• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.87-97
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• 2008

The traditional trip-based modeling approach has assumed that a trip is generated for a purpose. However, the approach has not considered trips as a set of connected trips nor has it considered trip chaining. The purpose of this study is to identify general characteristics of trip chaining, and to explore relationships between trip purposes and trip chains using multivariate regression models. The data for this study come from the 2006 Seoul household travel diary survey. It is found that simple trip chains are dominant phenomena, and socio-economic characteristics such as occupation, income, age, and gender are closely related to types of trip chains. People aged less than 20, females, or high-income people are more likely to have a higher number of home-based trip chains. In addition, commute and school trips for workers and students respectively tend to be strongly associated with simple trip chains, while shopping and leisure trips for housewives tend to be related to simple trip chains.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.68-79
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• 2014

This paper analyzes the characteristics of trip chaining activities of elderly and explores temporal and spatial distribution. The research also estimates ordered probit model and binary logistic model to investigate various factors affecting trip chaining and mode choice patterns. We utilized household survey data for elderly conducted in 2006 and 2010 in Seoul metropolitan area. Research results indicate that trip chaining showed an increasing trend and simple trip chaining counts for more than 85%. GIS mapping expressed spatial distribution of trip departure and arrival areas, particularly showing regional changes in job-related trips. We also found that more factors influence trip chaining in 2010, compared with 2006, and travel cost is more sensitive than travel time in determining travel mode. The research contributes to establish transportation policies based on travel behavior of elderly in a upcoming super-aged society.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.7-19
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• 2011

Using 2006 metropolitan household travel survey data, this study analyzes trip behaviors based on a concept of trip chains using both trip purpose and number of trip linkages. For the analysis, trip chains are classified into two groups depending on including commute trips. Each group is further classified into a single linkage (i.e., Origin-Destination trips without any intermediate stop-by) and multiple linkages (Origin-Destination trip with at least one intermediate stop-by). The analysis is conducted using the two-step Nested Logit Model. Computational results identifying the characteristics of single and multiple linkages show that the young, male and office employee drivers tend to have more multiple linkages than single linkages in their trips. In contrast, it is shown that a driver whose monthly income is less than 3,000,000 Korean Won with a longer commute time more likely to make a trip chain with single linkages (p<0.0001).

• 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.34 no.5
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• pp.437-448
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• 2016

Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip's origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman's rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.2
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• pp.192-202
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• 2008

Typical activity-based travel analysis has been focused on passenger travel using household survey data. The current research focuses on freight transport using one-day travel survey data. Passenger travel can be seen as the outcome of traveller's subjective decision-making, whereas freight transport is the outcome of shipper or transport company's optimized scheduling. The research conducts an activity-based analysis of freight-vehicle trip chains. In particular, the research focuses on the difference in travel pattern between shipper-oriented private vehicle and transport company-oriented business vehicle. The research analyzed the travel diary of freight vehicles collected as part of the third national logistic survey in 2005. The diary is freight driver's one-day travel record including the information of loading capacity, item transported, destination, arrival time, etc. The analysis results show the difference between private and business vehicles in the travel pattern regarding the sequences of destination, destination type and item transported and the multi-dimensional information of the three sequences.

• 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.

• 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.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.581-589
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• 2007

• Journal of Korean Society of Transportation
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• v.30 no.3
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• pp.71-82
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• 2012

This study analyzed factors affecting daily travel times at each stage of commuters' life cycle. In this study, travel times were dealt with in the context of trip chain. That is, the travel time was defined as the total amount of time commuters had spent to move for daily activities from leaving to coming back home. A commuter's life cycle was divided into 6 stages on a basis of both householder's age and family type: i.e., the unmarried youth period, the family forming period, the children education period, the children youth period, the children independence period, and the aged period. Variables such as commuting times, home-based trip cycle recurrences, and the number of stops differed for each stage of life cycle, the latter of which represents how many places a commuter dropped by during a trip cycle. Several factors were found to affect commuting times at each stage of life cycle as a result of applying a Cox proportional hazard model. The empirical study was conducted using 2010' household travel survey data collected from Gyeonggi-do.