Fundamental Diagram Estimation Using Virtual ...

Fundamental Diagram Estimation Using Virtual Detection Zone in Smart Phones’ Application and CCTV Data

Benny Hardjono 1, 2 Rachmad Akbar2, Ari Wibisono2, Petrus Mursanto2, Wisnu Jatmiko2, and Aniati Murni Arymurthy2 1Universitas

Pelita Harapan 2Universitas Indonesia

Presentation at GCCE 2014 9 October 2014, 14.40-15pm at R101B Makuhari Messe

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Traffic jam problem in Jakarta

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Agenda : Introduction Related Works, Defining VDZ & WorkFlow Method Comparison: Conventional FD vs FD using VDZ & CCTV data Field Work & Experiment Set-up Novelty

Cost

Results & Discussion Future work & Conclusions FD=Fundamental Diagram, CCTV=Closed Circuit TV & VDZ=Virtual Detection Zone3

Introduction

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• In the US, the “invoice” of traffic congestion (in 2010 US dollars) : 1982 – $21 billion, 2000 – $79 billion, and 2010, a whopping – $101 billion [37] in 2011 report. (80/30= 2.67 billion US $ per annum) • Great Britain [5], from 2003 to 2004, has to spend 242 million pounds to improve road traffic • As a contrast: Jakarta’s traffic jam has cost the country a loss of 2.95 Billion US dollars per annum [39] • Growth of new road building in Jakarta is only 0.01%.

How can we reduce or avoid traffic jams ? Firstly, we need to have concrete traffic data 4

General Solution Approaches to improve road Accessibility [40] 1. Improving the usage of existing roads OR building new roads 2. Implementing regulations which manage traffic 3. Installing new hardware AND/OR better usage of existing hardware: non intrusive and intrusive devices

Next Non-Intrusive device Intrusive device Source :U.S. Dept of Transportation, Traffic Detector Handbook : Third EditionVolume I, 3rd, vol. I, October. Research, Development, and Technology TurnerFairbank Highway Research Center, 2006, pp. 1–291.

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In summary : Traffic sensors are needed to monitor and obtain concrete traffic data Intrusive sensors are impractical to install on busy roads and expensive Our approach: Combination of multiple nonintrusive traffic sensors (e.g. GPS enabled smart phone-using VDZ system, and existing CCTV), can be used to improve traffic sensor availability, and to obtain concrete traffic data.VDZ & CCTV Experiment was carried out March-Aug 2014, and March-May data, is presented in GCCE 2014.

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Specific sol’n (for smart phone as traffic sensor) Falls in the category of better usage of existing hardware

GPS dedicated devices [1-5]

Open Problems : No traffic jam warning but no privacy problems if GPS data is only received by the sensor

Intelligent Integrated Transport System (ITS) NonIntrusive device with smart phone as Traffic sensor

Phones – non GPS enabled [6], e.g. Cell-ID

Open Problems : Lack of Privacy, VTL method is impractical in Placing 4 ITS using two non intrusive sensors GPS coord.

Open Problems : Inaccurate, Lack of Privacy, Less real time

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[49,32-33]

VTL=Virtual Trip Line

Smart Phones – GPS/A-GPS enabled [7-10, 12]

Internet based e.g. Google Traffic [11], Waze

Open Problems : Lack of Privacy, or Not many roads covered as it depends on Taxies, trucks; Dependence on internet connection, Lacks Local map accuracy 7

VTL Placement

[8, 12]

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Back To Specific Sol’n 8

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Defining via VDZ simulation

Server sends map & preset VDZ data, & App_ID to HP

Agent reads GPS position until it enters the target road with VDZ

Complete Flow chart

VDZ circle video becomes Agent sends info. e.g. green speed, time stamp, again valid Road_ID back to server

displayed on HP: the circle becomes red, as Agent enters VDZ Agent is installed in a GPS enabled, smart phone and carried by the user in a car

green circle means that the car/Agent has not entered VDZ, it continues to read GPS position until it enters the VDZ

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Related Works Cont’d [63] : GPS and CCTV speeds are compared with Ground Truth (GT i.e. Speedometer) in a) while in b) and c) only GPS and GT speeds are obtained because our CCTV system still unable to detect a vehicle in darker surroundings. This experiment was conducted late in the afternoon, while raining and cloudy. a) Speed Comparison 11.31am-12.12pm read from Speedometer Speed (km/hr)

Zone Detection

80 75 70 65 60 55 50 45 40 35

as Ground Truth (GT)

measured from CCTV video

measured from agents/GPS 1

2

3

b)

4

5

c)

Speed Comparison 17.00 - 17.40pm

Speed Comparison 19.30 - 20.15pm read from Speedomete r as Ground Truth (GT)

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read from Speedometer as Ground Truth (GT)

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35

30

measured from agents/GPS

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70

Speed (km/hr)

Speed (km/hr)

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65 60 55

50

measured from agents/GPS

45 40 35

20

1

2

3

4

5

1

2

3

4

5

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Summarized from the Depok experiment GPS and CCTV average speeds are compared with Ground Truth (GT i.e. Speedometer), to obtain % of average speed accuracy (from [63]) 11.31am -12.12pm

17.00 - 17.40pm

19.30 - 20.15pm

% GPS Accuracy*

% GPS Accuracy*

% of GPS

% of CCTV

Accuracy*

Accuracy*

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98.10

93.10

96.522

99.32

2

99.86

77.03

99.756

99.35

3

99.59

79.45

93.410

99.35

4

98.20

98.00

98.619

99.39

5

99.38

85.42

99.806

96.41

99.03

86.60

97.623

98.76

Speed Accuracy Experiment No.

Average Speed Accuracy

Our previous research results in [63] *Compared to GT

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Work Flow Diagram

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FD creation: Traditional way vs VDZ Vehicle Detection Station

Loop Detectors 1 km

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CCTV

VD Zones 1 km Distance Calibration

Estimate Density Estimate Speed by using dynamic G factor

AccurateSpeed Density is obtained from Normalized VDZ data to 1 km Flow = Speed x Density

Fundamental Diagram (FD) is made of traffic density vs flow

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Traffic parameters for FD, are commonly used also in macroscopic model e.g. Cell Transmission Model (CTM) [52] (1)

(2) (3) Motivation Behind : Macroscopic model can be used to simulate traffic conditions. 14

ALL field work photos work upto 16 April 2014 toll JKT-tangerang with exit, entry

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Distance Calibration using Odometer and Measuring stick

Walking stick odometer

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Distance calibration for CCTV photo 1 D C B

137m A

90m 15m

Fig.3. Example of distance calibration from a CCTV photo, with topological features (A-D). These photos are provided freely by jasamarga-live.com. The header, in purple ellipse, on top, consists of km 1.6 (location of cell 23) and the snapshot time 2015-05-24 11:35:52.

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Distance calibration for CCTV photo 2 Header consists of Km 3.5 (location) and the snap-shot date & time (in the left photo shows, 2014-05-02 12:44:04), in the photo provided freely by jasamarga-live.com. Topological features, are encircled and used to measure the distance, from CCTV to bottom edge of photo, and from bottom edge of photo to those features. Distance between CCTV lens and bottom edge is 8m (not shown).

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Trad. vs VDZ

Road Topology of Jakarta Tangerang Topology JKT-Tang 1-3 Experiment Set up

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Assumption : The system is designed for non urban canyon so that GPS will work fine and for high way only / toll road

http://www.jasamarga.com/layanan-jalan-tol/jakarta-tangerang.html

ALL VDZ GPS coord. in google map via gpsvisualizer.com

From google map, gpsvisualizer.com to VDZ app

VDZ data weekdays + 2 cluster times chosen (5-9am & 16-20pm) Time of Weekdays vs Speeds in Cell 1-21

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Time from 5am to 22pm

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Speed in Km/hr

100

80

60

40

20

0 0:00

4:48

9:36

14:24

19:12

Gathered using 7 volunteers for the VDZ system and utilizing existing CCTV

0:00

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Rules of imputation and aggregation[56-58]

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1. Zero speeds or lowest speeds and highest speeds in the zone are considered first. If at exact time (within seconds) of CCTV photo, the speed does not match the density, we consider CCTV photo within 3 minutes 2. Else, if not found, photos within 10 minutes window of the exact time are taken as nearest alternative which corresponds reasonably with the speed 3. Else, we take known values of densities in the same cell by aggregating speed from the same day but a week before or a week after, i.e. weekday of the same time, or taking values at adjacent zones with similar speeds [56] 4. If 1-3, are not possible, then adjacent VDZ zones with known speeds and densities at similar times are considered. This is especially true when there is no CCTV in that cell (i.e. cells 3, 6, 11, 13, 18 and 20)

Samples of FD estimation cell

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Data gathered from our VDZ and CCTV system can help us construct the Fundamental Diagram

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Compare to Traditionally Obtained Fundamental diagrams. 6A) I-880 Southbound Calif. B) Data from Paris

Our approach is able to show zero speeds at jam density

FD parameters into CTM Estimate the fundamental diagrams for each cell or zone i, i = 1..N.

Cell 15 v= 81.9 Km/h w= -34.5 Km/h Jam Density= 74 vpkml Capacity= 1700 vphpl Critical Jam= 24 vpkml

Work in Progress

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FD of 1-21 cells transformed to 24 cells 27

CTM simulation using our toll road data

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CTM simulation using our data

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Future Work and Suggestion

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These issues need to be explored further to make sure that the whole system works well in practice : 1) Our actual data should be compared with the macroscopic simulation (e.g. post km vs. speed time contour) to obtain best fit. 2) To make the VDZ system less prone to privacy attacks, VD zones can be placed more efficiently i.e. more in dense areas than in sparse areas. For further work also, 3) will less number of VDZ usage, mean more efficient in the use of battery power, and efficient in the use of memory ? i.e. no need to place too many VDZ [28]. 4) Will this system really avoid map mismatching phenomenon ? [10][29][30][31][32].

Suggestion: Automated adaptive car counting of existing low resolution photos should be pursued. 30

Conclusions

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1. The system is able to show zero speeds at jam density. 2. Initial results have been promising to estimate Fundamental Diagrams using VDZ and CCTV data, in other words two nonintrusive traffic sensors can be used to construct Fundamental Diagrams. However, in order to do so, VDZ time stamp, server time stamp and photo from CCTV snap shot, should be working together at about the same time 3. Concrete traffic data has been successfully gathered using 7 volunteers for the VDZ system and utilizing existing CCTV data from jasamargalive.com. For Validity, data has been cross checked with 2 other references. 31

Thank you for your attention

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And your knowledge-able feed back will be valuable for me References

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