• Journal of Smart Tourism
• /
• v.2 no.2
• /
• pp.15-19
• /
• 2022

Destination marketing organizations (DMOs) utilize the official website for marketing and promotional purposes, while tourists often navigate through the official website to gather necessary information for their upcoming trips. With the advancement of business analytics, DMOs may need to exploit the clickstream data generated through their official website to develop more suitable and persuasive strategic marketing and promotional activities. As such, the primary objective of the current study is to show whether clickstream data can successfully identify the trip purposes of a particular user. Using a latent class analysis and multinomial logistic regression, this study found the meaningful and statistically significant variations in webpage visits among different trip purpose groups (e.g., weekend getaways, day-trippers, and other purposes). The findings of this study would provide a foundation for more data-centric destination marketing and management practice.

• Journal of Korean Society of Transportation
• /
• v.34 no.5
• /
• pp.437-448
• /
• 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 Korean Society of Transportation
• /
• v.30 no.3
• /
• pp.107-118
• /
• 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.

• Management Science and Financial Engineering
• /
• v.15 no.1
• /
• pp.51-69
• /
• 2009

A network model and a Genetic Algorithm (GA) is proposed to solve the simultaneous estimation of the trip distribution and traffic assignment from traffic counts in the congested networks in a logit-based Stochastic User Equilibrium (SUE). The model is formulated as a problem of minimizing a non-linear objective function with the linear constraints. In the model, the flow-conservation constraints are utilized to restrict the solution space and to force the link flows become consistent to the traffic counts. The objective of the model is to minimize the discrepancies between two sets of link flows. One is the set of link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links. The other is the set of link flows those are estimated through the trip distribution and traffic assignment using the path flow estimator in the logit-based SUE. In the proposed GA, a chromosome is defined as a real vector representing a set of Origin-Destination Matrix (ODM), link flows and route-choice dispersion coefficient. Each chromosome is evaluated by the corresponding discrepancies. The population of the chromosome is evolved by the concurrent simplex crossover and random mutation. To maintain the feasibility of solutions, a bounded vector shipment technique is used during the crossover and mutation.

• Proceedings of the KOR-KST Conference
• /
• 1995.02a
• /
• pp.58-82
• /
• 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
• /
• v.29 no.3
• /
• pp.7-19
• /
• 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 the Economic Geographical Society of Korea
• /
• v.19 no.1
• /
• pp.68-82
• /
• 2016

Destination choice analysis is an important issue of transportation demand research. The current study analyses the influential factors for university students' trip destination choice. The university students differ from other population groups in many aspects. The study is concerned with shopping, leisure and amusement purposes of trips, other than obligatory trips such as going to school. University students' daily life differs from those of employees and middle and high school students in the sense that a lot of flexible activities are mixed with fixed activities such as work and school attending. A multinomial logit analysis investigates the significance of the impact of a set of variables including residential location, gender and income of the university student. The results show that these variables affect the destination choice of shopping, leisure and amusement. The analysis also provides interesting interpretation of the relationships of the variables with the location choices, which are particularly relevant to the university students.

• Journal of Korean Society of Transportation
• /
• v.14 no.4
• /
• pp.31-48
• /
• 1996

This paper develops a disaggregate model system for travel behavior of workers in a metropolitan area. We attempt to develop a set of models for predicting trip generation type, trip purpose, destination, mode choices in each trip on the way from work to home by using the concept of utility maximization of base-to-base tour. The model incorporates the concept that decisions of a trip in a trip in a travel tour depend on decisions of the trips having been made before and decisions of trip planned after of this trip, as well as on current trip conditions. As the structure of the model, the nested logit model is used to avoid a simultaneous model's complexity. The data to be used for estimating the model system are from the person trip survey which was carried out in 1981 in Nagoya metropolitan. Empirical tests of the model for Nagoya metropolitan area show encouraging results and prove the validity of the assumption of this model.

• Journal of Korean Society of Transportation
• /
• v.29 no.2
• /
• pp.81-89
• /
• 2011

Trip distribution is the second step of the conventional travel demand estimation process, which connects trips between origin and destination, while transport mode choice is the third step of the process, which chooses transport mode among several modes serving for each origin-destination pair. Although these two steps have closely connected, they have been estimated independently each other in the estimation procedure. This paper presents an integrated model combining trip distribution and transport mode choice, and also presents its solution algorithm. The model integrates gravity model adopted for the trip distribution process with logit model employed for the mode choice process. The model would be expected to cope with the inconsistency issue existing in the conventional travel demand estimation procedure. This paper also presents an equilibrium condition, sensitivity of the model, and compares them with those of existing models.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.5D
• /
• pp.763-769
• /
• 2006

Gravity Model estimates target year's distributed trips using three variables like as origin zone's trip production, destination zone's trip attraction and traffic impedance between origin zone centroid and destination zone centroid. Estimating inner zone trip by gravity model is impossible because traffic impedance of inner zone has "0" value. So till today, for estimating inner zone trips, other methods like growth factor model are used. This study proposed inner zone trip estimation method that calculates inner zone's traffic impedance using established gravity model and estimates inner zone trips by putting calculated traffic impedance into the gravity model. 1988 year's surveyed O-D as basic year's O-D, proposed method's and existing methods(growth factor method and regression model)'s estimated results of 1992 year's and 2004 year's were compared with each year's real O-D by $x^2$, RMSE, Correlation coefficient. And resulted that the proposed method is superior than other existing methods.