2. 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.
3. 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.,).
4. 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.
5. 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.
7. 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
8. 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.
9. 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
10. 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.
11. 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.
13. 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%
14. 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%
15. 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:
16. 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.
17. 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.
18. 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).
19. 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.
20.
21. 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.
22. 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.
23. An android application has
been created to record the
volume and the instantaneous
time of individual vehicle
electronically.
26. 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
27.
28.
29. 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
30. 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.
31. Transyt software has been used for the traffic signal coordination and
efficiently used green time utilization
32. The routing decisions are given in the form of O-D Matrix. For each arm the
origin and destination was calculated and given.
33. 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.
37. 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.
38. 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
39. The routing decisions are given in the form of O-D Matrix. For each arm the
origin and destination was calculated and given.
40. 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)
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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.
47. 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.
