Innovations in Traffic Management

Prof. dr. Serge Hoogendoorn Delft University of Technology, AMS, Arane

Using Wide Moving Jam (WMJ) suppression as leading example

Message of talk…• Recall importance of further integration of traffic management measures

• Refresh importance of field trials such as PPA for driving innovations

• Present why innovations in monitoring (in-car technology) are needed to improve integrated network management

• Discuss how control integration of road-side and in-car measures could work from a traffic engineering perspective

• Use wide moving jam resolution as main example instead of ramp-metering

Moving jam example• Wide moving jam (in Dutch: ’filegolf’)

• Occurs ‘spontaneously’ in unstable flow and is hence hard to predict

• Once present, a wide moving jam… - Has very predictable dynamics (moves at -18 km/h)

- Reduces freeway capacity with 30%!

- May trigger new bottlenecks

- Increases un-safety, pollution, and fuel consumption

• Can be solved using smart traffic management!Wide moving jam A10 (PPA site)

How to resolve?• Detect the wide moving jam

• Determine its severity (number of ‘excess’ vehicles to be ‘removed’ by limiting inflow)

• Determine if there is space to temporarily store vehicles that are witheld to flow into jam

• If solvable (available space > severity), implement control strategy

• Monitor to check if jam is resolved

Isolated approach using VSL• SPECIALIST algorithm developed by TU Delft on behalf of RWS

• Fixed speed limit deployed over variable roadway stretch: SPECIALIST computes length that is required to remove excess vehicles

• After tuning, we had 2.8 activations per day resolving jam in 72% cases…

Why only activate in 50% of all WMJ cases? Why is the WMJ not resolved in 28% of the activations?

Improving effectiveness?• Increase # activations by increasing control space:

support variable speed limits by using other control measures, e.g. ramp metering: coordination

• Increase % of WMJ resolved upon activation by correctly determine control task (vehicles ‘too many’ in moving jam) and available control space: improved state estimation

• Only deploy in case moving jam: improving diagnostics

• Note that all these improvements equally apply to PPA fase 1 and are effectuated in phase 2 (and 3)

Increasing control space

• COSCAL v2 integrates VSL and ramp metering

• Use of ramp as buffer to support VSL control approach making it much more effective

• Shows need for coordination of measures

• Extension to multiple on-ramps, intersection controllers, etc. (similar to PPA)

• But: requires insight into storage space on ramps / intersections and relation with bottleneck

• Note: both main issues in PPA!

Moving jam Relative space of Slave buffer = relative space of Master buffer

Improve queue estimation

• Improving queue estimates by fusing TomTom FCD data with intersection data using radar as ground truth

• Can we determine queue lengths from FCD data? How much will the estimates improve?

• Determine relation bottleneck and buffer based on FCD data to determine effectiveness of butter in real-timeCourtesy of FileRadar / Arane

s106

on-ramp

A10

Reducing hardware• When can we reduce the dependency on

inductive loops for freeways and for urban networks?

• Will this be different for different functions (queue tail protection, ramp metering)?

• What will it mean for the way we control traffic: current ramp-metering requires flows, but we can also meter with only speed information…

• Need to rethink traffic management!1700

1800

1900

2000

2100

2200

2300

2400

VVU

(vtg

-u)

No metering, capdrop 500 vtg/h Metering + queue protection

10% queue underestimation 10% queue overestimation

• Joint research with TNO and RWS into ‘value of data’

• Reveals impact of data quality on controller effectiveness

• Example showing queue estimation for ramp metering

Improving diagnostics using FCD• Use mix of traditional detection and FCD to

improve freeway state estimates

• Improved estimation allows determining surplus of vehicles in moving jam (head, tail, density jam) better

• Furthermore: development of advanced classification strategies pilot INM Melbourne using image processing techniques (using pattern recognition)

• Correct classification leads to better control

Better diagnostics, better control• Different bottlenecks, different strategies (example PPA fase 2.1 site)

Type Strategy Measures

Wide moving jam Reduce inflow into shockwave with moving jam ‘surplus’

Variable speed limits

On-ramp bottleneck Reduce inflow into bottleneck to achieve target density

Ramp metering

Merge bottleneck Reduce inflow into bottleneck to achieve target density at merge

Mainline metering

Spillback Increase outflow from queue spilling back Intersection control

Phase 2• No predictions but timely reaction • Only control for bottlenecks than

can be resolved • Careful consideration of buffers

to be used • Assessment shows positive

impacts (100-250 veh-h savings per peak)

• Currently further improving monitoring (data fusion)

Implementation of

lessons learnt from

phase 1

Phase 3: anticipatory control

• Anticipatory network management: optimise traffic management and control measures anticipating on impacts of information

• Theoretical studies show that we can get very close to system optimum

• Implications? Traffic information and management can be ‘decoupled’ (as long as you understand traveller response)

• TU Delft developing real-time approaches1

3

No anticipation on traveler response

Happy marriage of

road-side and

in-car systems

Car as actuator?

• COperative Speed Control ALgorithm developed by TU Delft / Berkeley

• Combination of in-car / roadside (or in-car only) variable speed limits

• Could be the ‘brains’ of more technically focussed tests

Message of talk…• Recall importance of further integration of traffic management measures

• Refresh importance of field trials such as PPA for driving innovations

• Present why innovations in monitoring (in-car technology) are needed to improve integrated network management

• Discuss how control integration of road-side and in-car measures could work from a traffic engineering perspective

• Use wide moving jam resolution as main example instead of ramp-metering

Innovations in Traffic Management

Prof. dr. Serge Hoogendoorn Delft University of Technology, AMS, Arane

Using Wide Moving Jam (WMJ) suppression as leading example

Guiding future developments…• Integration traffic management measures has large potential and can reduce

impact of performance reducing congestion phenomena

• Limited use of breakdown prediction due to high variability in demand and supply: timely response is more effective that inaccurate pro-active actions

• Traditional data collection techniques fall short for different tasks, but has value until FCD data collection has matured: implementing data fusion is key!

• In particular when cars become actuators, control tasks can become very complex: keep control design simple and if possible decentralized, focussing on achieving optimal emergent controller behaviour

Decentralisation• Modified backpressure technique with local

coordination only causes “optimal” emergent (self-organised) control patterns

• Example shows difference between traditional control approach (TA) and modified backpressure (BP)

• For lower loads, TA performs better

• For higher loads, BP performance is much higher (40% in case of 2000 ver in network)

TA

BP

• Field-test ready BP outperform traditional approach for higher network loads

Fighting complexity? • Phase 1: for each vehicle we held back for 1

min on urban road, we save 2 veh-min delay on freeway

• This means that for buffers with less than 50% of the vehicles actually traveling to the bottleneck the delay we incur is actually larger than the improvement we make

• Lack of strong ‘long distance’ relations provide support for decentralisation (e.g. BackPressure)

Relation with bottleneck for phase 1 reveals relatively few candidate buffers