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Mode Detection Algorithm for GPS-Based Personal Travel Surveys in NYC

This article discusses the development of a mode detection algorithm for GPS-based personal travel surveys in New York City. The algorithm aims to determine the travel mode used based on raw GPS data, considering car, bus, subway, commuter rail, and walk modes. It also addresses challenges like GPS signal distortion in high-density urban areas. The algorithm has shown a 79.1% success rate and has the potential to improve the accuracy and cost-effectiveness of future regional household travel surveys.

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Mode Detection Algorithm for GPS-Based Personal Travel Surveys in NYC

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  1. Development of a Mode Detection Algorithm for GPS-Based Personal Travel Surveys in New York City Evan Bialostozky September 16, 2009

  2. Quick History of Personal Travel Surveys • ongoing shift from “traditional” paper diaries to GPS-based surveys • advantages: • easy, precise collection of travel time, distance, route choice • disadvantages: • trip purpose? • travel mode?

  3. Objectives of Algorithm • determine mode used from raw GPS data • consider 5 modes: • car • bus • subway • commuter rail • walk • account for potential GPS signal distortion in high-density New York City

  4. Urban Canyon Effect: Reason 1

  5. Urban Canyon Effect: Reason 2

  6. Step 1: Division of Data into Trips

  7. Step 2: Division into Trip Segments Assumptions: • underground travel when 2 consecutive points are more than 120s and 250m apart • walk segment at every modal transfer

  8. Step 2: Division into Trip Segments Characteristics of walk segments: • at least 60s long • maximum speed ≤ 10km/h • average speed ≤ 6km/h

  9. Step 3a: Aboveground Subway/Rail Detection

  10. Step 3b: Car vs. Bus How to distinguish a bus from a car? A bus segment: • begins and ends near a bus stop • travels only along bus routes • has a maximum speed lower than 55mph • has a maximum acceleration lower than 1.5m/s2

  11. Step 3c: Signal Gaps

  12. Results • 79.1% success rate • urban canyon effect causes lower success rates in high-density neighborhoods

  13. Benefits for NYMTC Future regional household travel surveys: • more accurate • possibly multi-day data • more cost-effective

  14. Acknowledgments • NYMTC/UTRC • Hongmian Gong (Hunter College, CUNY) • Jorge Argote (NYMTC)

  15. Questions?

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