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

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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

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IntroductionObjectives

MethodologyCase StudiesResultsConclusions

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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

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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)

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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

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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

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IntroductionObjectives

MethodologyCase StudiesResultsConclusions

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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

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IntroductionObjectives

MethodologyCase StudiesResultsConclusions

Intersection Pines Blvd. & University Dr.

IntroductionObjectives

MethodologyCase StudiesResultsConclusions

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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

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ResultsIntroductionObjectives

MethodologyCase StudiesResultsConclusions

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Testing of the CMT ResultsIntroductionObjectives

MethodologyCase StudiesResultsConclusions

Results of CMT vs Manual Counts

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Simplistic regression analysis Placemeter VS Manual Counts Underestimate VS Overestimate Confidence of the relationship (CoR)

IntroductionObjectives

MethodologyCase StudiesResultsConclusions

Placemeter Accuracy - Case Study 1

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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

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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