This document summarizes and compares three reactive algorithms for controlling variable speed limits: 1. Mainstream Traffic Flow Control aims to maximize throughput and avoid congestion by finding the critical density and lowering speed limits to control inflow. 2. Specialist algorithm uses shockwave theory to identify traffic states, predict their evolution, and resolve shockwaves with suitable speed limits. 3. Reducing Crash Potential algorithm uses a log-linear model to set thresholds for lowering speed based on crash potential, which is calculated from crash precursors and external factors. The document discusses modeling congestion in traffic simulation and presents preliminary results showing Mainstream Traffic Flow Control improves congestion and mean speed over the base case. Further

1. Comparison of reactive algorithms for
controlling of variable speed limits
Ellen Grumert
Andreas Tapani
Transport Forum, Linköping
January 8-9, 2014

2. Variable speed limit system
• VSLS (Variable Speed Limit System)
• Connected variable speed limit signs
• Detectors, measuring the conditions on the road such as flow and/or mean
speed
• Decision algorithm based on flow or mean speed or both
Source: Foto taken 2010 by Holger Ellgaard, publiced at www.wikipedia.org (accessed 2011-04-13)
Source: Description of MTM, Automatic Incident Detection in the Motorway Control System MTM, March 1999

3. Problem to investigate
• Many of the algorithms in use in real systems are based on
simple control strategies
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May not necessary reflect the flow on the road accurately
• Many of the proposed control strategies in literature have
problems with
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Computational complexity
Uncertainty in robustness
Tuning difficulties of parameters – many parameters or
interpretation issues
High data demand

4. 1 Mainstream Traffic Flow Control (Carlson et. al. 2011)
- Idea
• Aim:
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Maximize throughput at potential
bottlenecks
Avoid congestion
Avoid capacity drop
• How?
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Find critical density (or occupancy)
(corresponding to the maximum throughput)
• Control the inflow by lowering the speed limits
Source: Carlson et. al. (2011)

5. 1 Mainstream Traffic Flow Control (Carlson et. al. 2011)
- Algorithm design
VSLS application area
• VSLS functionality is dependent of:
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Finding critical occupancy
Finding suitable acceleration area
Finding suitable application area
Acceleration area

6. 2 Specialist (SPEed Controlling AlgorIthm using
Shockwave Theory) (Hegyi et. al. 2008, 2010)
• Based on shockwave resolution
𝒒 < 𝒒𝒄
• Tuning parameters have physical interpretation

7. 2 Specialist (SPEed Controlling AlgorIthm using
Shockwave Theory) (Hegyi et. al. 2008, 2010)
• Idea:
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Identify the traffic states – Detect chock waves
Predict their future evolution
Resolve the shockwave with suitable speed limits
Lowering speed+same density
lower flow
Source: Hegyi, A. and Hoogendoorn, S.P., Dynamic speed limit control to resolve shock waves on freeways – Field test results of the SPECIALIST algorithm

8. 3 Reducing crash potential (Lee et. al. 2003, 2006)
• Log-linear model (analouge to linear regression)
• Crash potential = crash rate
• Crash potential based on crash precursors and external
control factors
• Thresholds for lowering the speed based on crash potential

9. 3 Reducing crash potential (Lee et. al. 2003, 2006)
• Model calibration
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Actual crash and traffic data collected from a 10-km stretch of the
Gardiner Expressway in Toronto, Canada
13-month period from January 1998 to January 1999
234 crash cases and 234 non-crash cases.
• Crash precursors:
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Temporal variation of speed: standard deviation of speed divided by
average speed (over all lanes)
Spatial variation of speed: difference in speed between upstream
and downstream locations
Lane changing behavior: covariance of volume difference between
upstream and downstream locations on adjacent lanes
• External factors
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Road geometry
Peak- or off-peak pattern

10. Microscopic traffic simulation
- Modeling approach
• How to model congestion?
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On-ramp
Lanedrop
Lowering speed on one section or for a few vehicles for some time
• Potential problems
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Models vs. reality
Calibration – data
How realistic is each senario?

11. Microscopic traffic simulation
- Base case modelled in SUMO
• Choice: Lanedrop
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Realistic flow levels
Modelling of capacity drop reflects reality (based on empirical
studies from litterature)
Detector area
150m
For VSLS modelling approach 1 and 2: Find critical occupancy!

12. Preliminary results – Mainstream Traffic Flow
Control

13. Conclusions
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Careful consideration needs to be taken regarding modelling
congestion when evaluating the algorithms.
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In SUMO a lanedrop seems to be most suitable to model congestion
Preliminary results from Mainstream Traffic Flow Control (MTFC)
shows:
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Improved results with respect to congestion (comparing basecase with MTFC).
Higher mean speed for MTFC compared to basecase.
Further work:
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Investigation of two more algorithms and comparisons between the three.
Expecations
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Different algorithms might have different advantages
The different algorithms might be more beneficial in some specific situations
(not necessary the same)