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Conventional travel-survey methodologies require the collection of detailed activity-travel information, which imposes a significant burden on respondents, thereby adversely impacting the quality and quantity of data obtained. Advances in the global positioning system (GPS) technology have provided transportation planners with an alternative and powerful tool for more accurate travel-data collection with minimal user burden. The data recorded by GPS devices, however, do not directly yield travel information; the navigational streams recorded by GPS devices have to be processed and the travel patterns derived from them. This paper investigates key issues important in the development of a trip-detection algorithm to automate the processing of raw GPS data and to generate outputs of activity-travel patterns in the conventional travel-diary format and to identify trips and characterize them by several attributes (trip-end locations, trip purpose, time of day, distance, and speed). The information is used to develop an algorithm to identify trips and characterize them by specific attributes, which are then validated. 1
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Stored only when both papers are in the corpus — 12 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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