Skip to main content
The Research Problem

Data collection of individuals’ travel patterns in a region is an important component in the urban transportation planning process. Through data collection, we are able to observe historical travel patterns, detect emerging trends, predict future travel distributions in time and space, and make investment plans accordingly. Household travel survey is a prime example of this type of data collection. It has traditionally relied upon paper surveys and telephone interviews to gather activity and travel data. These survey methods are increasingly subject to sky-rocketing cost, missing activity and trip records, inadequate data quality, and under-sampling of certain population segments. In public transit, ridership data is collected by counting metrocard swipes at the entrance of a subway or bus. Though this method has definitely surpassed its early generation of manual counts, information on the region’s trip pattern via transit is largely unknown.

The current practice of obtaining such information would again rely on a paper survey or phone interview. The difficulties embedded in these traditional survey methods call for a new way to collect and process data with lower cost, better accuracy and a more complete coverage of the region’s population.

Recent innovations in global positioning systems (GPS) make it possible to collect accurate person-based travel data using personal GPS units. However, little progress has been made in the migration of these data to geographic information systems (GIS) to make the use of personal GPS both practical and efficient in transportation. In a limited number of travel survey experiments, members of the research community have demonstrated the process of using the data to interpret travel patterns of individuals carrying personal GPS units. The GPS data includes data elements that accurately track the location and the time of travel, but not the essential mode information. To obtain mode information, the current practice is to conduct a modified survey on paper, phone, web, or directly on the GPS units. In either format, this modified survey requirement adds an additional responsibility for the user. It is the hypothesis of this research that developing an algorithm to automatically obtain mode information will revolutionalize the current survey practice by significantly reducing survey cost, reaching out to underrepresented populations and maintaining a sufficient sample size. The deployment of an automatic GPS/GIS mode identification procedure will also make a more frequent survey of the region’s travel pattern possible in the near future. A more frequent monitoring of the travel behavior in the region is becoming increasingly important in the current time and future, because of the dynamics of a changing world and heightened security concerns.

This study proposes to develop a “best practice” approach to linking the various geographic data streams from off-the-shelf equipment to end users, including the modeling community and travel behavior analysts. The research will trace the movement of the data streams from the original equipment software data elements (e.g., HDOP, number of available satellites) through the interpretation of modes (e.g., walking, traveling on transit by type, traveling by auto) using an algorithm within a GIS context.

The algorithms will make it possible to accurately diagnose the mode of transit used, adding value for travel surveys for regional models and for transit analysis. A series of experiments will be conducted to develop the algorithms and to test the reliability of the mode assumptions. The findings from this research will contribute to the development of an innovative and cost effective methodology for collecting and processing data to support the planning and management needs of a regional transportation systems in a changing world.

The Research Purpose

The objective of the proposed research is to develop an algorithm in the GIS environment that will take the travel sequence data from personal GPS units and automatically identify the modes of transportation that were used by an individual in time and space. The study will be set in New York Metropolitan Region, where the land use and the extent of transit services are the most intense in the nation. The results of this study will provide transportation agencies (e.g., NYMTC, New York City Transit, Port Authority) with new techniques to exploit during their future survey efforts.

Other Publications Issued from this Research

A GPS/GIS method for travel mode detection in New York City