• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.3
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• pp.457-465
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• 2018

This study proposes a novel approach to explore a "travel time budget (TTB)" using a mobile phone signaling data (MPSD), which are passively generated between a mobile phone and a base station. The data analyzied in this study were provided from KT for 8 days (from May 19 to 26 in 2016). They were about 45 million signals passively generated from users whose stay area during night was classified as three areas in Mapo-gu, Seoul and in the city of Sejong. The estmation of TTB was implemented with various pre-processing techniques on the MPSD data in a data-driven analysis. As a result, the TTBs of Mapo-gu, Seoul and Sejong were 82.94 and 80.70 minutes, respectively. The results in this study were also compared with those based on the traditional methods. The authors expect that this result will help transport experts improve the use of MPSD.

• International Journal of Highway Engineering
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• v.11 no.3
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• pp.13-21
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• 2009

The traveler's travel pattern has significantly changed due to the social and economic changes. The travel time among the traveler's pattern is the limited resource. The travelers are trying to maximize the utility of travel with the least travel cost. So, the travelers travel with their own travel time budget in mind, which they can pay or choose to pay for the optimal maximization of the utility of the individuals. This research is to group and extract the specific factors which affect the travel time budget by utilizing the CART analysis method, which enables the analysis of traveler's characteristics and their interrelationship based on the data collected from "2002 Household Travel Practice Research" and then try to derive a model for estimating the traveler’s travel time budget. The result of CART analysis shows that the factors which affect the travel time budget include the traveler's age, size of house, type of house, type of employment, job and relation to the head of household. Considering the affecting factors derived, I developed an estimation model. From that model, we found that the age, size of house and type of house were positively (+) related to the travel time budget while the homeworking people who have less travel frequency as a type of employment were negatively (-) related to it. In particular, from the point of type of job, the housewives, children not yet old enough to attend schools and people who are working in the agricultural, or marine product industries were found to have the negative (-) value while the people who have the administrative, office, management jobs were found to have the positive (+) value.