• Management Science and Financial Engineering
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• v.15 no.1
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• pp.51-69
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• 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
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• 1999.10a
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• pp.41-46
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• 1999

• 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).

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.1084-1092
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• 2005

Various urban transport policies have an effect on urban transit riderships. This study reports variations of metropolitan subway travel patterns affected by an enormous change in bus routes and transfer discount policy between subway and bus mode conducted by Seoul city in July 1st of 2004. In an effort to see the difference between the before and the after policies, two datasets are prepared. Firstly, on a daily bassis, an origin-destination trip table of May of 2004 is used. Secondly, on a daily bassis, an origin-destination trip table of August-September of 2004 is used as a counter measure. Even if seasonal variation was not considered. there were increasing riderships of about 0.25 million on a daily basis. Subway line 2 and 7 have an important role in changes. The effects or system changes, however. largely varied on location and subway line numbers.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.81-89
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• 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
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• v.26 no.5D
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• pp.763-769
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• 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.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.599-617
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• 2006

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 the non-linear objective functions with the linear constraints. In the model, the flow-conservation constraints of the network are utilized to restrict the solution space and to force the link flows meet the traffic counts. The objective of the model is to minimize the discrepancies between the link flows satisfying the constraints of flow-conservation, trip production from origin, trip attraction to destination and traffic counts at observed links and the link flows estimated through the traffic assignment using the path flow estimator in the legit-based SUE. In the proposed GA, a chromosome is defined as a vector representing a set of Origin-Destination Matrix (ODM), link flows and travel-cost coefficient. Each chromosome is evaluated from the corresponding discrepancy, and 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 is applied during the crossover and mutation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.123-132
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• 2008

Dynamic origin-destination (OD) trip matrix has been widely used for transportation fields such as dynamic traffic assignment, traffic operation and travel demand management, which needs precise OD trip matrix to be collected. This paper presents a quasi-dynamic OD matrix estimation model and applies it to real road network for collecting the dynamic OD matrix. The estimation model combined with dynamic traffic assignment program, DYNASMART-P, is based on GPS embedded in PDA, which developed for collecting sample dynamic OD matrix. The sample OD matrix should be expanded by the value of optimal sampling ratio calculated from minimization program. From application to real network of Jeju, we confirm that the model and its algorithm produce a reasonable solution.