• 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 Economic Geographical Society of Korea
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• v.14 no.4
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• pp.540-552
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• 2011

Freight travel pattern has been less studied comparing with the field of passenger travel. Nonetheless, the importance of the freight travel has been increasing in urban travel sector, and the research needs on the freight travel demand hence is increasing. The current paper aims to identify, by tons of freight trucks and cargos, the characteristics of mean travel pattern, efficiency or performance, and the characteristics of freight trip chain regarding destination location, destination type and freight type. The study analyzed the nation-wide data of freight travel behavior survey. This study intended to set the starting framework of decision-making principle in freight travel, which has already been popular in passenger travel study. Findings suggest that those characteristics are clearly distinguished among trucks and cargos of different sizes of tons. The results are expected to provide important insight to the development of relevant transportation policy measures.

• 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.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.50-63
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• 2017

While transit is inconvenient, it is also inevitable for the efficient public transportation. Reducing the number of transfers as much as possible is most important in providing the convenience of public transportation and facilitating the public transportation. As for the public transportation card data, 61,986 items on weekdays and 69,100 items on weekends were collected. Pattern analysis and traffic influence factors were analyzed using traffic data card. Trip chain results revealed that people have more transit transfers for shopping and leasure than commuting purposes on weekends and that commuting distance and time increase by 10 km and 9.9 minutes, respectively. Besides, results of the structural equation model showed that factor 1(total travel time, total travel distance), factor 2(number of people getting on and off), factor 3(transit time), and factor 4(number of bus connections, number of operations) were found to have significant effects on the number of transfers.

• 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.

• Journal of Korean Society of Transportation
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• v.30 no.3
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• pp.107-118
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• 2012

The purpose of the study is to analyze the travel characteristics of freight trucks in metropolitan areas, focusing on activity generation, destination choice, and trip chaining behaviors. The results showed that the number of service companies at departure areas has a primary influence on the activity generation pattern and destination choice behavior of trucks in metropolitan areas. The number of trips within a trip chain is largest, in case where the prevailing industry in destination areas is wholesale or retail and the shipment item is food or beverage. These results imply that for the reasonable estimation of truck travel demand both the trip chaining behaviors and the industrial compositions in departure and destination areas should be separately considered for each type of commodity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.96-110
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• 2018

As the development of ICT has made it easier to collect various traffic information, research on creating new traffic attributes is drawing attention. Estimation and forecasts of demand and traffic volume are one of the main indicators that are essential to traffic operation, assuming that the traffic pattern at a particular node or link is repeated. Traditionally, a survey method was used to demonstrate this similarity on trip behavior. However, the method was limited to achieving high accuracy with high costs and responses that relied on the respondents' memory. Recently, as traffic data has become easier to gather through ETC system, smart card, studies are performed to identify the regularity of trip in various ways. In, this study, route-level trip data collected in Daegu metropolitan city were analyzed to confirm that individual traveler forms a spatially similar trip chain over several days. For this purpose, we newly define the concept of spatial trip regularity and assess the spatial difference between daily trip chains using the sequence alignment algorithm, Dynamic Time Warping. In addition, we will discuss the applications as the indicators of fixed traffic demand and transportation services.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.