SFScon21 - Gianluca Antonacci - Optimal motorway speed management. a real-time algorithm for calculating the best flow condition
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The document discusses a real-time algorithm for optimizing motorway speed management to enhance traffic flow and reduce congestion through dynamic speed limits. It details the implementation of a state machine that calculates optimal speeds based on real-time traffic data and coordinates speed limits along a specific highway stretch in Italy. Additionally, it outlines potential future developments for integrating this system with vehicle communication technologies.
![Application
* Level of service (A
free flow → F stop &
go, not reached in this
case)
* Flow [vehicle/hour] in
both driving and
overtaking lane
* Speed [km/h] in both
driving and overtaking
lane + dynamical speed
limit on VMS
* Grey color =
automatic control
system running](https://image.slidesharecdn.com/gianlucaantonacci-optimalmotorwayspeedmanagement-211115155716/85/SFScon21-Gianluca-Antonacci-Optimal-motorway-speed-management-a-real-time-algorithm-for-calculating-the-best-flow-condition-12-320.jpg)
![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](https://image.slidesharecdn.com/gianlucaantonacci-optimalmotorwayspeedmanagement-211115155716/85/SFScon21-Gianluca-Antonacci-Optimal-motorway-speed-management-a-real-time-algorithm-for-calculating-the-best-flow-condition-14-320.jpg)
SFScon21 - Gianluca Antonacci - Optimal motorway speed management. a real-time algorithm for calculating the best flow condition
- 1. Optimal motorway speed management: a real-time algorithm for calculating the best flow condition Gianluca Antonacci, PhD CISMA Srl, NOI techpark, Bolzano gianluca.antonacci@cisma.it SFScon, Bolzano 12-13/11/2021
- 2. This work was developed within the EU-LIFE+ BrennerLEC project Introduction Partners Autobrennero SpA (coordinator) Environmental Agency – Bolzano Environmental Agency – Trento University of Trento CISMA Ltd NOI Techpark Duration 01.09.2016 – 30.04.2021 (extended to 30.09.2021 due to Covid-19 emergency) Subject Dynamic traffic & speed management along an highway route for environmental and mobility purposes
- 3. Concept Speed limits reduction to increase flow capacity in case of high traffic Optimal speed
- 4. 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 speed are possible with different speed
- 5. 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
- 6. Implementation * A so called “state machine” was implemented, based on real-time traffic data (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 (~75 km) * Maximum driving speed is set and displayed on the variable massage sign calculated on the basis of current registered data * Homogeneous sub-stretches identified: each has its own “state machine” and they work synchronously
- 7. 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
- 8. Implementation VMS needed (distance of no more than ~2 km is necessary for this application)
- 9. Implementation Dashboard in the traffic control room...
- 10. Implementation … and quasi real-time exposition on VMS
- 11. Implementation 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
- 12. Application * Level of service (A free flow → F stop & go, not reached in this case) * Flow [vehicle/hour] in both driving and overtaking lane * Speed [km/h] in both driving and overtaking lane + dynamical speed limit on VMS * Grey color = automatic control system running
- 13. Results Tests Phase 1 Phase 2 Phase 3 Phase 4 Activation period 03.2017- 05.2018 01.2019- 09.2019 12.2019- 02.2020 05.2021- 09.2021 Test sessions 34 77 15 79
- 14. 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
- 15. 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
- 16. 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
- 17. Thanks for your attention Gianluca Antonacci, PhD CISMA Srl, NOI techpark, Bolzano gianluca.antonacci@cisma.it SFScon, Bolzano 12-13/11/2021