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Deriving Trip's Modes and Trip's Purposes from GPS-based Travel Surveys

出版	2020
URL	http://books.google.com.hk/books?id=oQ8d0AEACAAJ&hl=&source=gbs_api

註釋Mobility data play a crucial role in travel behavior research and demand forecast. The complete reliance on conventional datum collection techniques, that is, face-to-face interview, computer-assisted telephone/web/personal interview, postal survey and email has a number of big drawbacks, including (1) high burden on respondents, thus high non-response rate, (2) inclusion of one-day data per person, (3) lack of reliability due to human memory limits and habit of rounding travel time, (4) high cost with intensive labor and (5) big time gap between periodic household travel surveys not to mention the difficulties in combining and harmonizing data of surveys in different regions or countries. The unlimited use of Global Positioning System (GPS) has opened up great opportunities for dealing with the problem of poor data. GPS logs are objective, numerous, continuous, detailed and accurate spatiotemporally. Yet, positioning information itself is not eligible for analysis due to the lack of trip characteristics. This deficiency has induced the substantial development of two new research fields that are involved in imputing transportation modes and trip purposes from GPS data, respectively. On-board devices were initially utilized to take advantage of electricity. Afterwards, lightweight, small and wearable personal devices have been developed to collect data at person level, which emphasized the need of detecting trip modes. Currently, smartphone is the most preferred devices to gather both logs and their corresponding so-called ground truth. Detections of mode and purpose are essential steps prior to do any travel behavior analyses (e.g. mode choice or time spending in activities). In this sense, the performances of mode and purpose inference algorithms determine the potential of employing GPS-based surveys as a supplement and even an entire alternative to conventional techniques. In the literature, there are three research gaps related to imputation algorithms and GPS-assisted surveys. The first is the great focus of investigations in well-structured urban areas of developed countries and occasionally in China. Therefore, the use of GPS in mobility surveys in cities of developing countries has been questionable. The others are consequences of the first limitation. Second, the list of mode detection encompasses walk, bike, transit and car but not motorcycle that is one of the main means in emerging countries. Last, purpose imputation has been implemented very frequently with the support of GIS data; however, GIS data are not available and good enough everywhere. Lack of reasonable solutions to derive purposes from GPS data without GIS data is a gap. This thesis aims at seeking answers to three mentioned-above existing questions by building both mode and purpose inference models. Two data sets were used. The first was collected in Rhone-Alpes, France by dedicated device whilst the second was gathered in Hanoi, Vietnam by smartphone. Based on prediction results, the discussions and recommendations for enhancing the quality of GPS-based surveys in general and for developing countries in particular have been proposed.