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2nd Presentation PresenterBratislav Ostojic• Graduate Research Assistant at the College of Civil Engineering
and Computer Science at Florida Atlantic University.• Holds a BSc degree from the Faculty of Traffic and
Transportation Engineering in Belgrade, Serbia and a MSc Degree from the same university in the Air Transportation.
• Research interest: Intelligent Transportation Systems, microsimulation, autonomous and connected vehicles, pedestrian and cyclists flows.
• Secretary of ITE FAU student chapter.
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Introduction Need for safer and convenient facilities for pedestrians and
bicyclists Manual traffic data collections are time and resource intensive Future belongs to crowd-monitoring technologies Performance measures - tools for monitoring and evaluating Scope - Southeast Florida
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IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Objectives Deriving performance measures based on the available
counts. Assessment of Crowd Monitoring Technologies (CMT) for
their feasibility to collect data of the pedestrians and bicyclists
Creating spreadsheet tool that will simplify PM’s computation
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IntroductionObjectives
MethodologyCase StudiesResultsConclusions
4
Pedestrian Performance Measures
At the Intersection Corner Circulation Area Crosswalk Circulation Area Pedestrian Delay Pedestrian LOS ScoreOn sidewalk Average Pedestrian Space Sidewalk Pedestrian LOS Score
Bicycle Performance Measures
Bicycle LOS Score for Intersection Bicycle Delay
Performance measures
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Crosswalk circulation area
𝑀𝑀𝑐𝑐𝑐𝑐 =𝐿𝐿𝑑𝑑𝑊𝑊𝑑𝑑𝑔𝑔𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊,𝑚𝑚𝑚𝑚 − 4.0
𝑉𝑉𝑊𝑊𝑙𝑙,𝑝𝑝𝑝𝑝𝑝𝑝𝑚𝑚 + 𝑉𝑉𝑝𝑝𝑙𝑙 − 𝑉𝑉𝑝𝑝𝑙𝑙𝑟𝑟𝑝𝑝3,600 𝐶𝐶 𝑊𝑊𝑑𝑑
3.2 + 𝐿𝐿𝑑𝑑𝑆𝑆𝑝𝑝
+ 2.7 𝑉𝑉𝑑𝑑𝑟𝑟3,600 𝐶𝐶 (
𝐶𝐶 − 𝑔𝑔𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊,𝑚𝑚𝑚𝑚𝐶𝐶 ) 𝑉𝑉𝑑𝑑𝑟𝑟
3,600 𝐶𝐶 + 3.2 + 𝐿𝐿𝑑𝑑𝑆𝑆𝑝𝑝
+ 2.7 𝑉𝑉𝑑𝑑𝑚𝑚3,600 𝐶𝐶 (
𝐶𝐶 − 𝑔𝑔𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊,𝑚𝑚𝑚𝑚𝐶𝐶 ) 𝑉𝑉𝑑𝑑𝑚𝑚
3,600 𝐶𝐶
Pedestrian Delay
𝑑𝑑𝑝𝑝 = (𝐶𝐶−𝑔𝑔𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊,𝑚𝑚𝑚𝑚)2
2𝐶𝐶
Pedestrian LOS Score for Intersection
𝐼𝐼𝑝𝑝,𝑚𝑚𝑖𝑖𝑙𝑙
= 0.5997 + 0.681 𝑁𝑁𝑑𝑑 0.514 + 0.00569𝑉𝑉𝑝𝑝𝑙𝑙𝑟𝑟𝑝𝑝 + 𝑉𝑉𝑊𝑊𝑙𝑙,𝑝𝑝𝑝𝑝𝑝𝑝𝑚𝑚
4 − 𝑁𝑁𝑝𝑝𝑙𝑙𝑐𝑐𝑚𝑚,𝑑𝑑 0.00270.25𝑁𝑁𝑑𝑑
�𝑚𝑚∈𝑚𝑚𝑑𝑑
𝑉𝑉𝑚𝑚 − 0.1946
+ 0.00013 𝑛𝑛15,𝑚𝑚𝑚𝑚 𝑆𝑆85,𝑚𝑚𝑚𝑚 + 0.0401 ln(𝑑𝑑𝑝𝑝,𝑑𝑑)
Performance Measures - Formulation
5
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
6
Average Pedestrian Space
𝐴𝐴𝑝𝑝 = 60𝑆𝑆𝑝𝑝𝑣𝑣𝑝𝑝
Bicycle Delay
𝑑𝑑𝑏𝑏 =0.5 𝐶𝐶 (1−𝑔𝑔𝑏𝑏𝐶𝐶 )2
1−𝑚𝑚𝑚𝑚𝑖𝑖𝑉𝑉𝑏𝑏𝑚𝑚𝑏𝑏𝑏𝑏𝑏𝑏
,1.0 𝑔𝑔𝑏𝑏𝐶𝐶
Bicycle LOS Score at the Intersection
𝐼𝐼𝑏𝑏,𝑚𝑚𝑖𝑖𝑙𝑙 = 4.1324 + 𝐹𝐹𝑐𝑐 + 𝐹𝐹𝑣𝑣
Where,𝐹𝐹𝑐𝑐 = 0.0153 𝑊𝑊𝑐𝑐𝑑𝑑 − 0.2144 𝑊𝑊𝑙𝑙
𝐹𝐹𝑣𝑣 = 0.0066𝑣𝑣𝑊𝑊𝑙𝑙 + 𝑣𝑣𝑙𝑙𝑡 + 𝑣𝑣𝑝𝑝𝑙𝑙
4 𝑁𝑁𝑙𝑙𝑡
𝑊𝑊𝑙𝑙 = 𝑊𝑊𝑟𝑟𝑊𝑊 + 𝑊𝑊𝑏𝑏𝑊𝑊 + 𝐼𝐼𝑝𝑝𝑊𝑊 𝑊𝑊𝑟𝑟𝑜𝑜 ′
Performance Measures – Formulation Cont.
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Illustration of Measured Variables
7
Vrt
Vbic
Vth1
𝑽𝑽𝒅𝒅𝒅𝒅/𝒊𝒊𝑽𝑽
𝒄𝒄𝒅𝒅/𝒊𝒊
Vlt
Vth2
Vp
Vrt1
Vlt-perm𝑽𝑽𝒕𝒕𝒕𝒕𝒕𝒕 = through
demand flow rate (veh/h)
𝑽𝑽𝐭𝐭𝐭𝐭𝟏𝟏 = through demand flow rate
(veh/h)𝑽𝑽𝒍𝒍𝒕𝒕 = lelt-turn demand flow rate
(veh/h)
𝑽𝑽𝐫𝐫𝒕𝒕 = right-turn demand flow rate
(veh/h)
𝑽𝑽𝒍𝒍𝒕𝒕−𝒑𝒑𝒑𝒑𝒑𝒑 = Permitted left-
turn demand flow rate (veh/h)
𝑽𝑽𝐫𝐫𝒕𝒕 = right-turn demand flow rate
(veh/h)
𝑽𝑽𝒃𝒃𝒊𝒊𝒄𝒄 = bicycle flow rate
(bicycles/h)
𝑽𝑽𝐝𝐝𝐝𝐝/𝒊𝒊
= Flow rate of pedestrian arriving at the corner each cycle
to/after cross the major street (p/h);
𝑽𝑽𝐜𝐜𝒅𝒅/𝒊𝒊
= Flow rate of pedestrian arriving at the corner each cycle
to/after cross the minor street (p/h);
𝑽𝑽𝒑𝒑 = Flow rate of pedestrian (p/h)
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Crowd Monitoring Technologies Simplified and affordable solution? Provides opportunity for detection of possible problems Different computer vision technologies using live streams
considered Placemeter Inc was selected to provide traffic counts Placemeter delivers a variety of measurements :
–Pedestrians (Volume, Walking Direction, Store visits) –Automobiles (Volume, Direction)–Bicycles (Volume, Direction)
8
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Video footage from public cameras, smart phones or sensors Algorithms process trillions of data points simultaneously Deliver data to the user in daily, hourly and 15 minute
intervals
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PlacemeterIntroductionObjectives
MethodologyCase StudiesResultsConclusions
10
Placemeter Video footage from public cameras, smart phones or sensors Algorithms process trillions of data points simultaneously Deliver data to the user in daily, hourly and 15 minute
intervals Online dashboard
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Case Studies - Region
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IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Case Studies – Individual Intersections1. Intersection Oakland Park Blvd. & SR 7 2. Intersection Broward Blvd. & SR 73. Intersection Pines Blvd & University Dr.4. Intersection Pines Blvd & Flamingo Rd.5. Young Circle (3 locations considered)
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1
2
34
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
5
Case Studies Examples1. Intersection Oakland Park Blvd. & SR 7
3. Intersection Pines Blvd & University Dr.
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IntroductionObjectives
MethodologyCase StudiesResultsConclusions
1
3
Intersection Oakland Park Blvd. & SR 7
14
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Intersection Pines Blvd. & University Dr.
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
16
Results – Spreadsheet Format
Values that must be entered in order to calculate all available performance measuresIntersection OPB/US441 Cycle length 160 Effective Walk Time ↙ 14 Walking Speed 3.3
Time period start Crosswalk Circulation Area Category Pedestrian Delay (s/p) Pedestrian LOS Score for the intersection Category 19-05-16 07:00 78 A 69 1.56 B19-05-16 08:00 34 C 69 1.36 A19-05-16 09:00 102 A 59 1.68 B19-05-16 10:00 52 B 59 1.74 B19-05-16 11:00 37 C 59 1.74 B19-05-16 12:00 49 B 59 1.67 B19-05-16 13:00 63 A 59 1.65 B19-05-16 14:00 41 B 59 1.57 B19-05-16 15:00 33 C 59 1.55 B19-05-16 16:00 215 A 69 1.55 B19-05-16 17:00 27 C 69 1.55 B19-05-16 18:00 48 B 69 1.46 A19-05-16 19:00 22 D 59 1.76 B19-05-16 20:00 193 A 59 2.22 B
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
17
ResultsIntroductionObjectives
MethodologyCase StudiesResultsConclusions
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Testing of the CMT ResultsIntroductionObjectives
MethodologyCase StudiesResultsConclusions
Results of CMT vs Manual Counts
19
Simplistic regression analysis Placemeter VS Manual Counts Underestimate VS Overestimate Confidence of the relationship (CoR)
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Placemeter Accuracy - Case Study 1
20
Vdo/i
Vth1
Vrt
Vrt1 Vdo/I – underestimates for 32 %. CoR is 53.8 %
Intersection Oakland Park Blvd. & SR 7
Vrt – underestimates for 18.5 %. CoR is 64.5 %
Vrt1 – underestimates for 32 %. CoR is 53.8 %
Vth1 – traffic counts not collected
Vdo/I – underestimates for 10.5 %. CoR is 51.3 %
Vlt – underestimates for 18.9 %. CoR is 74.3 %
Vlt-perm – underestimates for 34 %. CoR is 75.6 %
Vth2 – traffic counts not collected
Vlt
Vlt-perm
Vth2
Vdo/i
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
21
Intersection Pines Blvd & University Dr.
Vdo/I – underestimates for 32 %. CoR is 53.8 %
Vrt – underestimates for 18.5 %. CoR is 64.5 %
Vth1 – traffic counts not collected
Vlt – underestimates for 18.9 %. CoR is 74.3 %
Vbic – underestimates for 18.9 %. CoR is 74.3 %
Vth1 VltVrt Vdo/i
Vbic
Placemeter Accuracy - Case Study 2
IntroductionObjectives
MethodologyCase StudiesResultsConclusions
Placemeter Accuracy
Placemeter overestimates/underestimates true counts when cameras are not specifically set to support Placemeter measurements Accuracy varies within a range of ± 30% Inaccuracy occurred due to:
- all measurement lines do not have same angle view- traffic “noise”- vehicle high beams or vehicle shadow - camera blindness/Sun glare- blurry live streams
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IntroductionObjectives
MethodologyCase StudiesResultsConclusions