The use of traffic data, acquired and transmitted in real time, is important for motorway traffic management. During the European LIFE+ project BrennerLEC different data streams converging on ODH were used for traffic flow management. We present a method for calculating the optimal motorway speed based on real-time measured data. On the basis of the vehicle flow and the transit speed measured at high frequency (1 minute), an algorithm was developed based on the concept of a "state machine", which in real time calculates the optimal transit speed. This information can be immediately communicated to drivers via variable message signs. The optimal speed is the one that avoids or reduces traffic jams and ensures maximum flow of a road section. Furthermore, Autostrada del Brennero intends to further invest in the digitalisation of its mobility management system and a data hub available for both on-board and centralised software applications opens the way to further implementation of services for users.

1. 1
Real time data and motorway traffic
management
Gianluca Antonacci - CISMA Srl, NOI techpark, Bolzano
Ilaria De Biasi – Autostrada del Brennero SpA, Trento
Open Data Hub Day, Bolzano 20/05/2022

2. 2
Abstract
* The use of traffic data, acquired and transmitted in real time, is important
for motorway traffic management. During the European LIFE+ project
BrennerLEC different data streams converging on ODH managed by NOI
techpark were used for traffic flow management.
* We present a method for calculating the optimal motorway speed based on
real-time measured data. On the basis of the vehicle flow data a real time
algorithm can elaborate information that can be immediately communicated
to drivers via variable message signs.
* The system aims at estimating the optimal speed based on present
situation; optimal speed is the one that avoids or reduces traffic jams and
ensures maximum flow of a road section.
* Furthermore, Autostrada del Brennero intends to further invest in the
digitalisation of the mobility management system and a data concentrator
available for both on-board and centralised software applications opens the
way to further implementation of services for users.

3. 3
Introduction
Partners
Autostrada del Brennero SpA (coordinator)
Environmental Agency – Bolzano
Environmental Agency – Trento
University of Trento
CISMA Ltd
NOI Techpark
Duration
01.09.2016 – 30.09.2021 (now extended in a
new agreement among partners)
Subject
Dynamic traffic & speed management along an
highway route for environmental and mobility
purposes

4. 4
Traffic
11 inductive loops / radars
17 Bluetooth sensors
4 travel time detectors
Noise
1 phonometer
Weather
3 weather stations
2 sonic anemometers
1 vertical wind profiler
1 vertical temp. profiler
Air Quality
13 air quality monitoring
stations
13 low-cost innovative sensors
12 passive air samplers
Monitoring system

5. 5
Open
data
server
Air quality
stations
Air quality
stations
Environmental
sensors
Weather
stations
Real-time road weather data
Real-time air quality data
Real-time weather data
Data
source
«air
quality»
DBMS
Data
source
«weather»
A22 NOI Techpark
10 minutes data
API
«Open Data
Hub»
platform
Province of Bolzano
Province of Trento
Weather
stations
Open
data
server
A22
serve
r
Raw
data
server
Open
data
server
Open
data
server
Hourly / daily
validated
data
FTP
serve
r
API
API
API
FTP
reader
Data integration

6. 6
Concept
Speed limits
reduction to
increase flow
capacity in case of
high volumes of
traffic
Optimal speed

7. 7
Concept
* Traffic density, driving speed and flow are strictly
related
* There’s an optimal speed for each road depending
on the geometry of the stretch, which is not the
maximum speed
* By “optimal speed” we mean the one
corresponding to the maximum flow capacity
* For sub-optimal capacity two different driving
speeds are possible with different speed

8. 8
Concept
The “stop & go” process in a jam
Breaking
Driving “normally”
Decelerating Accelerating
* In a stop & go situation traffic is
“jumping” between two speed states at
given flow
* Finding the optimal speeds optimizes
flow capacity

9. 9
Implementation
* A so called “state machine” was implemented, based on real-time traffic data sent to
ODH (flow, speed and density) from induction loop sensors along the highway
* Use of a semi-automatic management system to calculate the optimal speed limit
based on current traffic conditions
* Adoption on the A22 stretch between Bolzano and Rovereto (~80 km)
* Maximum driving speed is set and displayed on the variable massage signs calculated
on the basis of current registered data
* Homogeneous sub-stretches identified: each has its own “state machine” and they
work synchronously, all feeded by real time data through ODH

10. 10
Implementation
The state machine is part of a larger “Intelligent Traffic System” implemented
so to activate the measures only when needed so to obtain the best possible
efficiency (max benefit with the min. amount of time and the with the
min. disturbance for the travelers).
Software written in python and using JSON protocol for exchanging measured
data and elaboration. Data are read every 5’ and optimal speed calculated
within 2” and delivered back to the traffic control system

11. 11
Implementation
Dashboard in the traffic control room...

12. 12
Implementation
… and quasi real-time exposition on VMS

13. 13
State
machine
TCC
back-
end
TCC
front-
end
(dashboar
d)
1. Change of
status (real-
time)
2b.
Notification
to the TCC
operator
VMS
management
system
2a. Pre-
activation of
VMS template
3. Activation
of VMS
template
Implementation

14. 14
Results
Optimal speed calculation and maximum speed limit defined on the
basis of current traffic condition on each stretch
Progressive reduction of speed limit from
130 to 90 km/h in order to stabilize flow
and avoid stop & go or jams. Speed drops to
about 85 km/h (optimal speed with higher
flow capacity) but doesn’t reach a critical
state

15. 15
Results
Automatic traffic control
Flow in time slot 0-24
Flow in time slot 8-20
Average transit time
Average speed
Perturbation [hours]
Yes No

16. 16
Perspective
Project replication
* Need for an infrastructure that allows continuity of
information to users
* The implementation logic of the speed control dashboard
had to take into account the availability of variable
message signs and loops
* The infrastructure set up (hardware and software) proved
to be suitable, without further modification, for the
application of the dynamic traffic management measures

17. 17
Perspective
Possibility to integrate dynamic traffic control system in “cooperative ITS”
pushing recommended (i.e. optimal driving speed) directly on board of enabled
vehicles with “infrastructure to vehicle communication” which are nowadays
experimented
A glimpse into the future

18. 18
Thanks for your attention
Contacts:
gianluca.antonacci@cisma.it
ilaria.debiasi@autobrennero.it