for network managing purposes.
- fixed installation for long term traffic monitoring and policy
making
- mobile installation for short term monitoring and road projects
Conclusion

20. Thanks for your attention
Gianluca Antonacci, PhD
CISMA Srl, NOI techpark, Bolzano
gianluca.antonacci@cisma.it
SFScon, Bolzano 13-14/11/2020