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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_193.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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recording omissions when people forget to do that. Such recording omissions inevitably become noticeable when a survey is implemented for a longer period and in more detail. However, recording detailed travel behavior for a long period is indispensable for better understanding of travel behavior and analyzing the dynamics of that behavior. To solve such problems, Hato (2006) proposed a method for identifying travel activities by using infor- mation from multiple sensors, with the aim of enabling the achievement of long- term observations by completely eliminating the act of recording by subjects. Hato has already developed a small, portable travel- activity measuring instrument that requires no entry by subjects. Conventional surveys have collected identifica- tion information such as facility type, transport mode, and activity content through the operation of instru- ments, questionnaires, and the like. However, these com- plicated surveys burden the subjects and rely on their memory, problems often leading to recording omissions or incorrect records. Hato proposed a method for esti- mating behavioral contexts by using behavioral- context addressable loggers in the shell (BCALS), a wearable, behavioral- context information- measuring instrument, for reestimating label information, such as facility type and transport mode, from ecological and environmental sensors that are based on learning models. Figure 4 shows the BCALS used in the present study, and Table 1 lists the data to be acquired. Acceleration information is used for identifying the transport mode. Atmospheric pressure is used in combination with ultraviolet rays for judging the floor level and whether the person is indoors or outdoors. Sound and temperature are used for identi- fying the behavior content. Here are some examples of measurement results from the sensors. Figure 5 shows the changes in acceleration of each transport mode. Walking has the largest variabil- ity in acceleration and is followed by bicycling, motor- bike, and automobile. It shows the possibility of identifying transport modes on the basis of the magni- tude of acceleration without asking subjects. Figure 6 is a record of acceleration variability of subjects in coffee shops and CD shops. It shows that, in coffee shops, sub- jects move only when the menu is given by a waiter or waitress or when they try to drink water in a cup, and the accelerations at these occasions have been recorded. In contrast, in CD shops, subjects often move around looking for CDs, and the variability in acceleration has been observed. Most of the cases that show no variabil- ity in acceleration probably indicate that such actions include listening to a CD at a set location or paying at the cash register. Thus, it is possible to record detailed behaviors of subjects. Furthermore, Figure 7 shows changes in atmospheric pressure. Every time a subject changes floors or visits a different facility, the atmospheric pressure changes con- siderably. The floor of an activity can be identified from data on atmospheric pressure. A small logger equipped with multiple sensors has been introduced. Acceleration and sound are effective for identifying activity content and location. (They also enable capturing the number of steps and are effective for evaluating walking environments.) Moreover, atmos- 193 DATA- ORIENTED TRAVEL BEHAVIOR ANALYSIS FIGURE 4 Exterior view of BCALS. TABLE 1 Data to Be Acquired Data (Numerals Indicate Geographic Points of Observation) Bytes X- axis acceleration (32 Hz) 2 Y- axis acceleration (32 Hz) 2 Z- axis acceleration (32 Hz) 2 Atmospheric pressure sensor (32 Hz) 2 Angular velocity (32 Hz) 2 Ultraviolet ray (32 Hz) 2 Direction (32 Hz) 2 Sound (10 Hz) 2 PS location (latitude, longitude, altitude, velocity, direction) (1 Hz) 23 Elliptical error of GPS location measurement 15 NOTE: 88-day continuous recording (battery duration: about 3 days). OS is TRON (activity identification programs can be embedded or rewritten in C). FIGURE 5 Measurement results of changes in acceleration and noise.

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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