This slide is about the intelligent traffic prediction system using Cellular Networks.

1. Presentation on Road Traffic
Information using Cellular Networks
Presented By
Engr. Md. Tanzir Ahsan
Id-012152019

2. Road Traffic Information
 INTRODUCTION
 VIDEO SURVEILLANCE SYSTEMS
 TRAFFIC MONITORING ISSUES
 ROAD MAP & TRAFFIC DETECTION
 CONVENTIONAL & NEW TRAFFIC DATA SOURCES
 NEW DATA PROVIDERS
 VEHICLE TRACKING STRATEGIES
 A REAL TIME SYSTEM EXPLANATION
 DRAWBACKS

3. INTRODUCTION
• ROAD TRAFFIC CAN BE MONITORED BY MEANS OF STATIC SENSORS AND DERIVED
FROM FLOATING CAR DATA.
• THE TRAFFIC INFORMATION OF ROAD CAN BE COLLECTED IN A VARIOUS WAYS,
SUCH AS
 USING ROAD SIDE DETECTORS
 PUBLIC CALLERS
 FLOATING CAR DATA
 LICENSE PLATE MATCHING

4. Video surveillance
 Present Implementations
 Human detection systems.
 vehicle monitoring systems.
 Advantages of video surveillance
 Keep track of information video data for future use.
 Helpful in identifying people in the crime scenes etc..
 Disadvantages of the present system
 It’s difficult to maintain heavy amount of raw video data
 Human interaction.
 Require higher bandwidth for transmitting the visual data.

5. Need for Traffic Monitoring
 To reduce the traffic congestion on highways
 Reduce the road accidents
 Identifying suspicious vehicles. Etc..

6. Traffic Monitoring
 Traffic scene analysis in 3 categories.
 A strait forward vehicle detection and counting system .
 Congestion monitoring and traffic scene analysis.
 Vehicle classification & tracking systems which involve much more detailed scene traffic
analysis.

7. Road Map and Traffic Detection
Uses existing Detection Systems
Traffic: qCar, qHGV, vCar, vHGV (per time for every lane)
Road Map: Distance between adjacent cross sections (= section),
location of ascents, descents, entrances, exits
100100
Central Traffic ComputerLocal ComputerTraffic Detection
Traffic Sign Gantry
Environmental
Detection

8. Substitute Values
Substitute missing Data by Time-Distance Traffic Forecast
 use previous cross section to forecast traffic on main and exit lanes
 use following cross section to forecast traffic on entrance lanes
 use historical data when neighbor cross section not available
8 0 ! 8 0
q qCar Hgv
v vCar Hgv
100100
1 2
3
Time Distance Forecast
 Detect ariving vehicles at
cross section,
 Trace vehicles during the
sector by using detected
vehicle speed,
 calculate number of cars,
that arrive at subsequent
cross section for
observed time interval.

9. CONVENTIONAL DATA SOURCES
 ROAD TRAFFIC SENSORS
• INDUCTIVE LOOPS
• PNEUMATIC TUBES
• PIEZO-ELECTRIC SENSORS
• VEHICLE CLASSIFIERS
 HOUSEHOLD TRAVEL SURVEY
 TRAFFIC CAMERAS
 FLOATING CAR SURVEYS

10. NEW TRAFFIC DATA SOURCES
 ROAD-SIDE TRAFFIC SENSORS
• ACOUSTIC SENSORS
• MICROWAVE RADAR
• LIDAR AND ACTIVE INFRARED (SIDE-FIRE) SENSORS
• VIDEO IMAGE DETECTION
• RADIO FREQUENCY IDENTIFICATION (RFID) TAGS
 GPS-BASED HOUSEHOLD TRAVEL SURVEYS
 MOBILE PROBE-TYPE DATA SOURCES

11. GPS DEVICE ARCHITECTURE
Fig: GPS Device Architecture

12. CELLULAR GSM DATA AND TRAVEL ROUTE
DETERMINATION
Fig: A theoretical route (tessellated cell towers) Fig: A realistic route tessellated from cell towers

13. Fig: Traffic monitoring architecture

14. OTHER ISSUES
MASS COLLECTION OF TIME-STAMPED POSITION DATA WILL REQUIRE NEW
METHODOLOGIES FOR ITS STORAGE, INTERROGATION AND SUMMARIZATION.
 SAMPLING (AND DATA ACCURACY)
 POST-COLLECTION STATISTICAL ANALYSIS
 INFORMATION PRIVACY
 INTELLECTUAL PROPERTY RIGHTS
 STORAGE

15. ‘NEW DATA’ PROVIDERS AND NEW DATA
INITIATIVES
THERE ARE ALSO A RANGE OF CROWD-SOURCING PLATFORMS ENABLING ROAD
USERS TO SHARE TRAFFIC INFORMATION FROM MOBILE DEVICES, E.G. WAZE,
BEATTHETRAFFIC, WHICH ARE ALSO INCLUDED AS A SOURCE.
 IN AUSTRALIA, THERE ARE SOME COMPANIES PROVIDING CONVENTIONAL
TRAFFIC DATA COLLECTION SERVICES (E.G. AUSTRAFFIC AND SKYHIGH TRAFFIC
DATA) USING VARIOUS LEVELS OF TECHNOLOGY

16. PRIVATE SECTOR TRAFFIC DATA PROVIDERS
• WORLD-WIDE DIGITAL TRANSPORT DATA AND INFORMATION SERVICE
PROVIDERS —INCLUDING APPS. HERE (WWW.HERE.COM, FORMERLY
NAVTEQ/NOKIA MAPS)
• INRIX (WWW.INRIX.COM)
• AIRSAGE (WWW.AIRSAGE.COM)
• TOMTOM (WWW.TOMTOM.COM)
• GOOGLE (MAPS.GOOGLE.COM) – INCLUDING WAZE (APP) (WWW.WAZE.COM)
• SUNA GPS TRAFFIC UPDATES (HTTP://WWW.SUNATRAFFIC.COM.AU)
• NAVIGON USA ( APP) (HTTP://WWW.NAVIGON.COM/PORTAL/SITES.HTML)
• MOTIONX GPS DRIVE ( APP) (HTTP://NEWS.MOTIONX.COM)

17. Vehicle detection techniques
 Model based detection
 Region based detection
 Active contour based detection
 Feature based detection

18. A REAL TIME TRAFFIC MONITORING SYSTEM
Feature based tracking
algorithm
•Camera calibration
•Feature detection
•Vehicle tracking
•Feature grouping
Benjamin Coifman, Jitendra Malik, David Beymer

19. DRAWBACKS
 GSM NETWORKS DON’T WORK WELL IN THE URBAN AREAS
 TEMPORARY FAULTS OF SENSOR
 SENSOR DATA ARE MISSING FOR SOME DAYS DUE TO THE PROBLEM IN THE
RECORDING SYSTEM
 TOLL DATA ARE PARTLY MISSING ON WEEKENDS & HOLIDAYS

20. CONCLUSION
RECENT AND EMERGING TECHNOLOGIES OFFER SIGNIFICANT OPPORTUNITIES FOR
COLLECTING MORE INFORMATION, MORE COST EFFECTIVELY, ABOUT PERSONAL
TRAVEL ACTIVITY AND ROAD USE, THAT CAN BETTER INFORM DAY-TO-DAY
NETWORK MANAGEMENT, LONG-TERM INFRASTRUCTURE PLANNING AND ROAD
USER TRAVEL CHOICES. GPS, GSM AND BLUETOOTH TECHNOLOGIES PROVIDE
OPPORTUNITIES FOR COLLECTING MORE INFORMATION ABOUT WHEN AND WHERE
VEHICLES USE THE ROAD NETWORK.

21. Thank You
Any Questions