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OCR for page 35
35
Chapter Four
FIELD DATA COLLECTION
One of the major objectives of this study is to assemble a
comprehensive database of traffic operations at stop-
controlled intersections. A major investment of resources
was made to collect data at a wide variety of intersections.
This chapter describes the data collection process and Me
database that were collected during this study. It includes
a summary of the data requirement by venous models,
sampling plan, a summary of We site characteristics, Me
data reduction process, critical gap and foDow-up time
estimation, capacity and delay estimation from Me field.
DATA REQUIREMENTS FOR RECOMMENDED
MODELS
The data requirements for each of Me capacity and delay
models are summarized In Table 4.
SAMPLING PLAN
Site and geometric considerations were established as Me
basis for the sampling plan used to guide the selection of
candidate sites. Since capacit,~measurements were highly
desirable, the first criterion was Mat continuous queueing
existing on at least one stop-con~oDed approach for at
feast five minutes on a normal day. Second, areas In each
of five geographic regions of the United States were
i ~ e n ~ fi e ~ : S a n F r a n c i s c o / S a n ~ o s e , C a ~ i f o r n ~ a m e ~ o p o ~ i t a n
area (southwest sector), Montgomery and Auburn,
Alabama (southeast sectors, Troy and Rochester, New
York (northeast sector), Milwaukee, Wisconsin (central
. . ..
sector), and Portland, Oregon (northwest sectors. A
guideline of fourteen TWSC intersection sites In each
geographic sector was established.
within each geographic sector, guidelines were established
for numbers of sites wad three different geometric
configurations. These are shown In Table 5. Other site
criteria that were used in screening of potential sites are
listed below.
Presence or absence of upstream traffic signals.
Upstream traffic signals produce platooned flow
that affects the capacity of a stop-controDed
intersection. The study sample included some
sites Mat were affected within I/2 mile of an
upstream signal, so that the effects of platooning
Table 4. Data Definition
Intersection Geometry Data
·Nllmber of lanes per approach
Diane designations
·Grades
· Sight distance for non-pnor~ty vehicles
Traffic Flow Data
·Traffic flow rates for all movements
·Tr~ic stream composition for all movements
·Proportion of non- platooned vehicles for each priority stream
movement
· Capacity for each non-prior~ty movement
·Length of peak period
Gap "d Headway Data
uncritical gap for each non-prior~ty stream
·Follow-up gap for each non-priority stream
·Minimum headway for platooned priority stream
Raw Data Required for Model Estimation
For Each Vehicle
·Event time at conflict point
·Movement direction
·Vehicle type
·Lane used
Additional Data for Each Non-Priority Vehicle
·Event time at back of queue
·Event time et front of queue
·Event time departing queue
Computed or Derived Data Required for Model Estimation
For Each Traffic Stream
·Flow rates
·Stream composition
For Each Non-Priority Stream Vehicle
·Gap event history while et front of queue
· Measured or estimated capacity
· Service time or delay
·Queue delay
aTota1 stopped delay
For inch Priority Sbeam Vehicle
·Propofion offree vehicles
·Minimum headway of platooned vehicles
.
on capacity could be quantified.
Major street speed. The average vehicle speeds
on die major street may or may not affect the gap
acceptance behavior of minor street vehicles. The
study sample included some sites with major
street posted speed limits of at least 45 mi/hr.
OCR for page 36
Configuration #1
Geometry
· Lanes on major street = 1
Lanes on minor Swat = 1
Optional major street exclusive left-stun lane
Planned Number of Sites
· Urban area = 3
Rural area= 1
Total sites = 4
Configuration It2
Geometry
· Lanes on major sheet = 2
Lanes on minor street = 1
Optional major street exclusive left-sum lane
Planned Number of Sites
· Urban areas = 6
Rural areas = 1
Total sites = 7
36
.
.
One-way streets. One-way major streets may
affect gap acceptance behavior differently than
two-way major sweets.
Major street median'. A median storage area or a
two-way lefc-turn (TWLT) lane on the major
street may result In a two-stage gap acceptance
process for the minor street driver. This means
that Me minor street vehicle crossing or boning
left performs Me movement In two stages, using
the median refuge to store in between the two
stages.
Table 5. Sampling Plan for Two-Way Stop Controlled (IWSC) Intersection Sites
.
Single lanes with no queue by-pass. Many stop-
controlled intersections provide an opportunity for
drivers on major street through movement to by-
pass amajor street left turn queue on the right by
using a paved or unpaved shoulder. This
opportunity increases the capacity of the approach
without the addition of a complete lane. The
study sample included some sites that allow for
this queue by-pass and some sites that precluded
it.
Configuration #3
Geometry
Lanes on major sheet = 2
Lanes on minor street = 2
Optional major street exclusive lef`-tuin lane
Planned Number of Sites
· Urban axe" = 3
· Rural areas = 0
· Total sites = 3
A total of 68 unique sites were videotaped during 79
videotaping time periods. Overall, the sample size
objective of 70 TWSC intersections was met.
Tables 6 and 7 show the number of sites that were
1 1~
,. ~
All
videotaped by sector and by geometric configuration for
TWSC intersections. The sampling plan requirements are
also shown. In most cases, Me requirements were either
met or exceeded.
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37
Table 6. Number of Unique TWSC Intersection Sites
1
, .......... ............ .......... ............ ............... ................ ................
................... ... ... ............. ........ ,., ............... .. ..... .....
....... , ..,~. ,. , ~" ' . ~:"'' ' "'a' ' ' " . ~"" . ~ ..
1 5 4 4
2 4 5 6
3 6 3 3
15 12 13 .
4
6
3
13
6 4 23 20
5 7 26 35
4 3 19 15
15 14 68 70
Table 7. Number of Videotaping Periods-TWSC Idtersechon Sites
................................................ . ,., -
............................... ~., :' :
........ - ...... - ,.,., ,. .... ................. ,.,.,. _ , , ,.,, ...............
., ,,,, . ~. ~., ~. ~.. ~.......... ~
.. .. .. . . . .. .. ....... .... ... ..
7 4 5 5 7 4 28 20
5 5 9 6 5 7 30 35
7 3 3 4 4 3 21 15
19 12 17 14 14 14 79 70
...................
.~ . .
............
Tables g and 9 show the number of sites categonzed by
both geometric configuration and poster! major street
speed There is nearly an equal split In Me number of sites
avid major street speeds less Wan 40 mph and greater Man
40 mph.
Table 8. TWSC Intersection Unique Sites: Geometric Configuration
by Major Street Speed
I. ;
. ~.~.~
2
3
Total
10
10
6
26
8
9
6
23
1 1 111 11111...11111
·:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:
......................
2 .: . ~P ~.S.:
......................
·
r-
Table 9. TWSC Intersection Videtaping Periods: Geometric
Configuration by Major Street Speed
-
2
Total
4
2
1
7
14
11
7
3'
8
11
6
i, ~ , , _ , ~ , 6 1
Tables 10 and ~ ~ show the breakdown of Me number of
sites by geometric configuration and the distance to
upstream signal control on the major street. Since the
effects of vehicle progression on capacity are significant,
it is important to have a mix of sites with varying distances
to upstream signal conuo} and Bus varying degrees of
progression and/or platooning.
Table 10. TWSC Intersection Unique Sites: Geometric
Configuration by Upstream Signal Control
............................................... ..... .... ............. ..........
~,.~.~0 ,~,.~F it'
. .
2
3
Total
12
27
13
6
24
10
17
Table 11. TWSC Intersection Videotaping Periods: Geometric
Configuration by Upstream Signal Control
2
3
Total
10
9
14
33
;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.;.,
.,
.. .. ~
......... - · ''''' 1
s
14
6
25
21
OCR for page 38
SITE CHARACTERISTICS
This section contains a summary of the traffic data
for Me TWSC intersection sites, see Tables 12
through 16. Please also refer to Working Paper 15
(NCHRP 3-46, 1995) for a complete reference of
We physical characteristics of We intersection sites.
The data is presented In terms of minimum,
Table 12. TWSC Sites Traffic Characteristics SummarY-Sou~west Sector
SWTOO1
SWT002
SWT003
SWT004
SWTOOS
SWI006
SU'I007
SWTo08
SWT009
SWTO10
SWTO11
SUlT012
SWI013
SWI014
SWT015
SWT016
SWT017
swro~a
SWIO19
maximum and average values for 5-minute data
points. It is instructive to note the w~de range of
flow rates, queue percentage, and delays ~at were
exhibited in the field data for each site even dunag
one videotape penod of approximately I.5 hours.
Practitioners end researchers should be cogn~zant of
this vanabilit~r at any particular site dunug a g~ven
peak penod when interpreting the accuracy of
modeled results against field data.
1 | SB
3 T SB
T
2 | EB
| WB
2 T B
1 1 EB
3 1 SB
3 l EB
WB(Lanel)
.
WB(Lanc2)
.
1 NB
2 NB
SB
.
1 WB
3 WB
3 EB
WB0~l)
WB(~2)
. EB .
3 NB(La~cl)
NB(L9~2)
3 NB~1)
NBC1~2)
~ - 24 ~SB
-~ 1 NB
1 NB
_ 2 SB
01:48
01:S8
O1S4
01:48
01:S8
01 39
01:46
.
.
01 S6
01:48
01 S3
00:40
01:4S
01 S2
01:46
01-22
01:41
01 Sl
O1S9
01:47
.
1812 10321421
22S6 128418S7
= =
102O ~2372739
= =
1944 11761S27
- 372776
1836 11281412
864 348S42
~ -~- ~ ~
=
1380 S28938
17S2 82812S7
=
10DO 624824
1584 996ll79
1716 7801310
= .
1668 12~1~6
3432 17282461
2124 9361S08
= .
02S24 684~ 1689
104 624819
1248 492919
2340 14881879 _
180
216
408
168
48
276
1ao
408
84
36
372
S64
132
1S6
492
288
1S6
96
132
192
S64
600
S28
360
168
492
648
264
48
60
168
o
o
48
~2
24
12
o
24
228
12
24
240
96
24
o
o
48
300
1S6
1S6
48
12
288
276
. 60 .
121
142
299
4S
13
113
46
166
41
16
108
379
42
79
340
177
89
34
37
104 1
1
434
338
339
1S2
68 1
392
438
147
2.8
12.2
1
2S
1.2
o.7
o.7
13.4
1
1.3
2.9
8.4
4.8 1
1
3
8.9
28.8
S.2
9.3
3.1
23
11.6
2~7
3.1
12.8
313
13
12.8
S3
10.1
1S.8
4.3
48.2
1
6
9.7
18.6
8.6 1
1
10.7
12S 1
1
6.7
28.2
72.2
1
9.9
36.8
6.2
13.2
34.9 1
1
~.1 1
13.1
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39
Table 13. TWSC Sites Traffic Characteristics-Southeast Sector
B~:1)
B(~2)
WB(~1)
WB(~2)
B
WB
SBCl~I)
SB~2)
SIB amp)
NBO~l)
NBC[~nc2)
NB0-anc3)
WB
EB(I~ncl)
NB
SB
WB(19~1)
WBO~anc2)
Bowl)
EBC~nc2)
EB
WB
WB
WB(~1)
WB(1~2)
EB
.
S4.2
21.2
S4
11.?
30.1
31.4
33
36
18.9
39.S
20.3
9
44.8
6.6
21.2
43.2
41.8
273
99.3
68
19.6
18.7
47.6
115
16.7
_
3.3 26.8
l 8
2.4 183
_ 5.6
2.4 11.2
2.5 12
3.8 12.2
3.2 12.1
0 4.1
3.3 13.4
4.4 9.4
O 1.1
0 5.4
2.2 S.2
33 11.3
3.7 12.6
3.9 23.2
3.1 8.9
1S 23.S
.9 16.8
3 10.7
4.1 9.5
3.8 15.6
0 5.7
4.9 12.1
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40
Table 14. TVJSC Sites Traffic Characteristics Summar~,r-Nor~east Sector
NEIZO1
NET202
NE=03
NE=Z04
NEIZ05
NElz06
NE=Z07
NETZ08
NETZO9
NET210
NET211
NEl-212
NEl213
NUlZ14
NE=zl5
NET216
N~17
2 1 SB 1 01 So
2 1 WB(L9nel) 1 01:44
1 WBC~2) 1
2 WB(19r~l) 01:04
WB(1~2)
2 SB Ol:S9
2 SB 0151
SB(~1) 01 OS
SBC - e2)
1 SB Ol:SO
2 NB 01 :S1
1 SB 01:41
2 SB - - 01 ·SO
2 EB 01:47
3 _ EBCl~l) 01 SS
EBC1~2)
1 EB 01:26
WB ~
2 SB 01.47
3 ~NB0~l) ~ 01:47
NB(I ~2) ~
1 EB 01:41
1 ~EB 01 :S3
4020
828
684
4368
2484
1908
1896
3984
972
21 12
1128
1740
1044
18Q0
1908
408
480
21.1
ll S
4
65
2.8
21.1
613
125
12.2
17
18.1
18.8
12.6
8.8
19.6
S.2
lO.S
14.4
16.1
12
7.8
8.4
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41
Table 15. TWSC Sites Traffic Characteristics Summary-Cenhal Sector
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42
Table 16. TWSC Sites Traffic Characteristics Summarv-Nor~west Recta,
NWT401
Nurr402
Nwr403
NWI404
NWT405
Nwr406
Nwr407
Nwr408
NWT410
NWT411
NWT412
NWT413
NW r414
Nwr415
Nurr416
WB
EB
NB
NB
SB
NB
NB
SB
WB(I~cl)
WB(Lsuc2)
EB
NB
SB
EB(~1)
EBO~nc2)
WB
WB
WB
EB
WB
EB
NB(~el)
NBO Am)
SB
DATA REDUCTION PROCESS
Four video cameras were used to record traffic
operations at each of the TWSC intersections. Data
were reduced from the videotapes using the Traffic
Data input Program (TDIP, Boesen, Rin~esbacher,
and Kyte, 1991~. TDIP is a computer program Mat
records time events by observing a videotape and
pressing a corresponding key on a computer for a
particular event of interests For this study, the
following events are important: We time that the
vehicle passed through the conflict area, the time a
vehicle entered a queue, and the time a vehicle
reached the first position of a queue, the time a
vehicle exited from
14.9
ao.2
S9.8
103.8
~2
80.2
100
14.8
41.8
24.2
140.7
73.7
o
22.7
13.9
13
4S.6
282
94.?
33.8
44.5
303
O.1
14.S
3 179 1
12T6 1
63 T 19-1
13.4 136
11.8 22.6
10 33.1
13.S 44.?
6 10.2
4 1S.9
53 13.7
S.7 46.6
8 27.8
O O
83 15.8
3.6 6.6
5.8 99
6 ~ 14.7
6.9 14.8
IS S S2.3
1.1 16.9
S.4 13.8
3.? 1S.4
1.1 7S
1S 6.?
the stop line. In addition, the vehicle type, turning
movement type, lane usage are also recorded Once
these data were recorded by TDIP, a data file is
assembled using a spreadsheet for further data
reduction Table 17 gives the format of the raw data
file. Further data reduction exacted traffic flow
parameters such as flow rate, delay, critical gap,
follow-up time, and queue length. Table 18
illustrates the format of part of We summary data
for one of the minor street approaches. These data
are directly calculated from We raw data file of each
site. Notice Rat critical gap cannot be obtained
directly Dom We raw data file. Special procedures
have to be used to estimate critical gap.
OCR for page 43
43
Table 17. Raw Database Format for I WSC Intersections
00:10:04
, ~ ~ ~, ~ ~ ~ . B' .... ,,,, . .... ...... , , .,,,, , .,, ... . ................................
~~ t: ........ ........ ............ ............... . ~-
00:10:04 WBTH 1 1
00:10:08 WITH 2 1
00:10:09 NBRT 1 2 00 09 32
00:10:13 EBRT 1 1
00:10:14 WBLT 1 1 00:10:13
00:10:16 WBLT 1 1 00:10:14
00:10:23 EBRT 1 1
00:11:02 WBLT 1 1 00:11:00
00:11:04 EBTH 1 1
00:11:05 EBTH 1 1
00:11:06 EBBS 1 1
00:11:07 EBTH 1 1
00:11:10 EBTH 1 1
.;;;;;;;;; ;;;;;; ;.;;;;;.;;;;
:::::::::::::::::::~:~::::::::::
..................... I
00:10:08
00:10:13
00:10:14
00:10:13
00:10:14
00:11:00
00:11:00
Notes: PassTime is the time the vehicle passed the conflict point, LaneUse is the lane number the vehicle used; VehType is the vehicle type;
EnterQ is the time the vehicle entered the queue; FirstQ is the time the vehicle reached the first position in the queue; E~tQ is the time the vehicle
existed from the stop line. EnterQ, FirstQ, and ExitQ are recorded only for minor stream vehicles.
Table 18. Example of Summary Data Format Collected for Each Intersection
- ., , ... ,, ,., , ,,,. , .... , ., ...... ,.,. .,., , , _ _
~ . ,, ~,.~,,,, , ~ ' . ~' .~ . ~, , . ~., ~
1 216 0 184 400 33.8 7.8 41.6 3.9 2.6 100 4.6 0
2 200 0 176 376 25.0 7.3 32.3 3.4 2.3 80 3.3 0
3 216 0 172 388 32.3 7.7 40.1 3.4 2.6 98 4.7 0
4 188 0 108 296 20.6 8.9 29.S 2.9 2.2 79 2.3 0
5 176 0 104 280 4S.1 11.8 S6.9 3.6 3.S 95 5.3 0
6 212 0 88 300 64.8 10.4 75.3 3.6 2.6 100 5.8 1
7 2Q8 0 152 360 40.9 8.9 49.8 3.9 2.9 100 S.2 0
Notes: Ti is the time period; Ad, VTH, and ~ are the traffic volumes for left tum, through, and right turn movements; AD, SD, and TD are delays
in queue, in server (stop line), and total; Tt and To are average follow-up time and move-up time; %Q is the percent time where the approach has a
continuous queue; AL is the average queue length; °/~;ITK, ALTO, and °/`iMTR are the percentages of heavy bucks, light bucks, and motorcycles;
°/ciLT, PITH, and SORT are the percentages of leR tum, through, and right turn movements.
...~Ho~TE
S
o
2
3
1
1
o
.................. ! = -.
' .. ~ ! ..~., ~, .,~,0,~,n, ', :,Y,.,O,W,.
,
o
o
o
1
3
o
o
S4
S3
S6
64
63
71
S8
o
o
o
o
o
o
o
46
47
44
36
37
29
42
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44
CRITICAL GAP AND FOLLOW-I]P TIME
ESTIMATION
Defining Critical Gap Events
To use the maximum likelihood method to estimate the
critical gap, the accepted gap and the maximum rejected
gap of each vehicle must be known. These data need to be
extracted Dom the raw data file prepared in the data
reduction process.
It Is important to correctly define the gap events that reflect
a driver's behavior at TWSC intersections. The passage
time of any vehicle that conflicts directly with the subject
vehicle can be defined as a begin gap event. If it is a "lag",
the begin gap event is the arrival time of the subject
vehicle at the stop line (f~rst-in-queue timed. The end
gap/lag event must be a major street vehicle that conDicts
wad the subject vehicle. The end gapAag event was defined
as such because only the major street vehicles were
observed to affect the minor sheet driver - specifically,
what actually determines a driver's decision whether to
enter He intersection is when the next major street vehicle
wall active at the intersection. Vehicles on He opposing
minor street generally do not exhibit clear priority over He
subject approach, no matter what kind of movement they
are making. Rather, drivers were observed to enter the
intersection on a first-come-first-serve basis. Three
examples to illustrate the importance of correctly defining
He key events are shown in Table 19 and In Figures 19
Trough 25.
In Example I, the subject approach vehicle (SBET)
reached He stop fine at 00:10:48. It rejected several gaps
before accepting the final gap. The rejected gaps include
EBTH(a lag), EBTH-WBET, WBET-EBTH. The accepted
gap was NBTH-EBTH, which equaled 4 seconds. Note
that the vehicle NBTH is included in defining He begin
gap event (~e accepted gap), but would not be included in
defining He end gap event. Figures 19 through 25 illustrate
He gap events discussed in this example.
Ensample 2, anEBTH vehicle (00:10:58) was defined as
an end-gap vehicle rawer Han the SETH (00:10:58),
because only vehicles once major street can be defined as
an end-gap vehicle. Example 3 shows a situation where a
"lag" exists.
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45
Table 19. Examples to Illustrate Begin and End Gap Events for Maximum Likelihood Method for Critical Gap Computations
l
Example 1:
*Sou~bound is ~e subject approach
PassT'me Movement EnterQ FirstQ Ex~tQ
00:10:50 EBTH
00:10:51 WBLT 00:10:51 00:10:51 00:10:51
00:10:52 EBTH
Be~ Gap 00:10:54 NBTH 00:09:31 00:10:48 00:10:54
00:10:55 SBLT 00:08:42 00:10:48. 00:10:54
End Gap 00:10:58 EBTH
00:10:59 WBTH
00:11:00 EBRT
00:11:02 EBRT
00:11:03 EBTH
Example 2:
*The subject approach is northbound.
PassTime Movement EnterQ FirstQ ExitQ
00:10:50 EBTH
00:10:51 WBTH
00:10:51 WBLT 00:10:25 00:10:26 00:10:51
00:10:52 WBTH
Begin Gap 00:10:55 SBTH 00:09:31 00:10:30 00:10:54
00:10:55 NBLT 00:08:42 00:10:24 00:10:54
00:10:58 SBTH
End Gap 00:10:58 EBTH
00:11:00 EBRT
00:1 1:02 EBRT
00:11:03 EBTH
Example 3:
*The subject approach is nor~bound.
PassTime Movement EnterQ FirstQ ExitQ
00:09:45 WBTH
00:09:50 WBTH
Begin Lag 00:10:55 NBLT 00:10:24 00:10:24 00:10:54
00:10:58 SBTH
End Lag 00:10:58 EBTH
00:11:00 EBRT
00:1 1:02 EBRT
00:1 1:03 EBTH
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46
0) ~ ·cl MILT e=_ ~ be ~Ail;_ ·1 10:41. ~ MILT Be_ a IRAQI. SALT nor ~ W ~
~1 1: ~
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 rT
10:48 lO:S3 10:SS 11:03 10:48 10:S3 10:S8 11:03
Figure 19. Gap Event Example
Figure 21. Gap Event Example
~131U yam ·' 10~. "I.T Sect · 4; (~) ~ or_ e lo:SZ. BALI ~1 e IIC
i ~ ;~ ~
1 1 1 1 1 1 1 1 1 ~1 1 1 i ~1 ~ 1 1 ~ ~ 1 1 ~ 1 1 1 1 1 ~ rT
10:48 lO:S3 10:58 11:03 10:48 lO:S3 lO:SS 11:03
....
. . ~
Figure 20. Gap Event Example
Figure 22. Gap Event Example
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47
(I == ~ ~ 10:54. TO ~ Cot ~ ~ ~ ~ gap of SBLT .
1
(-
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
10:48 10:S3 10:S8 11:03
Figure 23. Gap Event Example
(6) SBLT pow at lO:SS, a¢coptod ~ IMP Of ~ coconde. NBTH def m" the
beam gap Ad ElBTH defin" the "d gap .
Hi'
rl I I I I I I I I I I I I I I
10:48 lO:S3
lO:S8 11:03
Figure 24. Gap Event Example
(7) BB1.H pesece at tO:SS. defying to cad gap event .
;~ ~ 1\;
~ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
10:48 lO:S3
lO:S8 11:03
Figure 25. Gap Event Example
Computer software developed during this research project
was used to extract these gap event data. First, the
software generates a Its file in which all the gaps rejected
and accepted by each minor street vehicle are listed, along
with some ofthe attributes associated with each gap event.
These attributes include the minor street vehicle type,
turning movement type, and lane usage; the begin gap
vehicle's turning movement, vehicle type, and lane usage;
the end gap vehicle type, turning movement, and lane
usage; Me accepted and rejected gap size; the maximum
rejected gap; the number of rejected gaps; the number of
rejected gaps Mat are larger Man the accepted gap size, and
the minor street vehicle's delay. The software further
processes1he data file and extracts all of the accepted and
the max~m~nn rejected gaps based on selected criteria, such
as~rningmovement,vehicleWpe, and delay. An input file
is then generated for a computerized maximum likelihood
estimation program developed by Troutbeck (1992~. The
software provides a report of the mean critical gap, the
standard deviation, and the number of observations. The
software processes the critical gap estimation for a
specified period at a site or across different sites where
similar intersection attributes exist.
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48
Table 20. Example of Exbachug Follow-Up Time Data
...................... ;
...,, e..........
00:39:31
00:39:34
00:39:53
00:39:58
00:39:58 l
00:39:59
00:40:00
00:40:04
00:40:06
00:40:09
00:40:14
00:40:17
00:40:20
00:40:24
00:40:25
00:40:27
00:40:30
00:40:35
00:40:38
00:40:41
00:40:43
00:40:46
00:40:47
00:40:50
00:40:55
00:41 :00
00:41 :01
00:41:02
..... ~ ^
- Knot
EACH
NBLT
EB7H
W8rH
EBTH
EBTH
WBTH
WITH
EBTH
NBLT
NBLT
EBTH
NBLT
EBTH
WBrH
EBTH
NBLT
EBTH
NBLT
NBLT
WBTH
WBTH
NBLT
NBLT
NBLT
WBrH
FRTU
00:39:50
00:39:58
00:39:50
00:40:10
00:40:08
00:40:12
00:40:06
00:40:09
00:40:30
00:40:38
00:40:36
00:40:40
00:40:26
00:40:28
00:40:51
~-
00:40:41
00:40:47
00:40:51
00:40:46
00:40:48
00:40:54
Measurement of Follow-up Time
Unlike He critical gap, the follow-up time is measured
directly from the collected field data. The follow-up time
can be also considered to be the saturation headway of Be
minor street vehicles. A follow-up time is observed only
under the following conditions:
· He following vehicle has been queued, i.e., when
the vehicle arrives at the intersection, there is
already at least one vehicle in front of it;
· botihof~e vehicles (i.e., He lead vehicle and the
following vehicle) use the same gap in the
conflicting streams.
When a follow-up hme is observed, it is calculated based
On the ex~t-queue times of the two vehicles using the same
gap. The software was used to extract the follow-up times
from the raw data file. Table 20 illustrates a portion of a
sample data set where the follow-up times are observed
and exacted. The minor sweet approach is in the
northbound direction in this example.
OCR for page 49
Irene 2
- ~ Inane 1
- > lee 1 `\
> Ire 2
71 ~
Figure 26. musha~cion of Conflicting Movements at Multi-Lane Sites
Field Estimation of Critical Gap and Follow-up Time
The critical gap and follow-up lime were measured for
each site based on the procedures discussed previously.
The measurements were collected Tom a total of 59
videotaped penods (53 unique intersections). The critical
gap or follow-up lime at some sites could not be measured
because of a I~m~ted data sample or because of unusual
geometry at the site. The measurements and associated
data are listed In Appendix IT of Working Paper 16, and
include the mean values of Me critical gap and follow-up
time, the standard deviation, and We number of
observations for each site. Two critical gap measurement
results for sites with multi-lanes on the major street were
also given, and denoted as AR Lanes and Cony: Lane
respectively.
At sites with multi-lanes, "conflicting" movement vehicles
may travel in more than one lane. Therefore, not all the
vehicles of a "conflicting movement" have physical
conflict potential with We minor street vehicle. Figure 26
illustrates such a situation. In Figure 26, the major street
has two through lanes ~ each direction. For the minor
street right An, only vehicles ~ lane 2 from the left side
have physical conflict potential wad the minor street right
turn vehicle.
For the minor street left turn, all Me vehicles from the left
side have physical conflict potential with the minor street
vehicle; however, those vehicles in Lane ~ from the right
side have the greatest physical conDict potential with the
minor street left turn vehicle.
· . . . - . . - ~..
49
Issues arise regarding how to define the gap events at
multi-lane sites. Two ways to deal with this were
investigated. The first method was to include ah the
vehicles In a conflicting movement to define the gap
events, no matter which lane the vehicle travels in. The
second method was to include only those vehicles ~ the
physically conflicting lanes while defining the gap event.
In the first memos it does not matter which lane the
conflicting vehicles are in, including vehicles traveling
parallel to each other, therefore, this method results in a
large number of smaller rejected gaps and accepted gaps.
Under this method, the critical gap is smaller Man that
obtained using the second method. Using the second
memos Is more rational, because the gap event confronted
by the minor sheet driver is much clearer. Vehicles in the
non-conflicting lane also have some elect on the minor
street Diver; however, this effect has been taken into
account implicitly by the set of accepted and rejected gaps
used to obtain the critical gap estimate. For example, by
excluding the vehicles in the non-conflicting lane, the
accepted gaps and rejected gaps calibrated from Me raw
data file are usually larger when compared to those at a
s~ngle-lane site. As a result, the critical gap would be larger
than that at a single-lane site. The following analysis and
calculations are based on We values calculated using the
second memos.
The same issues of defining gap events are relevant at a
multilane site to detemiiIiing the "weight' to assign to
mayor street vehicles approaching trom me right when
considering the parameter vp in the capacity formula for a
minor street left turn movement (if there are N lanes from
He right, then the total through flow approaching from Me
rift may be divided by the number of lanes N).
The critical gap and follow-up time measurement results
were first analyzed according to movement type,
intersection geometry type, major street speed, and
geographic sector. The mean values of each group were
calculated. Although this approach may provide a
generalized sense of what range of values We critical gap
and follow-up time have under venous U.S. conditions, it
may have also obscured some of Me factors that may cause
large variations in Me cntical gap and follow-up time. To
address these issues, regression analysis was conducted to
investigate these favors and develop generalized equations
for critical gap and follow-up time calculations.
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50
FIELD CAPACITY ESTIMATION
Two capacity terms have been used during this
project: field capacity and model capacity. Field
capacity is defined as the true capacity measured in
the field and that seines as the basis for testing
different capacity models. Mode} capacity is the
capacity predicted by a theoretical mode! based on
inputs of certain traffic and intersection
characteristics. Unless the field capacity is measured
correctly as the basis for capacity mode! testing, the
mode! testing process cannot be conducted
successfully.
The field capacity of a minor stream or approach
can be measured directly in the field under a
continuous queue condition. Under a continuous
queue condition, the departure flow rate is
unequivocally t e actual field capacity of a minor
stream movement or a minor street approach.
However, at most sites, it is typically difficult to
obsene a continuous queue in the field for a
sufficient length of time to estimate capacity.
Therefore, the number of usable data points usually
limits the mode} testing process. To obtain enough
data points of field capacity, shorter lateral data
must be used, say 1-m~nute lateral data. The HCM
regards the 15-minute peak period as the time for
which average capacity should be stated, however,
it is rare to find this condition at unsignalized
intersections. For example, in urban areas, drivers
may divert to alternative routes if they experience
high delay at unsignalized intersections. Further,
many intersections are signalized long before
reaching this condition.
One obvious disadvantage of using I-minute
interval data is He large variation In flows between
consecutive short lime intervals. For example, if one
vehicle on the mirror stream passes through the
intersection, the resultant capacity would be 60
veh/hr. Thus, with I-minute interval field data, the
capacity can only be reported to an accuracy of 60
vph. The data collected dunng the NCHRP 3-46
project showed that, at many of He intersections,
even a I-minute continuous queue situation may be
rare. It was found that a high number of continuous
queuing data points were concentrated at a small
number of intersections. For example, among all He
I-minute intervals where a continuous queue was
found,onlyI4 intersections had more thanI0 such
minutes of intervals, and they combine to account
for 86% of the total data points of field capacity.
The advantage of using the departure flow as the
field capacity under a continuous queue condition is
that it reflects He true capacity of He intersection.
This is especially important when the true capacity
cannot be obtained through other methods. For
example, where the minor street approach allows
right turn sneakers, the capacity usually Increases to
some extent as compared to when no sneakers are
allowed.
Kyte (1992) used He following equation to estimate
the field capacity of unsignalized intersections
which are undersaturated (i.e. no continuous queue):
3600
t5 tmv
where
(10~
en Is the field capacity for the minor stream
or minor sweet approach, vph
t5 iS He average service delay of vehicles
once Hey arrive at the stop line, see
to is the average move-up time Tom
second position to reaching He stop line,
see
The service delay is measured for a specified
interval and averaged for all the minor stream
vehicles that passed the intersection during the
interval. Service delay is He delay that occurs at the
first position of the queue (the stop line). It is the
duration Tom when a vehicle reaches the stop line
until it exits the stop line. The measurement of
service delay does not require a continuous queue
condition. This information is available in the
macroscopic database established for each site. The
move-up time is the amount of time from when the
previous vehicle exits the stop line until the
subsequent queued vehicle reaches the stop line. It
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51
is meas~ed In a manner similar to the service delay.
The measurement of move-up time requires that at
feast two vehicles are present so that the subsequent
vehicle is queued. Therefore, a move-up time may
not tee measured for each vehicle. An average move-
up time measured for ad the time Centrals was
usually used In the above equation. It should be
noted that move-up time is different from follow-up
time as used in the capacity models. Move-up time
Is part offoDow-up time. FoDow-up time consists of
move-up time and hesitation time, the tninimum
time a driver has to spend at the stop line.
This method is based on the assumption that We
service delay for each minor stream vehicle is a
randomly distributed variable Mat is affected by Me
composition and volume of the major conflicting
streams as well as by the gap acceptance process.
The sum of service time and move-up time is a
variable that reflects Me average time that each
minor stream vehicle occupies the server facility,
here defined as the stop line. Based on the concept
of queuing theory, the capacity of a facility is Me
inverse of the senice time plus move-up time.
The advantage of the application of this method is
Mat it does not require a continuous queue condition
to estimate capacity; therefore, the number of data
points that can be used for the mode! testing is
significantly increased. In addition, this method
gives the capacity of a single sewer (right turn
sneaker is not counted), which is consistent and
comparable with the results given by various
theoretical capacity models.
While this method gives identical results to
measuring departure flow under continuous queue
conditions, it is not known and is Impossible to
determine whether this method is valid when a
continuous queue does not exist. It is possible that
drivers may be more relaxed under low flow
conditions with higher resene capacity (critical gap
estimation methods cany the same caveats).
Another potentially serious problem is that, in many
cases, the exact instant of the beginning and the end
of afiIst-in-queue senice time is not easy to detect.
The absorption capacity method is another field
capacity estimation method that was considered by
Me project team. This method is based on the same
concept as the theoretical gap acceptance models.
Knowing or assigning the critical gap to, the follow-
up time tf, and the major stream gap distnbution,
the number of vehicles using each gap can then be
calculated using the equation:
q =
O forG``ctc
Gi-tc +1 forGet
If ~ c
(106)
where As Me number of minor stream vehicles that
can use gap Gi (integer value), and Gi is Me major
stream gap size, sec.
The capacity can then be calculated by aggregating
this number of vehicles over all gaps based on a
certain time period, as shown in Equation 107.
1
n T if
where T is the length of time period.
(10~
It Is assumed that each Giver has a constant critical
gap and follow-up time, and each major stream
movement is 1leated in an identical manner, i.e. each
major movement has the same effect on the minor
stream driver. Equation 106 assumes a stepwise
fimction for the number of vehicles using each gap.
Similar to Equation 105, this method does not
require a continuous queue condition to estimate the
field capacitor; The drawback of this method,
compared to the procedures mentioned before, is
Mat to and tf must be defined for the application.
Comparisons were made between Equation 105 and
Me absorption method. The first comparison of
these two methods was conducted for continuous
queue situations where true capacity is known. The
capacity estimated by these two methods was
compared with the departure flow rate, which is the
true capacity. Another comparison was conducted
for non-continuous queue situations. Previous
studies have shown that both Harders model and the
Siegloch/Troutbeck model can give good capacity
OCR for page 52
52
estimates given good estimates of Me cntical gap
and foRow-up time. As the hue capacity cannot be
measured directly under non-continuous queued
conditions, We capacity predicted by either Harders'
mode} or Me SiegJoch/Troutbeck mode! was used as
the basis of comparing the two field capacity
estimation methods. Table 21 and Table 22 show
the comparison between these two methods.
Table 21. Statistical Capacity Comparison Results Under
Continuous Queueing Conditions
,
it' A ................. .
................................................................................ ''. ~s ' :'' . ~
.................... , . ,. ~,,,. ~.......................................
Constant
Sty Error of Y
R Squared
No. Observations
X Coefficient
Std Error Coe~c~ent
o
18
0.92
3S
1.0
0.01
o
64
0.52
3S
0.96
0 03
.
Based ontihe s~icaI results of these comparisons
Me following conclusions were reached:
.
.
.
The departure flow under continuous
queuing reflects He true capacity. However,
the difficulty of finding a continuous queue
in the field for a substantial period at most
sites usually limits the ability of this
memos to test capacity models over a wide
range of flows.
Equation 105 does not require a continuous
queue condition. Under continuous queue
conditions, it gives identical results to the
departure flow. The difficulty of measuring
move-up time precisely for each vehicle
and the use"' of an average ''~move-up time
value for the whole period may cause some
variation in the results; however, the
variation does not have a significant effect
on the final results because the move-up
time is small relative to the senice time.
The results also show a good correlation
between the theoretical mode} results and
Kyte's method (Equation 105) under non-
continuous queue conditions.
The absorption method can be applied as
long as major street volume or gap data are
available. It is not necessary to measure the
minor street delay or flow to apply this
method except to estimate the critical gap
and follow-up time. When minor street
traffic data such as flow rate during
· · .
continuous queuing or average service
delay are available, the absorption method
does not show any advantages over
Equation 105. The absorption method
presents a problem when different Wing
movements exist on~emajor street. Minor
skeam drivers behave very differently when
Hey face different combinations of burning
movements. For example, if the oncoming
vehicle is a right turn vehicle on the major
street, the minor street driver would accept
a smaller gap, which would not be reflected
by this method.
Equation 105 was therefore recommended as He
basis for comparison of He mode! testing tasks in
the absence of continuous queue data. Whenever
possible, He departure flows during continuous
queuing should also be used for mode} testing,
especiaDywhen~ng sites wad unusual traffic and
geometry characteristics, such as right turn sneaker
conditions. If necessary, He absorption method can
be used for conducting special tests where He other
methods cannot be applied.
Table 22. Statistical Results of Canacitv Companion IJnder Non~Cnntinil~ll~ t~llelle~lline ~nnAifinn~
l :':':': :':':':':':':':':':':': :':':':':':':':':':':':':::':::'::'::::::':':':':':':':':':' :':':':':':':':':':':': :':::::::::::::::::::: ':::::::::::::::::':':':':':':':':':': :':::: ':':':: :':':':':':':': :':::::::::::::':::':::::::
::::::::::::::::::::: ::: :~:~:::::::::::::::::::::::::::::: ::::: ::::::: .:~:~:~:~:~:~:~:~:~:~:~:~:~:~:::~:~:~:~:~:~:::::::::::::: : :::::::::::::::: ::::....:::: :::::::::: ::::
.:~:..:.:.:~...:,:~:.:.:.:.:.,:.:~:~:2:::::: ::::::::::::::':::':::: ::'::: :::::::::::: ::: ::::::::: :':::':::':'::':':':::' ::::: ::
:::::::::::::::::::::::::::::::::::::::::::::::::::::::: :':: :':::::::::: '::::::::::::: ::::::::::::::::::::::: :::::::::::::::::::::::::::::::: ::::::::::: :::::::: a:::::::::::::
Constant O O
Stir KIT. Y 41 40
R Squared 0.81 0.81
No. Obs. 33 33
X Coefficient 0.98 0.96
Stir Err. Coefficient 0.02 0.01 _ _
. .
o
S3
0.79
33
0.99
0 02
o
SS
0.77
33
0.97
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53
FIELD DELAY ESTIMATION
The vehicle delay was measured from the field data based
on specified time intervals of 5 minutes and 15 minutes.
The Traffic Data Input Program (Boesen, Kyte, and
Rin~isbacher, 1991) was used to extract the traffic
information from the videotapes and a raw data file was
assembled us~ngtheinformation extracted. (The format of
the raw date file was given in Table 17~. The following
information is included in the raw data file:
.
.
.
.
.
.
.
passage time of each vehicle
movement direction
vehicle type
lane usage
time a vehicle enters queue
time a vehicle reaches the first position in We
queue
time the vehicle exits the queue.
The data are sorted in an ascending order based on Me
passage time of each vehicle through the intersection.
The individual delay experienced by each vehicle can be
calculated from the enter-queue time, f~rst-in-queue time,
and the exit-queue time. The difference between the
f~rst-in-queue time and the enter-queue time is called
"queue delay", and the difference between the ex~t-queue
time and first-'n-queue time is called "service delay?' or
"wont delay". The "total delay?' is the sum of queue delay
and service delay. Since the delay mode! predicts average
delay experienced by the minor stream vehicles during a
given period, the field delay should also be measured based
ontme intervals and averaged for all of Me minor stream
. .. ..
ve nc es.
For this study, delay data were collected from the raw data
file by averaging all of the individual delays experienced
by all of Me minor stream vehicles Mat departed the
intersection during a given interval. For example, if 4
vehicles departed Me intersection during Me same
5-minute time interval, and the individual vehicle delays
for these 4 vehicles were 10, 5, 5, and g seconds,
respectively, the mean average total delay would be 7
seconds [~10+5~5+g)/4 = 7 seconds].
Delay measured In this way is somewhat inconsistent USA
Me delay predicted by the delay models. The delay models
usually predict average delay for Dose vehicles that arrived
during a given interval. The two melons would give the
same results if all of Me vehicles arrived and departed the
intersection during the same interval. Working Paper
(NCHRP 3-46, 1995) indicated that
.
when average delay is less than 60 sec/veh, there
is no significant difference between the two
methods if a 5-minute or longer interval is used,
as the interval increases, the error between the two
methods is reduced; and
if a 15-minute interval is used, an average of 94°/O
of the total vehicles would arrive and depart
during the same interval.
The difference between the two delay measurement
methods is insignificant for 5 minute and 15 minute
periods of aggregation.
Delay was measured in this manner (delay data sorted
according to the departure time of the vehicle) for this
study to keep it consistent with other traffic parameter
measurements such as flow rate and queue length. Flow
measured in this manner during continuous queue
conditions yields the field capacity for a given interval. The
delay modeltesting process is discussed later in this report.
A 15-minute interval delay data was used to reduce the
vanance.
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54
. . ,
I. ~
Representative terms from entire chapter:
major street
