Kathmandu Valley Intelligent Traffic System

(KV-ITS)

Bikash Maharjan (16207)Bikram Thapa (16208)Roshan Manandhar (16228)Yugesh Shrestha (16246)

SupervisorDr. Arun K. Timalsina

Co-SupervisorMr. Saroj Yadav, EB pearls

Traffic Jam => Problem

System Overview

iPhone Application

Web Interface

Database

Image Processing

Past Data Analysis

Wireless Traffic Data

Generated Traffic Information

System

Display

CCTV Live Video

Input Input

Database

Update Jam

Update Road Condition

Data Visualization

Input

Web Interface

Input

Generated Traffic Data

Web Interface Implementation

Image Processing Overview

Image ProcessingAlgorithms

•Traffic Density •Traffic Flow Rate

CCTV Video Data

Outputs

Extract Frameinput

Traffic Density Traffic Flow Rate

free

Congestion Levels

High

Moderate

slight

DatabaseStore

FuzzyClassifier

inputs

outputs

Image Processing Overview

Traffic Density Estimation

. . .

. .

Consecutive Frame Difference

+ + =

. . . . . . .

Accumulate Differenced Images

Detected Lane Model

D1 D1+D2 Dn-1 + Dn

Lane Detection

Video Frame Sequence

Traffic Density Estimation

(Area=No. of white pixels)lane

vehicle

A

ADensity

Lane Mask Model

. . . .

1.Mask

1.Mask

3. Output after difference at T sec

.

.

Input Video Data

2.Difference

Background Model

Traffic Flow Rate Estimation

N frameN+5 sec frame

Video Sequence

1. Find Feature Points 2. Track Feature PointsMain Processes:

secT

DV

mean

n

dDmean

Traffic Flow Rate Estimation

Results

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 610

0.2

0.4

0.6

0.8

1

1.2

Optical Flow Rate and Density Vs Time

Optical Flow

Density

Time (5 seconds interval)

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 610

0.10.20.30.40.50.60.70.80.9

Congestion Level Vs Time

Congestion value

Free

highModerate

At Baneshwore

Time ( 5 sec interval)

Results

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 610

0.10.20.30.40.50.60.70.80.9

Congestion Level Vs Time At Baneshwore

Congestion value

Free

highModerate

Time ( 5 sec interval)

So Far

Web Interface

Database

Image Processing

Wireless Traffic Data

Generated Traffic Information

System

iPhone Application

Display

CCTV Live Video

Input Input

Information Mapping

Congestion Status

Updated CCTV image

Reason/Details

Live Streaming

Shortest path based on real time weights

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

JwalakhelNabil Bank

BhadrakaliSinghadurbar

New Baneshwor

Tinkune

New Plaza

Anamnagar

BICC

BirHospital

Start

Destination

95

4

2

2

11

2

1

1

1

6

10

411 1

Shortest path = 22 min

9:30 am

Practical scenario

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

JwalakhelNabil Bank

BhadrakaliSinghadurbar

New Baneshwor

Tinkune

New Plaza

Anamnagar

BICC

BirHospital

Destination

95

4

2

2

11

2

1

1

1

6

10

411 1

6

5

2

5

6

1 1

3

35

Shortest path = 33 min Next shortest path = 30 min

Start9:30 am

18 mins

Best Path Algorithm

• Successfully designed and developed• It first estimates the time to reach the node

then it uses the predicted weight at that estimated time to update the graph dynamically.

• but it’s level of accuracy completely depends upon the level of prediction analysis.

Best Path AlgorithmDestination

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

9:30 amStart

Best Path Algorithm

Start9:30 am

Destination

9:36

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

Best Path Algorithm

Start9:30 am

Destination

9:369:39

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

32

Best Path Algorithm

Start9:30 am

Destination

9:369:39

9:41

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

2

22

3

Best Path Algorithm

Start9:30 am

9:369:39

9:419:42

Destination

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

2

22

3

2

Best Path Algorithm

Start9:30 am

9:369:39

9:419:42

Thapathali

Maitghar

Kupondole

Teku

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

2

22

3

2

Destination9:44

Best Path Algorithm

Start9:30 am

9:369:39

9:419:42

9:43

9:459:46

Destination9:44

Thapathali

Maitghar

Kupondole

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

2Teku

22

2

2

11

3

Best Path Algorithm

Teku Start9:30 am

9:369:39

9:419:42

9:43

9:459:46

Thapathali

Maitghar

Kupondole

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

Destination9:44

3

2

22

2

2

11

3

Best Path Algorithm

Teku Start9:30 am

9:369:39

9:419:44

9:43

9:459:46Destination

9:47

Thapathali

Maitghar

Kupondole

Tripureswor

Sahid-gate

Patan Dhoka

PulchowkNabil Bank

BhadrakaliSinghadurbar

62

2

1

1 1

1

1

1

5

1

1

3

2

24

6

2

11

5

Kupondole-Thapathali edge

Kupondole

Tripureswor

Maitighar

Thapathali

Characteristic delay behavior of Thapathali node for Kupondole-Thapathali edge

Comparison of polynomial equation obtained with the real time test data collected on Bhadra 11

8:55 9:15 9:40 10:05 10:30 10:55 11:20 11:45

Time starting from 8:55 am to 11:45 am

Total Weight on edge

Average Speed=30 km/hr.

𝑇𝑜𝑡𝑎𝑙 𝑇𝑖𝑚𝑒𝑒𝑑𝑔𝑒=𝐴𝑣𝑔𝑇𝑖𝑚𝑒𝑒𝑑𝑔𝑒+𝑁𝑜𝑑𝑎𝑙 𝐷𝑒𝑙𝑎𝑦

Implementation

CCTV Cameras

Web Server

Image Processing

Server

CCTV Video Broadcasting

Server

Web Server

Thank You