ITS Heartland 2012 Annual Meeting Kansas City, MO Presented by Darcy Bullock, Professor at Purdue University

1. Operational Performance Measures and
A PictureBased
Outcome
Book
AssessmentApproach
for Arterial Management
using High Resolution
to Traffic Data and Database
Controller Data, Probe
Signal
Management
QA/QC Procedures
Darcy Bullock
darcy@purdue.edu
Purdue University School of Civil Engineering

2. March 27, 2012: 0830

5. Why?

7. Advanced Transportation Information
Systems (ATIS)
Messages
Opportunities to
What gets measured gets done, Push the State of
what gets measured and fed back the Possible
gets done well,
what gets rewarded gets repeated.
– John E. Jones
Enormous opportunities to fuse data from
• traffic signal controllers and
• probe data sources

8. Circa 1995 Screen Scrapes
(PPTs were so old they did not load)
LSU vehicle (25k miles) I-10/12 Split Segment
Coding
R D S an t nna
e
FM s ub ca r re r
i G P S an t nna
e S a t llit s gna l
e e i
1001
7 1005 1004
1002
1006 101
1003
1008 1010
1009 7
101 10 100
3 10 11
14 1018
RD S T ri b e
m l 10
D if fe ren ta lco r re c ton
i i 15 10
300 0 P a ce r
l 12
400
10
16
Lap t p
o
co pu t r
m e
DG PS da t
a

9. Evolution
LSU Early Controller
CMU (Denardo Data (Brute Force
Years)
1990 2000 2010
Brute force Performance
GPS Data Saban joins Measures
LSU

10. October 2006 State of the practice

11. “If one wants to be outstanding in
there field, one must first spend some
time standing in the field” Bill Kloose

12. Contributor to this talk
• INDOT (Infrastructure • Indiana LTAP
Support and Agency – Neal Carboneau
Perspective) – Jay Grossman (Elkhart
– Jim Sturdevant County)
– Jay Wasson • Andrew Nichols (Marshall
– Ryan Gallagher University)
– Greg Richards • Econolite (ASC 3 Data
• Purdue University Logger )
– Chris Day – Gary Duncan
– Tom Brennan – Eric Raamot
– Ross Haseman – Lu Ta
– Alex Hainen – Brian Griggs
– Steve Remias
– Darcy Bullock
12

13. Emerging Shared Vision
Active Traffic
Signal
1. Develop
Management infrastructure and
procedures to
systematically
prioritize investing
Agencies
Universities Vendors engineering
resources
2. Assess that impact
We are in a period where we need to re-
introduce theory and fundamentals so we
change how agencies spec. & operate traffic
signals and what vendors provide and

14. True or False
On Average, we have no capacity problems at any
signalized intersection?
250
200
Counts per 15 minutes
150
100
50
0
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time of Day

15. 15-Minute Counts (Phase “n”)
250
200
Counts per 15 minutes
150
100
50
0
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Time of Day
15

16. Challenge
How can we use controller and probe data to quantify
what we can see and assess if we can improve.

17. Outline
• Signal Management/ Challenges
• Data Sources
– Controller
– Probe Vehicles
• Fundamentals
• Data ->Information to assess fundamentals
• Results
• Q&A..Dialog Don’t wait
until the End

18. INDOT Signal Network
Question
• Where (and when) are
the opportunities to
improve signal
operations?
INDOT: 2,600 signals in 300 systems
Nationwide: 350,000 signals
Globally: who knows?
18

19. ~350,000 Traffic Signals…We need systematic
procedures for identifying operational
problems…and fixing them using controller and
probe data.

20. Chicago
Typical Corridor (22 Intersections)
80
65
65
65
Lafayette
74,000 Indianapo
Parameters/int 70
N
Map Area
Isolated Indianapolis, IN 65
(Free) Operation
Coordinated
Operation
1 65
2
3 US30 d) 5 ft antenna
4 5 6 8 10 12 65 18
2000 ft 20 22
14 16
500 m
e) 7.5 ft antenna
US30 d) 5 ft antenna
N 4,000 FT
1,000 M 7 9 11 65 15
13 17 19 21
e) 7.5 ft antenn
2000 ft
500 m
US30 d) 5 ft antenna

21. ~74,000 Configurable
Parameters/Int 192M
Statewide!
~1.6M
Parameters
Default
Database
Default Database Query
of Each Intersection
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Longitudinal Query of Intersection
Controller Databases

22. So how many do we really use?
3200
Isolated Coordinated Vendor Specific
3000 (Free) Operation Operation
NTCIP 1202
2800
2600
2400
COUNT OF ALTERED PARAMETERS
2200
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
INTERSECTION ID

23. Traffic Signal Timing Process
Opportunity Today’s
Message
I. II. III. IV. V. VI.
Define Assembly Software Timing Design Deployment Assess
Objectives, relevant data Modeling and Docu-
Assess and to support mentation
Prioritize timing and
activities by docu-
Time of Day mentation
and location objectives

24. Outline
• Signal Management/ Challenges
• Data Sources
– Controller
– Probe Vehicles
• Fundamentals
• Data ->Information to assess fundamentals
• Results
• Q&A..Dialog

25. Historical data collection (~1,000:1)
averages out all of the information
Typical less then 500
average values/day
sent to Traffic
~500,000 Management Centers
Events/day (and those are rarely
archived)

26. Easter Weekend: Southbound
Example 40 min travel time sample
Observed at 18:10
~MM 244
~40
~MM 220 minutes
Observed at 18:50

27. Easter Weekend
Southbound Travel Time (~24 miles)
140
Friday Saturday Sunday Monday
120
SB Travel Time (minutes)
Approximately
1 hour of delay Diversions on
100 State/Local Roads
80
60 Normal Travel
Time ~ 22 minutes
40
20
0
4/10/09 0:00 4/11/09 0:00 4/12/09 0:00 4/13/09 0:00 4/14/09 0:00

28. Sample SR 32 Arterial Data
SR 32 Instrumented
Arterial from SR 238
to SR 37

30. Econolite ASC 3
Bluetooth
Antenna
with Indiana Data
Logger Enabled
SR 32 @ SR 238
Bluetooth Data
Ethernet Switch
Logger

31. SR-37 Travel Times
10
Perhaps
9
Opportunity to
8
improve on
Saturday
7
Travel Time (min)
6
5
4
3
2
1
Perhaps
M T W Th F Sa Su M T W Th F Sa Su Opportunity to
0
6/15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/24 6/25 6/26 6/27 6/28 6/29 improve on
Saturday

32. Outline
• Signal Management/ Timing overview
• Data Sources
– Controller
– Probe Vehicles
• Fundamentals
• Data ->Information to assess fundamentals
• Results
• Q&A..Dialog

33. Highway Capacity Manual Delay
Equation
Oversaturation
d d1 PF d2 d3 (Split Failures)
2
g ,a
1 P ,a
0.5C 1 PF ,a fPA
Ca g ,a
d1, ,a 1
g ,a
C
1 min 1 X
, ,a
Ca
1 Capacity Utilization
(Volume-to-Capacity Ratio)
Quality of Progression
(Percent on Green)
2

34. Coordination:
Split, Cycle, Offset
5L
Cycle
Really Hard
4L to get perfect
for 22
Intersections
Distance
3L
2L
L
1 2 3 4
Time (cycles)

35. Coordination:
Split, Cycle, Offset
5L
Phone calls
4L
work pretty
well
Distance
3L
2L
Split
L
1 2 3 4
Time (cycles)

36. Coordination:
Split, Cycle, Offset
5L
Tuning is
4L
labor
intensive
Distance
3L
2L
Offset
L
1 2 3 4
Time (cycles)

37. Coordination:
Split, Cycle, Offset 74,000
parameters, 1000’s
of opportunities to
make mistakes
5L
Cycle
4L
Distance
3L
2L
Offset Split
L
1 2 3 4
Time (cycles)

38. Operational Performance Measures and
A PictureBased
Outcome
Book
AssessmentApproach
for Arterial Management
using High Resolution
to Traffic Data and Database
Controller Data, Probe
Signal
Management
QA/QC Procedures

39. Outline
• Signal Management/ Challenges
• Data Sources
– Controller
– Probe Vehicles
• Fundamentals
• Data ->Information to assess fundamentals
• Results
• Q&A..Dialog

40. Purdue Coordination Diagram Construction (PCD)
Loop
Detection
Time in cycle
120 Cycle
boundary
90 Red
Green phase ends 70
Green
Cycle begins
window
phase begins
Cycle ends
Green
50 Green
time
0
0 sec 50 sec 90 sec 120 sec
12:00:00 12:02:00 time of day
12:00:00
12:01:10
12:02:00
70 sec
12:01:10

41. a
c383
c384
f
c
g
b
e
d
c385
c386
c387
c388
i
c389
c390
h
c391
c392
c393
c394
c395
c396
c397
Phase 3
Phase 4
Phase 1
Clearance
Phase 2 Green
52
Phase 2 Red

42. Timing Plan Pattern
1 24 Hour 3Overview
2 4 5 6 7 8
20-pt.
moving average
b2
a2
a1
b1
53

43. NB @ Pleasant
NB @ SR 238
Travel Times
10
9
8
7
Travel Time (min)
6
Better Progression
5
4
3
2
1
Perhaps
M T W Th F Sa Su M T W Th F Sa Su Opportunity to
0
6/1 6/2 6/3 6/4 6/5 6/6 6/7 6/8 6/9 6/10 6/11 6/12 6/13 6/14 6/15 improve on
Saturday

44. Saturday Offset Adjustment
SR 32 good
Random
arrivals
Pleasant
No
platoons
bad
Town &
Country
good good
bad Greenfield
bad
55

45. Three Poor Offsets
NB @ 37/Pleasant SB @ 37/Greenfield NB @ 37/Greenfield
56

46. Offset Adjustments on Middle Segment
SR 32 good
Random
arrivals
Pleasant
No
platoons
bad
Town &
Country
good good
bad Greenfield
bad
57

47. Offset Adjustments on Middle Segment
• Northbound at 37/Pleasant is bad. The platoon is captured in red.
• However, any offset adjustments that we make will also impact Southbound progression at
37/Town and Country (intersection to south) by shifting arrivals.
• We can mitigate any impacts at 32/37 (intersection to north) by adjusting its offset to keep it fixed
relative to 37/Pleasant.
NB @ 37/Pleasant SB @ 37/Town and Country
POG = 40.1% POG = 80.2%
5069 arrivals on green
(0600-2200)
58

48. Add 10 seconds at 37/Pleasant
• Green times will occur 10 seconds earlier at 37 & Pleasant
– Equivalent to vehicles arriving 10 seconds later
• Southbound vehicles will arrive 10 seconds earlier at 37 & Town and Country
NB @ 37/Pleasant SB @ 37/Town and Country
POG = 55.4% POG = 77.8%
5589 arrivals on green
59

49. Add 20 seconds at 37/Pleasant
• Green times will occur 20 seconds earlier at 37 & Pleasant
– Equivalent to vehicles arriving 20 seconds later
• Southbound vehicles will arrive 20 seconds earlier at 37 & Town and Country
NB @ 37/Pleasant SB @ 37/Town and Country
POG = 67.4% POG = 68.8%
5688 arrivals on green
60

50. Add 30 seconds at 37/Pleasant
• Green times will occur 30 seconds earlier at 37 & Pleasant
– Equivalent to vehicles arriving 30 seconds later
• Southbound vehicles will arrive 30 seconds earlier at 37 & Town and Country
NB @ 37/Pleasant SB @ 37/Town and Country
POG = 73.4% POG = 57.5%
5446 arrivals on green
61

51. Comparison with original (actual “before” case)
SR 32 good
Pleasant
bad
Town &
Country
good good
bad Greenfield
bad
62

52. Predicted Vehicle Distributions with Offset Adjustments
SR 32 Unchanged
Pleasant
Better
Town &
Country
Still OK Still OK
Better Greenfield
Better
63

53. Outline
• Signal Management/ Timing overview
• Data Sources
– Controller
– Probe Vehicles
• Fundamentals
• Data ->Information to assess fundamentals
• Results
• Q&A..Dialog

54. So..did it have an impact.

55. SB, SR 37 / SR 32 NB, SR 37 / SR 32
Before d
1001
0600 1400 2200 0600 1400 2200
SB, SR 37 / Pleasant NB, SR 37 / Pleasant
1002
a
0600 1400 2200 0600 1400 2200
SB, SR 37 / Town & Country NB, SR 37 / Town & Country
1003
0600 1400 2200 0600 1400 2200
SB, SR 37 / Greenfield NB, SR 37 / Greenfield
c
1004 b
0600 1400 2200 0600 1400 2200

56. SB, SR 37 / SR 32 NB, SR 37 / SR 32
e
After d
1001
0600 1400 2200 0600 1400 2200
SB, SR 37 / Pleasant NB, SR 37 / Pleasant
a
1002
z z
0600 1400 2200 0600 1400 2200
SB, SR 37 / Town & Country NB, SR 37 / Town & Country
1003
f
0600 1400 2200 0600 1400 2200
SB, SR 37 / Greenfield NB, SR 37 / Greenfield
b
c
1004
0600 1400 2200 0600 1400 2200

57. SB, SR 37 / SR 32 NB, SR 37 / SR 32
e
Predicted d
1001
0600 1400 2200 0600 1400 2200
SB, SR 37 / Pleasant NB, SR 37 / Pleasant
a
1002
0600 1400 2200 0600 1400 2200
SB, SR 37 / Town & Country NB, SR 37 / Town & Country
1003
f
0600 1400 2200 0600 1400 2200
SB, SR 37 / Greenfield NB, SR 37 / Greenfield
b
c
1004
0600 1400 2200 0600 1400 2200

58. Before
9
(Sample size=4797)
8
7 Long Saturday travel
times compared to rest
Measured Travel Time (min)
of week
6
5
4
3
2
1
Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun
0
6/15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/24 6/25 6/26 6/27 6/28 6/29
Date/Time

59. After
9
(Sample size=5401)
8
7 Saturday travel times
Measured Travel Time (min)
comparable to rest of week
6
5
4
3
2
1
Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun
0
7/13 7/14 7/15 7/16 7/17 7/18 7/19 7/20 7/21 7/22 7/23 7/24 7/25 7/26 7/27
Date/Time

60. Lets Evaluate the Impact
Statistically and Financially
I. II. III. IV. V. VI.
Define Assembly relevant Software Modeling Timing Design and Deployment Assess
Objectives, Assess data to support Docu- mentation
and Prioritize timing and docu-
activities by Time mentation
of Day and location objectives

61. NB: June 6, 2009
0900-1200 Travel Time Histograms
60 100%
90%
50
80%
Frequency
70%
40
Cumulative %
60%
Frequency
30 50%
40%
20
30%
20%
10
10%
0 0%
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 More
Travel Time Bin (Minutes)

62. NB: July 18, 2009
0900-1200 Travel Time Histograms
60 100%
90%
50
80%
Frequency
70%
40
Cumulative %
60%
Frequency
30 50%
40%
20
30%
20%
10
10%
0 0%
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 More
Travel Time Bin (Minutes)

63. Before (6/20/09)
After (7/25/09)
~1 min Travel
Time Reduction

64. Business Case: SR 37 Timing Improvements
(Largest Cost Benefit/Reduction/Avoidance)
USER SAVINGS
• Travel time tests for SR37 Corridor
1
have improved Northbound Travel
Time by ~ 1 Minute.
0.75
• ~8,500 Cars per Day Are Effected by
Cumulative Probability
this benefit (NB).
0.5 • ~0.17 Cents per minute ($10/hour)
saved for each driver in fuel costs and
time value.
0.25
• ~1.0 Minutes are assumed saved on
average for the intersection over 1-
0 Year with improvements.
0 1 2 3 4 5 6 7 8 9
Travel Time (min) • User benefit =
0600 -2200 (8,500 veh) (8,500 Veh/Day * $0.17/min * 1-
min/Veh * 2*52 Days/Year )=
$150,000/year for a 1.6 mile stretch
of roadway is realized.
76

65. Southbound Northbound
Baseline Optimized (Obj. III) Baseline Optimized (Obj. III)
1.0 1.0 1.0 1.0
1/SB 1/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
Int. 1
1.0 1.0 1.0 1.0
2/SB 2/NB
0.5 0.5 Int. 2 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0
Int. 3 1.0 1.0
3/SB 3/NB
0.5 0.5 0.5 0.5
0.0 0.0 Int. 4 0.0 0.0
1.0 1.0 1.0 1.0
4/SB 4/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
5/SB 5/NB
0.5 0.5 Int. 5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 Int. 6 1.0 1.0
6/SB 6/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
7/SB Int. 7 7/NB
0.5 0.5 0.5 0.5
Int. 8
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
8/SB 8/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
0 29 57 86 114 0 29 57 86 114 0 29 57 86 114 0 29 57 86 114
Time in Cycle (s) Time in Cycle (s) Time in Cycle (s) Time in Cycle (s)

66. Travel Time between 4 intersections
100% 100%
IV IV
I III
75% 75% II
II I
50% 50%
III
Base Base
25% 25%
0% 0%
1 2 3 4 5 6 7 1 2 3 4 5 6 7
(e) Southbound, Case B to Case C. (f) Northbound, Case C to Case B.
Cumulative frequency diagrams of probe vehicle travel times for alternative objective functions,
Saturday, 1500-1800.

67. Economic Impact (52 Saturdays)
Daily Annual
CO2 CO2
Total Time Emission Emission
Saved Reduction CO2 User Multi- Reduction CO2 User
Objective (veh-min) (tons) Savings Benefits plier (tons) Savings Benefits
(a) System 1, Northern Section
I Min Delay 5032 0.71 $16 $1,697 52 37 $810 $88,233
II Min Delay and Stops 3813 0.54 $12 $1,286 52 28 $614 $66,864
III Max Ng 1760 0.25 $5 $593 52 13 $283 $30,855
IV Alt. Max Ng 7883 1.11 $24 $2,658 52 58 $1,268 $138,229
(b) System 2, Southern Section
I Min Delay 24386 3.43 $75 $8,223 52 178 $3,924 $427,614
II Min Delay and Stops 25327 3.56 $78 $8,541 52 185 $4,075 $444,111
III Max Ng 25147 3.54 $78 $8,480 52 184 $4,046 $440,962
IV Alt. Max Ng 26338 3.70 $81 $8,882 52 193 $4,238 $461,845
(c) System 1 and System 2, Arterial
I Min Delay 29418 4.14 $91 $9,920 52 215 $4,733 $515,847
II Min Delay and Stops 29140 4.10 $90 $9,826 52 213 $4,689 $510,976
III Max Ng 26907 3.78 $83 $9,073 52 197 $4,329 $471,817
IV Alt. Max Ng 34221 4.81 $106 $11,540 52 250 $5,506 $600,073
Controller PM + Probe Data

68. Emerging Shared Vision
Active Traffic
Signal
1. Develop
Management infrastructure and
procedures to
systematically
prioritize investing
Agencies
Universities Vendors engineering
resources
2. Assess that impact
We are in a period where we need to re-
introduce theory and fundamentals so we
change how agencies spec. & operate traffic
signals and what vendors provide and

69. 1
$150,000
0.75
Cumulative Probability
0.5
0.25
0
0 1 2 3 4 5 6 7 8 9
Travel Time (min)
0600 -2200 (8,500 veh)
What gets measured gets done,
what gets measured and fed back gets done well,
what gets rewarded gets repeated.
– John E. Jones
Darcy Bullock
darcy@purdue.edu
Purdue University School of Civil Engineering

71. Diagnostics (Jim Sturdevant)

72. SR37 Fishers/Noblesville- (9)
Very good 2 way
progression

73. Coordination:
Split, Cycle, Offset
5L
Cycle
Really Hard
4L to get perfect
for 22
Intersections
Distance
3L
2L
L
1 2 3 4
Time (cycles)

74. US 31 @ 126th (8 in system)
Plan /cycle
change
discrepancy
from north

75. Diagnostics (Andrew Nichols)

76. Thursday (12/8)
Should we still be
running coord @ 9pm?

77. Opportunities
Active Traffic
Signal
1. Hi Resolution
Management Controller Data
Performance
Measure
Picture Book
Universities
Agencies
Vendors
Manual
2. Integrated (but
independent)
Probe Data
We are in a period where we need to re-
introduce theory and fundamentals so we Assessment
change how agencies spec. & operate traffic
signals and what vendors provide and

78. Scales well…4 more intersections
added to corridor in Summer 2010

79. Alternative Objective Functions
8 14
Performance Index (veh-h/h)
7 12
6
Delay (veh-h/h)
10
5
8 +44
4
+56 6
3
4
2
1 2
0 0
0 15 30 45 60 75 90 0 15 30 45 60 75 90
Offset Adjustment Offset Adjustment
(a) Objective I: Estimated delay. (b) Objective II: Delay and stops.
600 400
350 +40
500
Alt. Number on Green
Number on Green
300
400
+44 250
300 200
150
200
100
100
50
0 0
0 15 30 45 60 75 90 0 15 30 45 60 75 90
Offset Adjustment Offset Adjustment
(c) Objective III: Number of arrivals on (d) Objective IV: Alternative number of
green. arrivals on green.
1

80. Southbound Northbound
Baseline Optimized (Obj. III) Baseline Optimized (Obj. III)
1.0 1.0 1.0 1.0
1/SB 1/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
Int. 1
1.0 1.0 1.0 1.0
2/SB 2/NB
0.5 0.5 Int. 2 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0
Int. 3 1.0 1.0
3/SB 3/NB
0.5 0.5 0.5 0.5
0.0 0.0 Int. 4 0.0 0.0
1.0 1.0 1.0 1.0
4/SB 4/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
5/SB 5/NB
0.5 0.5 Int. 5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 Int. 6 1.0 1.0
6/SB 6/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
7/SB Int. 7 7/NB
0.5 0.5 0.5 0.5
Int. 8
0.0 0.0 0.0 0.0
1.0 1.0 1.0 1.0
8/SB 8/NB
0.5 0.5 0.5 0.5
0.0 0.0 0.0 0.0
0 29 57 86 114 0 29 57 86 114 0 29 57 86 114 0 29 57 86 114
Time in Cycle (s) Time in Cycle (s) Time in Cycle (s) Time in Cycle (s)

81. Travel Time between 4 intersections
100% 100%
IV IV
I III
75% 75% II
II I
50% 50%
III
Base Base
25% 25%
0% 0%
1 2 3 4 5 6 7 1 2 3 4 5 6 7
(e) Southbound, Case B to Case C. (f) Northbound, Case C to Case B.
Cumulative frequency diagrams of probe vehicle travel times for alternative objective functions,
Saturday, 1500-1800.

82. Economic Impact
Daily Annual
CO2 CO2
Total Time Emission Emission
Saved Reduction CO2 User Multi- Reduction CO2 User
Objective (veh-min) (tons) Savings Benefits plier (tons) Savings Benefits
(a) System 1, Northern Section
I Min Delay 5032 0.71 $16 $1,697 52 37 $810 $88,233
II Min Delay and Stops 3813 0.54 $12 $1,286 52 28 $614 $66,864
III Max Ng 1760 0.25 $5 $593 52 13 $283 $30,855
IV Alt. Max Ng 7883 1.11 $24 $2,658 52 58 $1,268 $138,229
(b) System 2, Southern Section
I Min Delay 24386 3.43 $75 $8,223 52 178 $3,924 $427,614
II Min Delay and Stops 25327 3.56 $78 $8,541 52 185 $4,075 $444,111
III Max Ng 25147 3.54 $78 $8,480 52 184 $4,046 $440,962
IV Alt. Max Ng 26338 3.70 $81 $8,882 52 193 $4,238 $461,845
(c) System 1 and System 2, Arterial
I Min Delay 29418 4.14 $91 $9,920 52 215 $4,733 $515,847
II Min Delay and Stops 29140 4.10 $90 $9,826 52 213 $4,689 $510,976
III Max Ng 26907 3.78 $83 $9,073 52 197 $4,329 $471,817
IV Alt. Max Ng 34221 4.81 $106 $11,540 52 250 $5,506 $600,073

83. Assessing Deployments is Essential
and Possible….we just have to do it
I. II. III. IV. V. VI.
Define Assembly relevant Software Modeling Timing Design and Deployment Assess
Objectives, Assess data to support Docu- mentation
and Prioritize timing and docu-
activities by Time mentation
of Day and location objectives
9
8
7 Long Saturday travel
times compared to rest
Measured Travel Time (min)
of week
6
5
4
V/C ratio, Northbound approach Phase 2, Noblesville, 07/25/2006
3
Equivalent Hourly Volume
2 2
Free AM Off- Mid- Off- PM Off- Free
Peak Peak day Peak Peak peak
1
Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun
0
6/15 6/16 6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/24 6/25 6/26 6/27 6/28 6/29
Date/Time
V/C ratio
1
0
0:00 6:00 12:00 18:00 0:00
Time of Day

84. Closing message
• Let’s stop modeling what we can measure.
• Let’s continue to enhance controllers to help us
manage our systems.
• Let’s close the “system performance
measurement” loop.

85. References
• Day, C.M., R.J. Haseman, H. Premachandra, T.M. Brennan, J.S. Wasson, J.R.
Sturdevant, and D.M. Bullock, “Visualization and Assessment of Arterial
Progression Quality Using High Resolution Signal Event Data and
Measured Travel Time,” Transportation Research Board Paper ID:10-
0039, January 2010.
• Bullock, D.M., R.J. Haseman, J.S. Wasson, and R. Spitler, “Anonymous
Bluetooth Probes for Airport Security Line Service Time Measurement:
The Indianapolis Pilot Deployment,” Transportation Research Board Paper
ID:10-1438, January 2010.
• Haseman, R.J., J.S. Wasson, and D.M.Bullock, “Real Time Measurement of
Work Zone Travel Time Delay and Evaluation Metrics,” Transportation
Research Board Paper ID:10-1442, January 2010.
• Day, C.M., J.R. Sturdevant, and D.M. Bullock, “Outcome Oriented
Performance Measures for Signalized Arterial Capacity Management,”
Transportation Research Board Paper ID:10-0008, January 2010.
• Smaglik E.J., A. Sharma, D.M. Bullock, J.R. Sturdevant, and G.
Duncan, “Event-Based Data Collection for Generating Actuated Controller
Performance Measures," Transportation Research
Record, #2035, TRB, National Research Council, Washington, DC, pp.97-
106, 2007.

86. OperationalPicture Book
A Performance Measures and
Outcome Based
Approach
Assessment for Arterial Management
to Traffic Signal
using High Resolution
Controller Data and Bluetooth Probes
Management
Jay Wasson & Jim Sturdevant,
Indiana Department of Transportation
Chris Day, Ross Haseman, Tom Brennan, Alex Hainen,
Steve Remias & Darcy Bullock
Purdue University School of Civil Engineering

88. •SR 37 Free

89. 400
1/14/2010 Clear PM Peak (Vehicle Arrival w/Reference to Upstream BOG)
T&C to Pleasant
350
300
250
Vehicles
200
150
100
50
0
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230
Travel Time (Seconds)

90. 400
1/7/2010 Snow PM Peak (Vehicle Arrival w/Reference to Upstream BOG)
T&C to Pleasant
350
300
250
Vehicles
200
150
100
50
0
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230
Travel Time (Seconds)