• 대한교통학회지
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• 제7권2호
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• pp.45-52
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• 1989

The network equilibrium theory is to estimate the travel choices on a transportation network when the resulting travel times and costs are one basis for the choices. Increasing use of this principle on travel assignment problem lead to develop the combined choice models including not only travel options such as mode and route, but location options like trip distribution problems. This paper, first, reviews earlier developments of variable demand network equilibrium models, combined modeles of trip distribution and assignment, and entropy constrained combined models. Then various model structures of combining travel choice models based on network equilibrium theory and entropy constraints are discussed.

• 유통과학연구
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• 제15권6호
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• pp.13-24
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• 2017

Purpose - The current study aims to examine the effect of social network factors on travel agency employees' occupational outcomes such as job performance and job satisfaction through innovation behavior in a comprehensive model. Research design, data, and methodology - Based on a theory of social network, the concept of social network was assessed by three factors: a) network size, b) network range, and c) tie strength. To test the proposed hypotheses, structural equation modeling (SEM) was employed based on data from 197 travel agency employees in Korea. Result - The results showed that the associational activity of network size had a positive effect on innovation behavior, while the network range of network size had a significant negative effect on innovation behavior. Subsequently, innovation behavior positively influenced on job performance and job satisfaction, respectively. Conclusions - The results offer some insights into the extended model and have important managerial implications for Korean travel agencies. More specifically, considering diverse domains of social network and organizational research, this study advances critical utility of social network factors in a high facilitating level of innovation behavior, which can help travel agency employees promote their job performance and job satisfaction.

• 대한교통학회지
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• 제19권1호
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• pp.89-99
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• 2001

기존의 고정수요(Fixed Demand)를 전제로 한 가로망 설계 모형에서는 가로망의 구조나 용량이 개선되더라도 장래 기·종점 통행수요는 변하지 않는다고 가정한다. 이는 단기적인 가로망 설계에서는 성립할 수 있지만, 현실적으로 기·종점 통행수요는 네트워크 서비스수준에 따라 변화하므로 고정수요를 전제한 장기적인 가로망 설계문제에서는 그 타당성을 잃어버린다 그러므로 장래 최적 가로망 설계는 현실적 여건과 교통특성상 기·종점 통행 수요가 모형 내부에서 결정되는 내생변수로 처리하는 가변수요(Variable Demand)를 반영한 가로망 설계 문제로 모형을 구축하는 것이 바람직하다. 이러한 맥락에서 본 논문은 가변수요를 갖는 가로망 설계문제에 대한 이중계층 모형을 구축한 다음, 가로망내의 특성치가 변화하였을 때 그 파급영향을 먼저 파악하고 현 가로망 개선에서 가장 먼저 고려해야 할 링크를 찾아내기 위해 민감도 분석을 수행하였고, 민감도 분석과 연관되어 전체 시스템 효과척도를 최적화할 수 있는 대안적인 알고리즘을 제시하고 적용하여 구축된 모형으로 그 유효성을 검증하였고, 기존 고정수요 가로망 설계기법에 내재된 한계점을 극복하고자 하였다.

• 산업경영시스템학회지
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• 제22권50호
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• pp.221-230
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• 1999

The important issue for intra-city truck dispatching system is to measure and store actual travel speeds between customer locations. Travel speeds(and times) in nearly all metropolitan areas change drastically during the day because of congestion in certain parts of the city road network. We propose a back-propagation neural network model to recognize the pattern of intra-city vehicle travel speeds between locations that relieve much burden for the data collection and computer storage requirements. On a real-world study using the travel speed data[1] collected in Seoul, we evaluate performance of neural network model and compare with Park & Song model[2] that employs the least square method.

• 대한산업공학회지
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• 제32권1호
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제4권10호
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• pp.455-464
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• 2015

최근 해외여행에 대한 관심과, 스마트폰의 사용이 증가함에 따라 여행객들의 여행 관련 응용 사용이 늘고 있다. 여행객들에게 필요한 응용은 날씨, 지도, 여행 정보 제공 응용 등이 있다. 하지만 해외에서의 네트워크는 비싸고 불안정하기 때문에, 해외여행객들에게 금전적인 부담을 주고 사용상의 불편함을 준다. 예를 들어, 지도 응용은 네트워크 연결이 필수적이며 이미지 다운로드 양이 많아서 배터리 소모가 많다. 하지만 해외여행은 야외 활동이 주를 이루기 때문에 스마트폰의 충전이 쉽지 않다. 이러한 문제점을 해결하고자 본 논문에서는 네트워크, 배터리의 사용을 최소한으로 줄이는 방법을 목적으로 Travel Manager를 제안하며 구현한다. Travel Manager는 사용자가 네트워크의 연결 여부를 확인하여 수동으로 여행 관련 정보를 동기화할 수 있도록 한다. 그 밖에 환율을 자동 적용하여 경비를 계산하며, 사용자 간에 여행 정보를 주고받을 수 있는 기능을 제안한다. 또한 배터리의 사용을 최소화하면서 네트워크 연결 없이도 사용이 가능한 네트워크 프리 그린 네비게이션을 제안한다.

• 대한교통학회지
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• 제1권1호
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• pp.28-42
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• 1983

In recent years, significant advances have been made enabling travel demand analysis and network design methods to be used as increasingly realistic evaluation tools. What has been lacking is the integration of travel demand analysis with network design models. This paper reviews some of advanced (integrated) modeling approaches and presents future research directions of integrated modeling system. To design urban transportation networks, it is argued that the travelers' free choice of mode, destination and route should be introduced into transportation network design procedure instead of assuming that trips from a zone to a workplace are fixed or deriving them in a normative procedure to achieve hypothetical system optima.

• 지능정보연구
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• 제4권2호
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• 경영과학
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• 제17권2호
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• pp.87-95
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• 2000

This paper presents two analytical approaches to determine the vehicle fleet size for container shuttle service. The shuttle service can be defined as the repetitive travel between the designated places during working period. In the first approach, the transportation model is adopted in order to determine the number of vehicles required. Its advantages and disadvantages in practical application are also discussed. In the second approach, a logical network which is oriented on job is transformed from a physical network which is focused on demand site. Nodes on the logical network represent jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next job. Then a mathematical formulation is constructed similar to the multiple traveling salesman problem (TSP). A solution procedure is carried out based on the well-known insertion heuristic with the real world data.

• 산업공학
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• 제11권1호
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• pp.75-84
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• 1998

The important issue for intra-city vehicle scheduling is to measure and store actual vehicle travel speeds between customer locations. Travel speeds(and times) in nearly all metropolitan areas change drastically during the day because of congestion in certain parts of the city road network. We propose three models for estimating departure time-dependent travel speeds between locations that relieve much burden for the data collection and computer storage requirements. Two of the three models use a least squares method and the rest one employs a neural network trained with the back-propagation rule. On a real-world study using the travel speed data collected in Seoul, we found out that the neural network model is more accurate than the other two models.