Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study

The combination of increasing challenges in administering household travel surveys and advances in global positioning systems (GPS)/geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extreme dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.

Research highlights
► Detection of auto and walking modes reaches over 90% in a complex urban environment such as New York City (NYC).
► Identification of trip purposes is 67% and 78% accurate for home-based and non-home-based trips, respectively.
► The accuracy in the detection of transit modes can be significantly improved if real-time transit routes information is available.
► Even though a complete replacement of travel diary by GPS/GIS is currently infeasible, it may be a possibility in the future as technologies advance and algorithms are refined.