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Traffic Modeling

Feasibility Study Problem Definition. Traffic Modeling. Guided by Prof Kavi Arya. Presented by Shivendra S. Meena 01005030. Motivation. Transportation affects Natural Environment Economic Prosperity Social Future Trends of Transportation Unsustainable Resource utilization

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Traffic Modeling

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  1. Feasibility Study Problem Definition Traffic Modeling Guided by Prof Kavi Arya Presented by Shivendra S. Meena 01005030

  2. Motivation • Transportation affects • Natural Environment • Economic Prosperity • Social Future • Trends of Transportation • Unsustainable • Resource utilization • Safety reasons

  3. Transport Management Systems • Traffic Management Systems • Mass Transport Schedulers • Tracking Systems for Fleets • Crisis Management Systems • Traveler Information Systems

  4. Components of TMS • Surveillance and Detection • Loop detectors, GPS/GIS,CCTV etc • Communication and Data Transfer • Ramp meters,VMS etc • Management and Control • Servers,PSAP,IVR etc

  5. Assumptions • Surveillance and Detection • GPS on selected vehicles • Communication and Data transfer • Cellular Network and Its services • Management and Control • Central Answering point • No inter vehicle communication

  6. Classification • Surveillance • Location Specific Detection • Vehicle Specific Detection • Knowledge Processing • Pattern Matching and Analysis • Neural networks • Functional Approach • Exponential average

  7. Case Studies 1. Mumbai Navigator 2. RETINA

  8. Mumbai Navigator • Input • Partial Schedule of Public Transportation System • Output • Generates best plan after taking account of • Frequency of vehicles • Delays in arrival • Knowledge Processing Approach • Probabilistic

  9. Algorithm • Estimation of distance between stops using linear programming on stage length data. • Estimation of time between two consecutive stops using linear programming. • Generates all bus restricted plans by dynamic programming. • Select the plan with minimum expected time

  10. Real Time Traffic Navigation System • Input • Real time locations of vehicle using cellular network • Output • Response to location dependent continuous queries from vehicles

  11. Some Issues • Consistency of distributed data • Functional Temporal Logic • Types of queries • Instantaneous Queries • Continuous Queries • Persistent Queries

  12. Comparison • Static Vs Dynamic • Dynamic model is the need of hour. • Probabilistic Vs Continuous Queries. • Data consistency • Crisis management • Wandering

  13. Conclusion • Functional approach with real time addresses major issues. But... • Probabilistic component is unavoidable.

  14. References • Mayur Datar and Abhiram Ranade. Commuting with delay prone buses. In Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms, pages 22-29.Society for Industrial and Applied Mathematics, 2000. • Dick Hung and Kam-Yiu Lam and Edward Chan and Krithi Ramamritham. Processing of Location-Dependent Continuous Queries on Real-Time Spatial Data: The View from RETINA. In Proceedings of the 14th International Workshop on Database and Expert Systems Applications (DEXA'03), page 961.IEEE Computer Society, 2003. • A. Prasad Sistla and Ouri Wolfson and Sam Chamberlain and Son Dao. Modeling and Querying Moving Objects. In ICDE, pages 422-432, 1997.

  15. Thank You

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