• Journal of Korean Society of Transportation
• /
• v.29 no.6
• /
• pp.7-16
• /
• 2011

This study investigates the degree of dependency on freeways when auto travelers make route choices in the Seoul Metropolitan Area. The investigation is conducted based on aggregated auto trip data, defining the degree of dependency as the proportion of auto trips selecting freeways in their travel paths. The analyses reveal that the trips departing from the areas with higher accessibility to freeways tend to exhibit higher dependency on freeways. In addition, the dependency is significantly affected by the travel time differences between two paths including and excluding freeways, respectively. The number of service interchanges was found to be one of significant factors for trips to Incheon and Gyenggi areas. The finding indicates that the factors affecting the degree of dependency on freeways may vary depending on the areas' characteristics. The findings would enhance the understanding of drivers' route choice behavior in Seoul at an aggregate level.

• Proceedings of the KSR Conference
• /
• 2010.06a
• /
• pp.2166-2178
• /
• 2010

The Korea Transportation Database (KTDB) is used to obtain data on the origin and destination (OD) of inter-city travel, which are currently used in railroad planning when estimating traffic demand. The KTDB employs the trip assignment method, whereby the total traffic volume researched for inter-city travel in Korea is divided into road, rail and air traffic, etc. However, as regards rail travel, the railroad stations are not identical to the existing zones or the connector has not been established because there are several stations in one zone as such, certain problems with the applicable methods have been identified. Therefore, estimates of the volume of railroad traffic using the KTDB display low reliability compared to other modes of transportation. In this study, these problems are reviewed and analyzed, and use of the aggregate model method to estimate the direct demand for rail travel is proposed in order to improve the reliability of estimation. In addition, a method of minimizing error in traffic demand estimation for the railroad field is proposed via an analysis of the relationship between the aggregate model and various social-economic indicators including population, distances, numbers of industrial employees, numbers of automobiles, and the extension of roads between cities.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.2
• /
• pp.1-9
• /
• 2012

This study proposed a method to determine the number and location of bus information terminals (BIT), which is a device to provide passengers with bus arrival time at bus stops in a Bus Information System (BIS). In low-density area, it is not efficient to survey bus demands such as the number of passengers at all bus stops due to time and cost. This kind of a survey would, however, competently cover all bus stops if performed inside the bus. The number of riding-on and -off passengers is observed for every bus stop, and this data collection is repeated over all day. Data obtained from the survey are aggregated each bus stop. This study defines Utility Index (UI), an aggregate each bus stop. Bus stops are ranked according to UI and determined for a BIT within budget limitation. As a case study, a bus line in Jeju island, Korea, was dealt with. This case showed that the more aggregate the better data quality. This study is expected to contribute to solving a location problem of BITs in a BIS.

• Journal of Korean Society of Transportation
• /
• v.27 no.1
• /
• pp.73-81
• /
• 2009

While various factors in passenger and freight demand analysis affect on destination choice, a key factor, in general. is an attractiveness measure by size variable (e.g., population. employment etc) in destination zone. In order to measure the attractiveness, some empirical studies suggested that disaggregate gravity model are more suitable than aggregate gravity model. This study proposes that truck travelers trip diary data among Korean commodity flow data could be used to estimate the behaviors of incorporating trip departure time, activity duration and attractiveness in destination. As a result, the main findings of size and distance variables coincide with the conventional gravity model having a positive effect of population variable and a negative effect of distance variable. Due to disaggregate gravity modeling, the unique findings of this study reports that small trucks are more likely to choose short distance and early morning, morning peak and afternoon peak departure time choice. On the other hand, large trucks are more likely to choose long distance and night time departure time choice.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.1
• /
• pp.29-48
• /
• 2014

The recent keyword advertising does not reflect the individual customer searching pattern because it is focused on each keyword at the aggregate level. The purpose of this research is to observe processes of customer searching patterns. To be specific, individual deal-proneness is mainly concerned. This study incorporates location as a control variable. This paper examines the relationship between customers' searching patterns and probability of purchase. A customer searching session, which is the collection of sequence of keyword queries, is utilized as the unit of analysis. The degree of deal-proneness is measured using customer behavior which is revealed by customer searching keywords in the session. Deal-proneness measuring function calculates the discount of deal prone keyword leverage in accordance with customer searching order. Location searching specificity function is also calculated by the same logic. The analyzed data is narrowed down to the customer query session which has more than two keyword queries. The number of the data is 218,305 by session, which is derived from Internet advertising agency's (COMAS) advertisement managing data and the travel business advertisement revenue data from advertiser's. As a research result, there are three types of the deal-prone customer. At first, there is an unconditional active deal-proneness customer. It is the customer who has lower deal-proneness which means that he/she utilizes deal-prone keywords in the last phase. He/she starts searching a keyword like general ones and then finally purchased appropriate products by utilizing deal-prone keywords in the last time. Those two types of customers have the similar rates of purchase. However, the last type of the customer has middle deal-proneness; who utilizes deal-prone keywords in the middle of the process. This type of a customer closely gets into the information by employing deal-prone keywords but he/she could not find out appropriate alternative then would modify other keywords to look for other alternatives. That is the reason why the purchase probability in this case would be decreased Also, this research confirmed that there is a loyalty effect using location searching specificity. The customer who has higher trip loyalty for specificity location responds to selected promotion rather than general promotion. So, this customer has a lower probability to purchase.