• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.91-100
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• 2010

To understand the trip patterns of subway passengers is very important to making plans for an efficient subway system. Accordingly, there have been studies on mining and classifying useful patterns from large smart card transaction databases of the Metropolitan Seoul subway system. In this paper, we define a new classification of subway trip patterns and devise a classification algorithm for eleven trip patterns of the subway users from smart card transaction databases which have been produced about ten million transactions daily. We have implemented the algorithm and then applied it to one-day transaction database to classify the trip patterns of subway passengers. We have focused on the analysis of significant patterns such as round-trip patterns, commuter patterns, and unexpected interesting patterns. The distribution of the number of passengers in each trip pattern is plotted by the get-on time and get-off time of subway transactions, which illustrates the characteristics of the significant patterns.

• Journal of the Economic Geographical Society of Korea
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• v.10 no.1
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• pp.44-63
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• 2007

The purpose of this study is to propose mining processes in the large trip-transaction database of the Metropolitan Seoul area and to analyze the spatial characteristics of travel behavior. For the purpose. this study introduces a mining algorithm developed for exploring trip patterns from the large trip-transaction database produced every day by transit users in the Metropolitan Seoul area. The algorithm computes trip chains of transit users by using the bus routes and a graph of the subway stops in the Seoul subway network. We explore the transfer frequency of the transit users in their trip chains in a day transaction database of three different years. We find the number of transit users who transfer to other bus or subway is increasing yearly. From the trip chains of the large trip-transaction database, trip patterns are mined to analyze how transit users travel in the public transportation system. The mining algorithm is a kind of level-wise approaches to find frequent trip patterns. The resulting frequent patterns are illustrated to show top-ranked subway stations and bus stops in their supports. From the outputs, we explore the travel patterns of three different time zones in a day. We obtain sufficient differences in the spatial structures in the travel patterns of origin and destination depending on time zones. In order to examine the changes in the travel patterns along time, we apply the algorithm to one day data per year since 2004. The results are visualized by utilizing GIS, and then the spatial characteristics of travel patterns are analyzed. The spatial distribution of trip origins and destinations shows the sharp distinction among time zones.

• Journal of the Korean Geographical Society
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• v.45 no.5
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• pp.592-608
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• 2010

This study investigates the diurnal pattern of transit ridership in the Metropolitan Seoul area. For the purpose, we use a weekday Smart Card passenger transaction data in 2005. Eleven passenger trip patterns are found from 2.74 million passengers moving on the Metropolitan Seoul Metro system. Among them, we analyze 2.4 million passengers blonging to five trip types having only one or two transaction record during a day. A total of 357 metro stations are classified to four types according to their diurnal pattern of passenger riderships. We analyze the relationships between passenger's trip chain patterns and subway station's diurnal transit ridership patterns. The result shows that the ratio of the number of passengers of particular time of the day is hierarchically related with trip chain patterns.

• Journal of the Korean Geographical Society
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• v.42 no.3 s.120
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• pp.421-433
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• 2007

The Metropolitan Areas have experienced the phenomenon that some of their peripheral parts emerged as a core business area because of the relocation of residential and economic activities from the central area. An important phenomenon in the spatial transformation of metropolitan area is the weakening of centrality in the center and the increasing strength of centrality in the periphery. This paper examined the changing patterns of spatial interaction in the Seoul Metropolitan area through an analysis on outflow trips. Outflow trip by Seoul decreased in nearby regions and increased in remote regions, however as times goes by, the spatial patterns of the largest outflow trip destination were diversified and the rate of outflow trip to Seoul has decreased in the periphery regions. This research reveals that the most remarkable changes of spatial interactions occurred nearby regions of Seoul and also the changes of outflow trip by Seoul was also distinct. In relation to this, the results arising from the similarity analysis by the variance of trip clearly show the changing spatial patterns of interaction in Yongin, Seoul, Suwon and Hwaseong.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.58-82
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• 1995

This study providesd an empirical analysis of trip-chaining behavior and its application to transportation planning. In the empirical analysis, changes in trip-chaining patterns since 1970 have been examined and details of current trip-chaining behavior as they describe shopping trip-chaining behavior has changed. Individual trip-chaining has become longer and complex. It appears that the average number of trips per chains has substantially increased over the past 20 years. An increased number of trips in chains means fewer home-based trips. Changes in trip-chaining behavior have several consequences. Important consequences are for transportation and land-use planning. Up to now trips have been treated as if they are independent clusters of home-to-destination-to-home; this approach has not usually taken into account the trip-chaining behavior of individuals. this calls for a different approach to at least the trip generation and trip distribution part of transportation planning. In this study, application of trip-chaining behavior to trip distribution model formulation is proposed and its calibration results are presented.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.7-16
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• 2008

There are unique aspects of truck vehicle movements compared with the personal travel in trip chaining. This paper reports an analysis on the truck vehicle trip chaining which intercity/metropolitan/intraregional trips are classified. Data collected from the travel dairy survey is used the truck trip-chaining analysis. The pattern of trip chaining classes is classified by the GIS mapping based on orgin-destination trip information. The physical index and efficiency index for each trip diary is used to the truck vehicle activity. Truck trips lengths and time differs from its truck type, service type and travel patterns. It is shown that the efficiency of the truck trip chaining depends on vehicle types and its delivery patterns. There are many other topics for research on trip chaining modeling such as the classification of trip chain, time use and mode choice by trip chaining.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.143-150
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• 2005

Estimation of trip distribution, estimated O-D matrix must satisfy the condition that the sum of trips in a row should equal the trip production, and the sum of trips in a column should equal the trip attraction. In most cases the iterative calculation for convergence is needed to satisfy this condition. Most of all present convergence of iterative methods may results a big difference between estimated value and converged value, and from this, the trip distribution patterns may be changed. This paper presents a new convergence of iterative method that comes closer to meeting the convergence condition and gives the maximum likelihood estimation for calculating a distribution patterns from the trip distribution estimation model. The newly developed method differs from existing methods in three important ways. First, it simultaneously considers both the convergence condition and the distribution patterns. Second, it computers simultaneous convergence of rows and columns instead of iterating respectively. Third, instead of using the growth rates to the trip production, trip attraction, it uses the differences between trip production and sum of trips in a row, and trip attraction and sum of trips in a column. Using 38 by 38 O-D matrix, this paper compared the Fratar method and the Furness method to the newly developed method and found that this method was superior to the other two methods.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.4
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• pp.759-770
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• 2014

The aim of this study is to examine how time-use and activities are affected by work hours. To achieve this, we focused on the weekday time-use of full-time workers in Seoul Metropolitan Area(SMA). The long 'work hours' are under active discussions since it is related to the quality of life. However, many Social researcher thought that problem of Korean working hours is linked to quality of life in the abstract. Because activity connects time-use and quality of life, the key point is activity under time constraints. Therefore, travel patterns should be understood by time-use and activity patterns. This study composes trip-chains from travel data of 2010 Household Travel Survey(HTS). Grouping trip-chains by activity patterns, we could make sure that a few of activities after work is affected by a short free time. This study has potential implications for the policy of work hours and traffic problems in the evening, and will provide new geographical perspective related to measuring quality of life.

• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.77-86
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• 2007