Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests (2014) / Chapter Skim
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Pages 44-51
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44 Introduction Chapter 1 discussed several methods for processing and deriving information from GPS traces in the context of HTSs, as well as a wide range of applications of GPS data in the development of transportation models. Also, the research identified the need for guidelines in the processing and archiving of GPS-derived travel survey data.
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45 Without this contextual information foundation, it becomes very challenging to develop the analytical models that provide the foundation for modern TDMs. The overall trend in travel model development today is to apply individual behavioral models that explain the outcome (i.e., travel by such dimensions as origin, destination, mode, and time of day)
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46 data sets collected by stand-alone GPS data loggers deployed as part of the study; and (2) smartphone-collected GPS data collected in tandem with a household travel survey.
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47 Data Fusion and Transferability There has been extensive research in recent years on the transferability of travel attributes of individuals from one context to another. Travel attributes like number of trips, distance traveled, and modes used for each individual are critical requirements in any disaggregate travel demand analysis, and data transferability approaches are seen as reliable alternative solutions for smaller communities where data collection is more costly and challenging.
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48 the individual traces into a sequence of trips. The major steps of behavior-ization include identification of individual trips, trip modes, purposes, and activity types.
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49 Identifying Behavior from GPS Traces The first challenge to overcome when extracting behavior from GPS data is to clean and process it into trips and activities. Performing these types of tasks can take significant effort when processing raw GPS data from emerging sources such as smartphones and wearable (and continuously powered)
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50 ing trip purpose to various trip and person attributes. This is a method similar to the one proposed in Chen et al.
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51 The overall performance of the methods was evaluated by comparing their results with the responses reported in the original data. In the case of the HTS data sets, these responses came from the set of GPS trips that were matched to the traditionally reported travel.
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