Network effects from improved traffic signals

Kristina Schmidt

Transek AB

“On trial, we penetrate recently discovered areas and the straight road we find only by detours.

...

Thorough knowledge can only be accomplished by studies of literature on the one hand and

practical experience on the other”

C G Jung

From the preface of the 5:th edition of The Unconscious

BackgroundThe idea was born in an early ITS-evaluation project (TOSCA I) showing large network effects of traffic signals

• As routes with improved signals attract traffic the delays will increase because of increased flow.

• If the improvements were spread evenly over the urban area the route choice effect should become minimal

• The idea was born to study how area strategies for traffic signals can contribute to fulfil traffic policy goals compared to i e construction of roads

Stage I• Inventory of strategies for

improved traffic signals

• Development of methodology for transferring data between micro- and macro simulation models

• Indication of network effects.Comparison between effects calculated by micro simulation models and effects calculated by macro model

Stage II• Evaluation and comparison of

two strategies for implementation of improved traffic signals

• Traffic effects, emission and safety effects

• Aim: to stimulate strategic thinking when implementing improved traffic signals

Micro and macro simulation models

Micro simulation• detailed analyses; intersection

design and signal settings

• behaviour rules for individual drivers

• dynamic - includes variation and randomness in arrival time

• extremely requiring on quality of input

• route choice lacking or dubious

• for disrupted traffic conditions as incidents

Macro simulation• long-term network effects of larger

investments as bridges or by-pass roads

• traffic flows through network as the consequence of defined relations of speed and flow

• demand is given for od-pairs as a medium-flow rate for an hour or a day

• network interaction not considered

• for ‘normal’ traffic conditions

Transferring data between micro simulation model and emme/2 Approach #one• Micro simulation of signalised intersection

• Volume-delay pairs for turns with different characteristica, i e signal settings, geometry and dependency of flow on interacting links

• Estimation of turn delay functions for Emme/2

• Effects on route choice, traffic work and delay

The definition of capacity is the problem to the answer

Flow

Speed

Micro simulation experiences

• The delay at low traffic hours is very different for different turns

• Some signalised junctions are critical. As the traffic is stopped in those, over saturation is stopped, and downstream turns do not show any dependency on flow at all

• Typical junctions are hard to define

Turn 5

0,00

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60,00

0.4 0.6 0.8 1.0 1.2

Flow Volume

De

lay

(s

ec

on

ds

)Figure 4.1 turn 5 Secondary road filtering signal right turn high dependency 1 lane

Transferring data between micro simulation model and Emme/2 Approach #two• Micro simulation of signalised intersection before and after

improvement

• Average turn delay in seconds in the Emme/2 scenarios

• Simplification assumption for result analysis: All delays that are dependent of flow refers to the upstream link

• Effects on route choice, traffic work and delay

Evaluation of two strategies - I

99-05-21 14:15MODULE: 2.13TRANSEK5.. ..ks

EMME/2 PROJECT: Nteffekter av trafiksignalerSCENARIO : 2000 summa scenario 1

WINDOW:1269.1/6401.561274.7/6405.79

LINKS: mod=c& type=1,99

UI1COL-IND:

THRESHOLD:LOWER: -*****UPPER: ******

SCALE: .5

Reduce congestion in inner city of Gothenburg

• 53 signal intersections were improved

• The improvement is dependent of the functionality of the signal today - how well the co-ordination works and whether there is public transport prioritisations

Evaluation of two strategies - II

99-04-28 18:00MODULE: 2.13TRANSEK5.. ..ks

EMME/2 PROJECT: Nteffekter av trafiksignalerSCENARIO : 3000 summa scenario 3

WINDOW:1267.1/6400.521275.5/6406.83

LINKS: mod=c& type=1,99

UI1COL-IND:

THRESHOLD:LOWER: -*****UPPER: ******

SCALE: .5

Reduce traffic in inner city• “Gating” is introduced at 17

intersections and 46 turns

• 2-minute queuing time at gate

• The junctions in question were carefully studied in terms of geometry and demand

• Try to compensate those who have their destination in the city by i e improved signals at parking lots

Reduce congestion - ??

• Vehicle mileage is increased by 3.5% in inner city area

• Vehicle mileage is decreased by 0.3% in the rest of the urban area

• Average speed increase in inner city from 26.2 to 27.4 (4%).

• Average speed for those having their origin and/or destination in city increases from 38.4 to 38.9 kph

• Average speed for the rest of the traffic is almost unaffected.

30.9

11.8

31.4

12.9

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1000

veh

kmReference Less congestion

Vehicle mileage in inner city

Other traff ic

City traff ic

Reduce traffic by gating

• Vehicle mileage in inner city area is decreased by 8.9%.

• This increase refers to through-traffic only

• All gated turns but three get a decreased traffic

• Average speed decreases for the vehicles with destination and/or origin in city with 4% (from 38.4 to 36.7)

• Emission effects?

30.9

11.8

30.9

8.2

0

10

20

30

40

50

1000

veh

kmReference Gating

Vehicle mileage in inner city

Other traff ic

City traff ic

Conclusions

• Micro simulation results might be used as an average gain of time.

• OD-matrix and location of parking lots more important than detailed coding

• The results are easier to analyse if network coding in Emme/2 is detailed

• As presumptions are uncertain results should be handled with care but can help the traffic planner anyway.