INTEGRATING GEOGRAPHIC INFORMATION SYSTEMS WITH TRANSIT SURVEY METHODOLOGY
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- planning - surveys, planning - signage/information, land use - planning, ridership - forecasting, ridership - forecasting, ridership - demand, technology - geographic information systems, mode - mass transit; Weighting; Validation; Travel surveys; Travel models (Travel demand); Travel demand; Transportation planning; Transit; Spatial analysis; Software validation; Scenarios; Public transit; Projections; Origin and destination; Orange County Transportation Authority; O&D; Mass transit; Local transit; GIS; Geographic information systems; Geocoding; Forecasting; Data quality; Data integrity; Accuracy
Literature demonstrating how to use geographic information systems (GISs) effectively for transit origin-destination (O-D) data analysis is rather inadequate to date. Other than geocoding O-D survey data to point locations, not much attention has been accorded GIS as either a tool for checking survey data quality or for validation of data analysis. The purpose here is to demonstrate how the Orange County Transportation Authority applied GIS technology effectively to project transit passengers' mobility patterns with greater accuracy and consequently strengthen the validation database for travel demand forecasting analysis with respect to transit planning. In addition to the use of traditional statistical methods for weighting procedures, this study relied on analytical GIS functions to achieve the following two objectives: (a) incorporation of the spatial element while expanding the weighting factors, and (b) validation of the weighted O-D survey returns with passenger counting data derived from other independent data sources. This project demonstrates both the usefulness of GIS technology in its ability to improve the quality of travel survey data and its value in combination with weighting-factors expansion methodology. In addition to the traditional statistical weighting methodology, a spatial component was incorporated as a criterion for enhancing the expansion results of the O-D data. Furthermore, comparative analysis with transit passenger counts and transfer data volumes were used to validate the findings of the weighting methodology. The results support the conclusion that GIS can greatly improve O-D data quality and statistical representation.