COORDINATION OF ACTUATED SIGNALS FOR A CORRIDOR

Dr.K.GUNASEKARAN RAKESH.V

ASSOCIATE PROFESSOR (2012266037)

The major problem faced by metro cities is traffic congestion. Traffic

volume changes every day, so it’s very tedious to manually handle the

intersection and with pre- determined signal time

Its difficult for the traffic police to handle the queue length for each phase

in peak hour.

The signals can't be operated with fixed times because the vehicle arrival

rate is not constant.

An alternate method can reduce the travel time, waiting time and queue

length using actuated traffic signal.

The traffic signals coordination

needs to use platoon dispersion

characteristics for heterogeneous

traffic flow

As a platoon moves downstream from an upstream intersection, the

vehicles disperse i.e., The distance between the vehicles increase which may be

due to the differences in the vehicle speeds, vehicle interactions (lane changing

and merging) and other interferences (parking, pedestrians,etc.,).

PLATOON DISPERSION – LITERATURE

Platoon moves from upstream to downstream which can be based on the

kinetic wave theory. Dropping stone in the water, displacement dissipates rapidly in a

circular type. Here geometric varies from start stock to end stock.

Vehicle-Actuated Signals require actuation by a vehicle on one or more

approaches in order for certain phases or traffic movements to be serviced.

They are equipped with detectors and the necessary control logic to

respond to the demands placed on them.

Vehicle-actuated control uses information on current demands and

operations, obtained from detectors within the intersection, to alter one or

more aspects of the signal timing on a cycle by-cycle basis.

Semi actuated control Full-actuated control

They can reduce delay (if properly timed)

They are adaptable to short-term fluctuations in traffic flow

Usually increase capacity (by continually reapportioning green time)

Provide continuous operation under low volume conditions

Especially effective at multiple phase intersections

To understand the platoon dispersion of heterogeneous traffic on

an ideal corridor (IT Corridor).

To measure the traffic flow and speed profile of vehicles along

the study corridor.

To simulate the traffic flow in a corridor under isolated fixed time

signal control, with co-ordination of fixed time signal control,

and co-ordination of vehicle actuated signal control.

To quantify the delay and queue length of the study corridor for

fixed time signal control, co-ordination of fixed time signal

control and vehicle actuated signal control with the aid of

simulation.

Platoon dispersion of heterogeneous traffic

Signal co-ordination system

Vehicle actuated signal

Simulation of traffic signal controls

The earlier works carried out related to these studies in the areas as follows

Platoon dispersion has been studied extensively under homogenous and lane

disciplined traffic conditions. Robertson model has been used to calibrate

the actual platoon dispersion data.

Study area: Madya kailash to Tidel park stretch, chennai.

Data collection method: video recording systems

Robertson predicted a best fit value of 0.4 for k as per his studies in western

countries, but the k value estimated for the present condition turns out to be

0.022 indicating a high dispersion and thus a complex model is required to

model the heterogeneous traffic conditions.

The main aim was green split allocation for a queuing system. This system

results from a signalized intersection regulated by semi-actuated control in

an urban traffic network. This method based on queuing theory.

Analysis method: Mathematical Program with Equilibrium (or

Complementarity) Constraints (MPEC).

Signal system: sensor installed along the secondary street, main street

depends upon the secondary street. Secondary street queue length maintain

at some constant. All vehicle cleared in secondary street signal turn to red

phase.

output:

Green: Average green times

split

Red: Red time split

Cycle: Average cycle lengths

Benefits of coordinated actuated traffic signal systems by conducting an analysis of

before-and-after data using simulation software. Performance of actuated signal

reduce the travel time and delay. benefit/cost ratio compared to the non-coordinated

actuated traffic signal system.

Study area: Gloucester County, Virginia

Total length: 3.84 km

Number of intersection: 5

Minimum intersection distance:0.8km to 2.4km

PCU: 600 vehicle per hour per lane( non peak hour)

Data collection method: Manual and video mode traffic volume count and delay

time for each intersection. Travel time measurement by GPS vehicle.

Simulation Software: Synchro, and TRANSYT-7F

Result: adaptive spilt feature in travel time improvement range was 30-36% and

intersection delay was reduced by 18- 35%

In this micro simulation software (VISSIM) decreasing the delay, queue

length and travel time by reducing the signal phases at each intersection.

No of intersection: 4

Analysis method: simulation software VISSIM.

Data collection: 1. Incoming traffic volume

2. Intersection traffic volumes

3. Cycle length and split time

output:

average delay time reduced 13.42%

average stop delay reduced 18.49%

Each phase has a minimum and maximum green time to fit the traffic’s

randomicity and fluctuation. This paper considers the influence on drivers as

caused by changing phase, optimizes the phase number and order of actuated-

coordinated signal control intersection based on fuzzy control theory.

Location:

Detectors:

1. Upper detector

2. Stop-line detector

The detectors check the incoming and outgoing traffic volume, and

the information acquired could include cars’ running speed, capacity,

saturation flow rate, and head time in green time

Efficiency of the delay and stop time improved this activated signal

system.

Improvement of effective green time, and saturation flow. The major arm of the

intersection has LOS C while minor arm has LOS D, which was still acceptable.

Site location: Skudai in Malaysia.

No of intersection: 3

Intersection distance: 300m and 100m

Method: Manual calculation and TRANSYT 13

Analysis: Observed‐estimated actual green relationship, Observed‐estimated

effective green relationship, Observed‐estimated g/c relationship,

Observed‐estimated degree of saturation relationship, etc.

In this paper an optimal optimization method, Genetic Algorithm (GA), was

applied for finding a suitable combination of VISSIM parameters.

Vissim calibaration. The main parameters affecting simulation precision are

Desired Speed in Reduced Speed Area (DSRSA), Desired Lane-Change

Distance (DLCD), and Wiedemann99 car-following parameters, the average

desired distance between stopped cars (CC0), the headway time (in second) that

a driver wants to keep at a certain speed (CC1), and safety distance a driver

allows before he intentionally moves closer to the car in front (CC2).

Roberson’s model describes platoon dispersion effectively and needs to be

calibrated for heterogeneous traffic flow.

Delay as the primary performance measure for signalized intersections.

Considering the variability of delay, more reliable signal control strategies

may be generated resulting in improved Level of Service (LOS) of

signalized intersections.

Enhanced understanding of actuated signal control system using to

oversaturate and under saturated flow.

Duo to proceeding simulation performance evaluation vehicle actuated

procedure of traffic signal controlling systems.

IT CORRIDOR MOUNT POONAMALLE HIGH ROAD

• Platoon dispersion of heterogeneous traffic flow data was collected at

selected locations, to capture the characteristics of vehicle platoon

movements.

• Volume count survey and spot speed was conducted on Mount Poonamalle

High Road. This has been used for coordination of actuated signal on the

study corridor.

Data collection.

Madya kailash to Tidel park

Fixed time signal. (green time 45

sec. total cycle time 120 sec.)

Each 200m platoon distribution

was measured

Platoon size change due to

heterogeneous condition,

intersection distance, speed, size,

and lane change.

An android application has

been created to record the

volume and the instantaneous

time of individual vehicle

electronically.

Distance Time

interval

Average

travel

time(s)

β(unit

less)

α(unit

less)

Smoothing

factor (F)

Robertson

arrival rate

(q)

Actual

dispersion

200

15

18.44 0.887 0.113 0.878

24 27

30 19 18

45 23 24

400

15

31.27 0.9338 0.07 0.878

28 32

30 26 26

45 14 12

600

15

40.722 0.9491 0.053 0.878

23 26

30 28 29

45 24 23

800

15

53.29 0.9611 0.04 0.878

30 34

30 24 23

45 16 15

1000

15

64.23 0.9677 0.0333 0.878

28 31

30 23 25

45 18 16

1200

15

77.08 0.973 0.0276 0.878

26 29

30 18 19

45 27 25

1400

15

86.8 0.976 0.24 0.878

31 33

30 17 18

45 24 22

Robertson Platoon Dispersion

Models:-

Average speed = 46km/hr

Smoothing Factor F =.87

standard deviation σ=5.0

Robertson equation :-

𝑞𝑡𝑑 = 𝑓𝑛 ∗ 𝑞𝑡−𝑇 + 1 − 𝑓𝑛 ∗ 𝑞𝑡−𝑛

𝑑

𝑓𝑛 = 𝑛(𝑛2+ 4𝜎2) − 𝑛

2𝜎2

𝛽𝑛 =2𝑇𝑎 + 𝑛 − √(𝑛2 + 4𝜎2)

2𝑇𝑎

𝛼𝑛 =1 − 𝛽𝑛𝛽𝑛

Platoon Dispersion From IT Corridor

24 27

19 18

23 24

28 32

26 26

14 12

23 26

28 29

24 23

30 34

24 23

16 15

28 31

23 25

18 16

26 29

18 19

27 25

31 33

17 18

24 22

MOUNT POONAMALLE

HIGH ROAD ( SH 55)

SL.

No Name of the intersection

Distance

(metres)

1 Miot signal to Ramapuram Signal 300

2

Ramapuram Signal to L&T

Signal 521

3

L&T Signal to Mugalivakkam

Signal 1400

4

Mugalivakkam Signal to TVS

Motors Intersection 1220

5

TVS Motors Intersection to

Porur Signal 711

The traffic composition in the each arm was calculated and given below,

S/NO Vehicle Direction Car% Bus% Two wheeler% HCV% Others%

1 Miot to Ramapuram signal 37 3 57 2 14

2 Manapakkam road to Ramapuram signal 38 1 54 1 6

3 Sathyanagar main road to Ramapuram signal 34 2 50 2 12

4 L&T office building to Mount Poonamalle high

road 64 2 34 0 0

5 Mugalivakkam main road to MountPoonamalle

road 30 2 53 2 13

6 Vanniyar street road to Mount Poonamalle road 28 0 64 0 8

7 Ramakrishna street road to Mount Poolanamalle

road 25 0 63 1 11

8 Sriperumbudur road to Mount Poonamalle high

road 28 3 51 3 15

9 Kodambakkam road to Mount Poonamalle high

road 30 3 54 3 14

To model the behavior of existing traffic

stream, spot speed survey was conducted

during peak hour.

From the spot speed survey, the mean speed

and 85th percentile speed for each category

of vehicles were determined.

Transyt software has been used for the traffic signal coordination and

efficiently used green time utilization

The routing decisions are given in the form of O-D Matrix. For each arm the

origin and destination was calculated and given.

Cycle Time

Optimizer

Cycle

Time (S)

Total

Network

Delay

(PCU/Hr)

Highest

Dos (%)

Link With

Highest

Dos

Average

Speed Kph

Number Of

Oversaturated Links

Percenta

ge Of

Oversat

urated

Links

(%)

Mean

Delay

Per PCU

(S)

Exist 180 2444.69 421 35 3.67 21 43 658.04

Offset 180 2345.5 418 35 3.46 19 41 654.87

Offset And

Green Split 180 2235.34 383 31 4.21 16 36 613.78

On comparison of existing condition with offset green split

implemented condition, mean delay was reduced by 45s , average speed was

increased by 0.54 kph and oversaturated links was reduced by 5.

PORUR SIGNAL

MUGALIVAKKAM SIGNAL

• L&T SIGNAL

Ramapuram Signal

Road network drawing

Classification vehicle and types

Speed distribution

Lane change and overtaken distance

Lateral and longitudinal distance

between the each vehicle

Routing decisions

Traffic signal

Detectors use for actuated signal.

S/No VISSIM Parameters Default Value

Calibrated

Values

1 Average standstill distance 1.5 1

2 Additive part of safety distance 1.5 0.6

3 Multiple part of safety distance 2 1.1

4 Look head distance (min-max)m 0-250 0-150

5 Look back distance (min-max)m 0-150 0-100

6 Minimum lateral standing distance(m) car 1 0.3

7 Minimum lateral driving distance(m) car 1 0.4

8 Minimum lateral standing distance(m) bike 1 0.2

9 Minimum lateral driving distance(m) bike 1 0.4

10 Minimum lateral standing distance(m) bus 1 0.3

11 Minimum lateral driving distance(m) bus 1 0.4

12 Minimum lateral standing distance(m) HCV 1 0.3

13 Minimum lateral driving distance(m) HCV 1 0.4

The routing decisions are given in the form of O-D Matrix. For each arm the

origin and destination was calculated and given.

Three scenarios were formulated and compared to the existing scenario with

respect to average delay at intersection and number of vehicle along the

study corridor. The scenarios are

1. Existing scenario with fixed time signal (Scenario 1)

2. Fixed time signal with coordination (Scenario 2)

3. Vehicle Actuated with coordination (Scenario 3)

Vehicle

Class

No of

Vehicles

Avg

Speed

(km/h)

Per Vehicle

Avg

Delay

(s)

Avg No

of Stops

Avg

Stop

Delay

(s)

Car 4925 8.34 934.2 98 567

Bus 648 7.78 912 70 487

Bike 6998 8.76 954.7 96 587

HCV 388 7.69 889.45 66 445

Total 12961 8.14 922.59 82.50 521.50

s/n

o

Intersection

Name

Cycle Time

(sec)

Delay

(sec) LOS

1

Ramapuram

Signal 180 124 F

2 L&T Signal 90 67 E

3

Mugalivakkam

Signal 90 98 F

4

TVS Motors

Intersection 180 125 F

5 Porur Signal 180 136 F

6 Total 550

S/No Vehicle

Class

No of

Vehicles

Avg

Speed

(km/h)

Per Vehicle

Avg

Delay

(s)

Avg

No of

Stops

Avg

Stop

Delay

(s)

1 Car 4998 9.23 883.21 85 538

2 Bus 678 8.81 847.8 66 465

3 Bike 7067 9.67 902.5 87 538

4 HCV 412 8.58 829.02 63 425

5 Total 13155 9.07 865.63 75.25 491.50

s/no Intersection

Name

Offs

et

(sec)

Delay

(sec) LOS

1

Ramapuram

Signal 28 115 F

2 L&T Signal 47 62 E

3

Mugalivakka

m Signal 125 91 F

4

Tvs Motors

Intersection 109 103 F

5 Porur Signal 64 126 F

6 Total 497

S/No Vehicle

Class

No of

Vehicles

Avg

Speed

(km/h

)

Per Vehicle

Avg

Delay

(s)

Avg

No of

Stops

Avg

Stop

Delay

(s)

1 Car 4941 9.94 812.3 82 495

2 Bus 657 9.23 779.3 53 424

3 Bike 7044 10.23 813.54 82 476

4 HCV 407 9.18 773.56 44 375

5 Total 13049 9.73 834.56 65 435

s/no Intersection Name Delay (sec) LOS

1 Ramapuram Signal 79 E

2 L&T Signal 43 D

3 Mugalivakkam Signal 71 E

4 TVS Motors Intersection 74 E

5 Porur Signal 103 F

6 Total 376

S/NO Average Delay (s) Average No of

stops

Average Stop

Delay (s)

Scenario 1 922.59 82.5 521.5

Scenario 2 865.63 75.25 491.5

Scenario 3 795.56 65 435

Intersection Name Scenario 1 Scenario 2 Scenario 3

Ramapuram Signal 124 115 79

L&t Signal 67 62 43

Mugalivakkam Signal 98 91 71

Tvs Motors Intersection 125 103 74

Porur Signal 136 126 103

Variation of Delay for Scenarios

Network Capacity for Scenarios

S/NO No of Vehicles

Scenario 1 12961

Scenario 2 13155

Scenario 4 13049

The platoon dispersion derived from the data collected is matching closely with the

Robertson’s model at a standard deviation in the range of 5 to 6.

Three scenarios were formulated and were compared. Scenario-1: Fixed time signal,

Scenario-2: co-ordination of fixed time signal, Scenario-3: co-ordination of vehicle

actuated signal. On comparing of scenario 2 with scenario 1, reduction in average

delay per vehicle was about 9%. The individual intersection delay for scenario 2 was

from 7% to 11%, reduced when compared with scenario 1.

Now after analyzing all Scenarios, we can see that these methods have shown good

results for vehicle travelling along the corridor in terms of improvement in the LOS

(Level of Service).

On comparing of scenario 3 with scenario 1, reduction in average delay per vehicle

was found to be about 18%. The individual intersection delay for scenario 3 was

improved in the range of 13% to 20% when compare with scenario 1.

By adjusting the signal phase of the entry junction in scenario 3 total delay of the

junction improved in the range of 13 % to 18 %, when compare to the scenario 1.

Analysis of all the proposed scenarios helps us to conclude that all of these

methods are found to be efficient the reducing the delay for oversaturated

traffic flow at junction.

Vehicle type, driver behavior, and lane capacity are the factors which affect

the platoon dispersion and it has been compared to the Robertson’s model, it

has been observed that there are only slight changes in the actual dispersion,

because it is heterogeneous traffic flow.

The concept of providing Vehicle Actuated signal for oversaturated

conditions is proposed based on the study.

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