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Traffic Based Pathway Optimization

Michael LeGore TJHSST CSL. Traffic Based Pathway Optimization. Purpose. To create an algorithm to find an optimal path using the heuristic of time. To train the algorithm to avoid traffic hotspots at peak hours. To use traffic data to estimate travel time on roads.

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Traffic Based Pathway Optimization

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  1. Michael LeGore TJHSST CSL Traffic Based Pathway Optimization

  2. Purpose • To create an algorithm to find an optimal path using the heuristic of time. • To train the algorithm to avoid traffic hotspots at peak hours. • To use traffic data to estimate travel time on roads. • To optimize algorithm based on results of analysis.

  3. Background • Path finding algorithms principally use distance as a heuristic. • Many consumers are concerned only with travel time. • Traffic patterns in certain area at certain times can be used to accurately predict travel times.

  4. Background: A* Search • Algorithm to find the optimal path. • Searches through current node and adds each segment to search space. • Iterates over search space until the minimum pathway returned is at the destination. • Uses Heuristic to estimate distance to destination.

  5. Traffic Based Algorithm • Instead of dealing with distance, algorithm finds path based on travel time • Travel Time is estimated from data VDOT • Estimate can take into account contexts such as weather, time of day, and other traffic fluctuations.

  6. Implementation • Ruby for programming • A* Search with a modified heuristic to find path • Use VDOT traffic data to model the average speed. • Feed this data into A* Search • Use it to find the optimal path • Tweak algorithm based on results.

  7. Development • Search Algorithm • Algorithm is an A* search with a modified heuristic. • Interpreting of Traffic Data • Needed to provide a heuristic for travel time • Provides data to estimate the speed of cars. • Speed used to find the time spent on each road segment.

  8. VDOT data • Gives three main factors: • AAWT – Average Annual Daily Traffic • AAWDT – Average Annual Weekday Traffic • K Factor – The ratio of AAWT to design (peak) hour traffic • Ratio of AAWT to AAWDT • K value used to factor in Rush-Hour effects

  9. Limitations of VDOT Data • Only provides data over time • Does not model based on time of day • Gives peak hour traffic concentration but not time of peak hour(s). • Only gives concentrations of traffic • Does not correlate with the TIGER data used for the road shapes

  10. Demonstration • Input Addresses • Input time of day • Returns optimal path for that time of day.

  11. Original Implementation/Plan • Traffic Simulation • Use traffic simulation to feed data back into the algorithm • Keep track of travel times of agents • Unfeasible given time and computing constraints • TIGER data not adequate for an agent based model.

  12. Possible Extensions • Find more complete data for estimating travel time • Integrate more factors into the heuristic • Weather • Holidays • More? • Distributed Approach to data collection • Integrated into GPS devices • iPhone app?

  13. Things I learned • Data collection as a single person is not possible • Writing a sophisticated traffic simulator to model 1 Million+ agents is not at all feasible in the current state of computing • Data for traffic can be very fragmented and not standardized

  14. Conclusion • Time can be a good heuristic for finding a path • An A* search is easily modifiable to accommodate many types of heuristics with different effects • Proper data is necessary for such an algorithm to work

  15. Questions?

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