This document provides an overview of signalized intersection analysis and optimization for a transportation engineering course. It defines key terms related to signal timing, describes methods for calculating vehicle delay under uniform and random traffic arrivals, and approaches for optimizing cycle length, green time allocation, and level of service. Examples are provided to illustrate calculations for critical lane group volume-to-capacity ratio, total lost time, optimal signal timing, green time distribution, and intersection level of service.
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Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
traffic volume studies pdf
traffic studies pdf
types of traffic engineering studies
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traffic volume study
traffic impact studies
types of traffic studies
traffic safety studies
average daily traffic calculation
traffic volume formula
how to calculate adt traffic
calculating adt from peak hour
traffic volume growth factor formula
traffic growth rate calculator
aadt to peak hour volume
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Detailed description of Capacity and Level of service of Multi lane highways based on Highway Capacity Manual (HCM2010) along with one example for finding LOS of a highway
Capacity & Level of Service: Highways & Signalized Intersections (Indo-HCM)Vijai Krishnan V
This presentation gives a glimpse on estimating the capacity and Level of Service (LOS) of highway midblock sections and signalized intersections under heterogeneous traffic conditions using the Indo-HCM 2017 Manual. It also compares the Indo-HCM LOS estimation methods with US-HCM. Some practice questions are also included.
I acknowledge the co-author Ms. Sethulakshmi G (Ph. D. Scholar, NIT Surathkal) for her valuable contribution to this presentation.
topics which are discussed in this slide are,
1) pavement and requirement for pavement design.
2) Rigid and flexible pavement .
3) pavement design method.
this is a brief introduction to various traffic engineering basic characteristics which are useful in designing any corridor or passage with safety & reliability.
Origin and Destination ( O-D) Study. defined all types very well with advantages and disadvantages. Introduction of OD, Objective of OD Study
Information required for OD
OD Survey Types
Methodology
Road Side Interview Method
License Plate Method
Tag on Car method
Home Interview method
postal method
online survey method
commercial and public vehilce method survey
OD MATRIX
Desire line diagram and Flow Line diagram
Conclusion and Reference.
Capacity & Level of Service: Highways & Signalized Intersections (Indo-HCM)Vijai Krishnan V
This presentation gives a glimpse on estimating the capacity and Level of Service (LOS) of highway midblock sections and signalized intersections under heterogeneous traffic conditions using the Indo-HCM 2017 Manual. It also compares the Indo-HCM LOS estimation methods with US-HCM. Some practice questions are also included.
I acknowledge the co-author Ms. Sethulakshmi G (Ph. D. Scholar, NIT Surathkal) for her valuable contribution to this presentation.
topics which are discussed in this slide are,
1) pavement and requirement for pavement design.
2) Rigid and flexible pavement .
3) pavement design method.
this is a brief introduction to various traffic engineering basic characteristics which are useful in designing any corridor or passage with safety & reliability.
Origin and Destination ( O-D) Study. defined all types very well with advantages and disadvantages. Introduction of OD, Objective of OD Study
Information required for OD
OD Survey Types
Methodology
Road Side Interview Method
License Plate Method
Tag on Car method
Home Interview method
postal method
online survey method
commercial and public vehilce method survey
OD MATRIX
Desire line diagram and Flow Line diagram
Conclusion and Reference.
Establishment of any new development generates additional trips that may have negative effects on the existing traffic network. To assess the impact of the development traffic on the transport network and to identify reasonable solutions Traffic Impact Assessment (TIA) is performed. In the absence of sophisticated travel demand model TIA is carried out manually. In manual process, total trip generated from the development site is estimated by multiplying the trip rate with the development size. In this process the task can be completed very easily, but the accuracy of the calculation depends on the reliability of the trip rates used in the calculation. As Bangladesh does not have standard trip rates, TIA is generally performed by using trip rates obtained from the Institute of Transportation Engineers’ “Trip Generation” report, which is ideal for Western countries. In this research, trip rates of shopping centers in Dhaka city are estimated.This study conducts only for trip attraction rates of shopping centers having different sizes and located at different places (Dhanmondi, Gulshan and Siddheswari) in Dhaka city. In order to do so number of persons and vehicles entering the shopping centers in every fifteen minutes interval during peak periods are counted then converted for an hour. From the survey data, it is found that the trip attraction rates of small shopping centers are much higher than that of medium size shopping centers. Macroscopic model is also developed from the survey data. The macroscopic modelrelates the attraction rates of the shopping centers as a function of the physical features such as gross floor area, number of car parking, number of shops and availability of restaurants(available or not) in the shopping centers.
good communication system is very for the following purposes:
1-Synchronization of controller timer at each intersection for offset implementation.
2-Exchange of traffic data between controllers.
3-Malfunction reporting from each controller to the control room.
4-Incident reporting to the control room.
Dr Glyn Rhys-Tyler - Road vehicle exhaust emissions; 'an age of uncertainty' ...IES / IAQM
DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
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It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
3. CEE320
Winter2006
Key Definitions (1)
• Cycle Length (C)
– The total time for a signal to complete a cycle
• Phase
– The part of the signal cycle allocated to any combination
of traffic movements receiving the ROW simultaneously
during one or more intervals
• Green Time (G)
– The duration of the green indication of a given
movement at a signalized intersection
• Red Time (R)
– The period in the signal cycle during which, for a given
phase or lane group, the signal is red
4. CEE320
Winter2006
Key Definitions (2)
• Change Interval (Y)
– Yellow time
– The period in the signal cycle during which, for a given
phase or lane group, the signal is yellow
• Clearance Interval (AR)
– All red time
– The period in the signal cycle during which all
approaches have a red indication
5. CEE320
Winter2006
Key Definitions (3)
• Start-up Lost Time (l1)
– Time used by the first few vehicles in a queue while reacting
to the initiation of the green phase and accelerating.
2 seconds is typical.
• Clearance Lost Time (l2)
– Time between signal phases during which an intersection is
not used by traffic. 2 seconds is typical.
• Lost Time (tL)
– Time when an intersection is not effectively used by any
approach. 4 seconds is typical.
– tL = l1 + l2
• Total Lost Time (L)
– Total lost time per cycle during which the intersection is not
used by any movement.
6. CEE320
Winter2006
Key Definitions (4)
• Effective Green Time (g)
– Time actually available for movement
– g = G + Y + AR – tL
• Extension of Effective Green Time (e)
– The amount of the change and clearance interval at the
end of a phase that is usable for movement of vehicles
• Effective Red Time (r)
– Time during which a movement is effectively not
permitted to move.
– r = R + tL
– r = C – g
7. CEE320
Winter2006
Key Definitions (5)
• Saturation Flow Rate (s)
– Maximum flow that could pass through an intersection if
100% green time was allocated to that movement.
– s = 3600/h
• Approach Capacity (c)
– Saturation flow times the proportion of effective green
– c = s × g/C
• Peak Hour Factor (PHF)
– The hourly volume during the maximum-volume hour of
the day divided by the peak 15-minute flow rate within
the peak hour; a measure of traffic demand fluctuation
within the peak hour.
8. CEE320
Winter2006
Key Definitions (6)
• Flow Ratio
– The ratio of actual flow rate (v) to saturation flow rate (s)
for a lane group at an intersection
• Lane Group
– A set of lanes established at an intersection approach
for separate analysis
• Critical Lane Group
– The lane group that has the highest flow ratio (v/s) for a
given signal phase
• Critical Volume-to-Capacity Ratio (Xc)
– The proportion of available intersection capacity used
by vehicles in critical lane groups
– In terms of v/c and NOT v/s
11. CEE320
Winter2006
Quantifying Control Delay
• Two approaches
– Deterministic (uniform) arrivals (Use D/D/1)
– Probabilistic (random) arrivals (Use empirical equations)
• Total delay can be expressed as
– Total delay in an hour (vehicle-hours, person-hours)
– Average delay per vehicle (seconds per vehicle)
12. CEE320
Winter2006
D/D/1 Signal Analysis (Graphical)
Arrival
Rate
Departure
Rate
Time
Vehicles
Maximum delay
Maximum queue
Total vehicle delay per cycle
Red Red RedGreen Green Green
Queue dissipation
13. CEE320
Winter2006
D/D/1 Signal Analysis – Numerical
• Time to queue dissipation after the start of effective green
• Proportion of the cycle with a queue
• Proportion of vehicles stopped
µ
λ
ρ = 0.1<ρ
( )ρ
ρ
−
=
1
0
r
t
c
tr
Pq
0+
=
( )
( ) qs P
c
tr
gr
tr
P =
+
=
+
+
= 00
λ
λ ( )
( ) c
t
c
t
gr
tr
Ps
ρλ
µ
λ
λ 000
==
+
+
=
14. CEE320
Winter2006
D/D/1 Signal Analysis – Numerical
• Maximum number of vehicles in a queue
• Total delay per cycle
• Average vehicle delay per cycle
• Maximum delay of any vehicle (assume FIFO)
µ
λ
ρ = 0.1<ρ
rQm λ=
( )ρ
λ
−
=
12
2
r
Dt
( ) ( )ρλρ
λ
−
=×
−
=
12
1
12
22
c
r
c
r
Dt
rdm =
15. CEE320
Winter2006
Signal Analysis – Random Arrivals
• Webster’s Formula (1958) - empirical
d’ = avg. veh. delay assuming random arrivals
d = avg. veh. delay assuming uniform arrivals (D/D/1)
x = ratio of arrivals to departures (λc/µg)
g = effective green time (sec)
c = cycle length (sec)
( )
)/(52
3/1
2
2
65.0
12
' cg
x
c
x
x
dd +
−
−
+=
λλ
16. CEE320
Winter2006
Signal Analysis – Random Arrivals
• Allsop’s Formula (1972) - empirical
d’ = avg. veh delay assuming random arrivals
d = avg. veh delay assuming uniform arrivals
(D/D/1)
x = ratio of arrivals to departures (λc/µg)
( )
−
+=
x
x
dd
1210
9
'
2
λ
17. CEE320
Winter2006
Definition – Level of Service (LOS)
• Chief measure of “quality of service”
– Describes operational conditions within a traffic
stream
– Does not include safety
– Different measures for different facilities
• Six levels of service (A through F)
19. CEE320
Winter2006
Typical Approach
• Split control delay into three parts
– Part 1: Delay calculated assuming uniform arrivals (d1).
This is essentially a D/D/1 analysis.
– Part 2: Delay due to random arrivals (d2)
– Part 3: Delay due to initial queue at start of analysis time
period (d3). Often assumed zero.
( ) 321 ddPFdd ++=
d = Average signal delay per vehicle in s/veh
PF = progression adjustment factor
d1, d2, d3 = as defined above
20. CEE320
Winter2006
Uniform Delay (d1)
( )
−
−
=
C
g
X
C
g
C
d
,1min1
15.0
1
d1 = delay due to uniform arrivals (s/veh)
C = cycle length (seconds)
g = effective green time for lane group (seconds)
X = v/c ratio for lane group
21. CEE320
Winter2006
Incremental Delay (d2)
( ) ( )
+−+−=
cT
kIX
XXTd
8
11900
2
2
d2 = delay due to random arrivals (s/veh)
T = duration of analysis period (hours). If the analysis is based on the
peak 15-min. flow then T = 0.25 hrs.
k = delay adjustment factor that is dependent on signal controller mode.
For pretimed intersections k = 0.5. For more efficient intersections k <
0.5.
I = upstream filtering/metering adjustment factor. Adjusts for the effect of
an upstream signal on the randomness of the arrival pattern. I = 1.0
for completely random. I < 1.0 for reduced variance.
c = lane group capacity (veh/hr)
X = v/c ratio for lane group
22. CEE320
Winter2006
Initial Queue Delay (d3)
• Applied in cases where X > 1.0 for the
analysis period
– Vehicles arriving during the analysis period
will experience an additional delay because
there is already an existing queue
• When no initial queue…
– d3 = 0
23. CEE320
Winter2006
Control Optimization
• Conflicting Operational Objectives
– minimize vehicle delay
– minimize vehicle stops
– minimize lost time
– major vs. minor service (progression)
– pedestrian service
– reduce accidents/severity
– reduce fuel consumption
– Air pollution
24. CEE320
Winter2006
The “Art” of Signal Optimization
• Long Cycle Length
– High capacity (reduced lost time)
– High delay on movements that are not served
– Pedestrian movements? Number of Phases?
• Short Cycle Length
– Reduced capacity (increased lost time)
– Reduced delay for any given movement
25. CEE320
Winter2006
Minimum Cycle Length
∑=
−
×
= n
i ci
c
c
s
v
X
XL
C
1
min
Cmin = estimated minimum cycle length (seconds)
L = total lost time per cycle (seconds), 4 seconds per
phase is typical
(v/s)ci = flow ratio for critical lane group, i (seconds)
Xc = critical v/c ratio for the intersection
26. CEE320
Winter2006
Optimum Cycle Length Estimation
( )
∑=
−
+
= n
i ci
opt
s
v
L
C
1
1
55.1
Copt = estimated optimum cycle length (seconds) to
minimize vehicle delay
L = total lost time per cycle (seconds), 4 seconds per
phase is typical
(v/s)ci = flow ratio for critical lane group, i (seconds)
28. CEE320
Winter2006
Pedestrian Crossing Time
ft.10for7.22.3 >
++= E
E
ped
p
p W
W
N
S
L
G
( ) ft.10for27.02.3 ≤++= Eped
p
p WN
S
L
G
Gp = minimum green time required for pedestrians (seconds)
L = crosswalk length (ft)
Sp = average pedestrian speed (ft/s) – often assumed 4 ft/s
WE = effective crosswalk width (ft)
3.2 = pedestrian startup time (seconds)
Nped = number of pedestrians crossing during an interval
30. CEE320
Winter2006
Example
An intersection operates using a
simple 3-phase design as
pictured.
NB
SB
EB
WB
Phase Lane
group
Saturation Flows
1 SB 3400 veh/hr
2 NB 3400 veh/hr
3 EB 1400 veh/hr
WB 1400 veh/hr
33. CEE320
Winter2006
Example
Determine the green times allocation using v/c equalization.
Assume the extension of effective green time = 2 seconds and
startup lost time = 2 seconds.
=
ii
i
X
C
s
v
g
LC
C
s
v
X
n
i i
c
−
×
=
∑=1
34. CEE320
Winter2006
Example
What is the intersection Level of Service (LOS)? Assume in all
cases that PF = 1.0, k = 0.5 (pretimed intersection), I = 1.0 (no
upstream signal effects).
∑
∑
=
i
i
i
ii
A
v
vd
d
∑
∑
=
k
k
k
kk
I
v
vd
d
35. CEE320
Winter2006
Example
Is this signal adequate for pedestrians? A pedestrian count
showed 5 pedestrians crossing the EB and WB lanes on each side
of the intersection and 10 pedestrians crossing the NB and SB
crosswalks on each side of the intersection. Lanes are 12 ft. wide.
The effective crosswalk widths are all 10 ft.
( ) ft10for27.02.3 ≤++= Eped
p
p WN
S
L
G
36. CEE320
Winter2006
Signal Installation: “Warrants”
• Manual of Uniform Traffic Control
Devices (MUTCD)
• Apply these rules to determine if a
signal is “warranted” at an
intersection
• If warrants are met, doesn’t mean
signals or control is mandatory
• 8 major warrants
• Multiple warrants usually required
for recommending control
http://mutcd.fhwa.dot.gov/
FYI – NOT TESTABLE
38. CEE320
Winter2006
Primary References
• Mannering, F.L.; Kilareski, W.P. and Washburn, S.S. (2003).
Principles of Highway Engineering and Traffic Analysis, Third Edition
(Draft). Chapter 7
• Transportation Research Board. (2000). Highway Capacity Manual.
National Research Council, Washington, D.C.
Editor's Notes
When intersection v/c approaches 1.0 then an actuated controller will tend to behave like a pretimed one
If you truly want to minimize cycle length then set Xc = 1.0, which means that your critical v/c will be 1 and you can just squeeze all the vehicles through on that phase’s green time. However, due to the stochastic nature of arrivals, if you set Xc = 1 then there will be times when more arrivals than your assumed v will show up and the cycle will fail (not all vehicles will be let through on a particular green). Therefore, often values less than 1 are assumed for Xc (such as 0.90).
This is only one estimation
Values between 0.75Copt and 1.5 Copt give similar delay times
Assumes 15th percentile walking speed of pedestrians is 4 ft/s
PHASE 1
SB T/left/right= (400+150+30)/3400 = 0.171
PHASE 2
NB T/left/right = (1000+100+50)/3400 = 0.338
PHASE 3
EB T/right = (200+20)/1400 = 0.157
WB T/right = (300+30)/1400 = 0.236 limiting since v/s is highest
Yc = 0.171 + 0.338 + 0.236 = 0.745
Total lost time = 3(2+2) = 12 seconds
Copt = 1.5(12 seconds) + 5/(1-0.745) = 90.2 seconds = 95 seconds (rounded up to nearest 5 seconds)
DETERMINE Xc
Xc = 0.745(95)/(95 – 12) = 0.853
CALCULATE EFFECTIVE GREEN TIMES
gSB = 0.171(95/0.853) = 19.04 seconds
gNB = 0.338(95/0.853) = 37.64 seconds
gEBWB = 0.236 (95/0.853) = 26.28 seconds
CHECK
19.04 + 37.64 + 26.28 + 12 = 94.96 = 95 seconds
ACTUAL GREEN TIMES
In this case, they are the same as g
G = g+e-l1 = g +2 -2 = g
Determine the delay for each lane group
SB lane group
c = s (g/C) = 3200(19.04/95) = 641.35 vehicles
d1 = (0.5)(95)(1 – 19.04/95)/(1 – 0.853(19.04/95)) = 45.81 seconds
d2 = 900(0.25)((0.853-1) + sqrt((0.853 – 1)2 + 8(0.5)(1.0)(0.853)/((641.35)(0.25))) = 13.52 seconds
d3 = 0 (assumed)
d = 45.81 + 13.52 + 0 = 59.33
NB lane group
c = s (g/C) = 3200(37.64/95) = 1267.87 vehicles
d1 = (0.5)(95)(1 – 37.64/95)/(1 – 0.853(37.64/95)) = 47.50 seconds
d2 = 900(0.25)((0.853-1) + sqrt((0.853 – 1)2 + 8(0.5)(1.0)(0.853)/((1267.87)(0.25))) = 7.41 seconds
d3 = 0 (assumed)
d = 47.50 + 7.41 + 0 = 54.91
EB lane group
c = s (g/C) = 1400(26.28/95) = 387.28 vehicles
d1 = (0.5)(95)(1 – 26.28/95)/(1 – 0.853(26.28/95)) = 44.97 seconds
d2 = 900(0.25)((0.853-1) + sqrt((0.853 – 1)2 + 8(0.5)(1.0)(0.853)/((387.28)(0.25))) = 20.57 seconds
d3 = 0 (assumed)
d = 44.97 + 20.57 + 0 = 65.54
WB lane group
c = s (g/C) = 1400(26.28/95) = 387.28 vehicles
d1 = (0.5)(95)(1 – 26.28/95)/(1 – 0.853(26.28/95)) = 44.97 seconds
d2 = 900(0.25)((0.853-1) + sqrt((0.853 – 1)2 + 8(0.5)(1.0)(0.853)/((387.28)(0.25))) = 20.57 seconds
d3 = 0 (assumed)
d = 44.97 + 20.57 + 0 = 65.54
Find the weighted average of delay for the four lane groups
dI = ((59.33)(580) + 54.91(1150) + 65.54(220) + 65.54(330))/(580 + 1150 + 220 + 330) = 58.60 seconds
From Table 7.4 this equates to LOS E (not very good)
EB/WB
Gp = 3.2 + 24/4 + 0.27(5) = 10.55 seconds
NB/SB
Gp = 3.2 + 48/4 + 0.27(10) = 17.90 seconds
OK