• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.55-71
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• 1990

The behavioral mechanism underlying the traffic assignment model is a choice, or decision-making process of traveling paths between origins and destinations. The deterministic approach to traffic assignment assumes that travelers choose shortest path from their origin-destination pair. Although this assumption seems reasonable, it presumes that all travelers have perfect information regarding travel time, that they make consistently correct decision, and that they all behave in identical fashion. Stochastic user equilibrium assignment relaxes these presumptions by including a random component in traveler's perception of travel time. The objective of this study is to compare "A Model of Deterministic User Equilibrium Assignment" with "Models of Stochastic User Equilibrium Assignment" in the theoretical and practical aspects. Specifically, SUE models are developed to logit and probit based models according to discrete choice functions. The models were applied to sioux Falls net ork consisting of 24 zones, 24 nodes and 76 links. The distribution of perceived travel time was obtained by using the relationship between speed and traffic flow.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.99-108
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• 2007

This paper presents an elastic demand stochastic user equilibrium traffic assignment that could not be easily tackled. The elastic demand coupled with a travel performance function is known to converge to a supply-demand equilibrium, where a stochastic user equilibrium (SUE) is obtained. SUE is the state in which all equivalent path costs are equal, and thus no user can reduce his perceived travel cost. The elastic demand SUE traffic assignment can be formulated based on a dynamic system, which is a means of describing how one state develops into another state over the course of time. Traditionally it has been used for control engineering, but it is also useful for transportation problems in that it can describe time-variant traffic movements. Through the Lyapunov Function Theorem, the author proves that the model has a stable solution and confirms it with a numerical example.

• 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.53-58
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• 1999

• Journal of Communications and Networks
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• v.18 no.3
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• 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.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.117-129
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• 2010

This study presents a sensitivity analysis method for stochastic user equilibrium of multi-modal network flows. We consider a multi-modal network consisting of a road network for passenger cars physically separated from a transit network for public transport. We first establish a sensitivity analysis method with respect to arbitrary link parameters and perform a sensitivity analysis with respect to link capacity and transit line frequency as practical link parameters. Next, We establish a sensitivity analysis method and perform the sensitivity analysis with respect to modal split by passenger car and public transit. As with the elasticity of economics, these results can be important information for analyzing changes in travel behavior due to the changes in operation and policy of transportation facilities, as well as for analyzing the effects of these operational changes and policies. These results also can be utilized as a tool to constitute a multi-modal network design problem by using cooperative game theory.

• Journal of Korean Society of Transportation
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• v.21 no.2
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• pp.95-105
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• 2003

Because the basic assumptions of deterministic user equilibrium assignment that all network users have perfect information of network condition and determine their routes without errors are known to be unrealistic, several stochastic assignment models have been proposed to relax this assumption. However. it is not easy to solve such stochastic assignment models due to the probability distribution they assume. Also. in order to avoid all path enumeration they restrict the number of feasible path set, thereby they can not preciously explain the travel behavior when the travel cost is varied in a network loading step. Another problem of the stochastic assignment models is stemmed from that they use heuristic approach in attaining optimal moving size, due to the difficulty for evaluation of their objective function. This paper presents a logit-based stochastic assignment model and its solution algorithm to cope with the problems above. We also provide a stochastic user equilibrium condition of the model. The model is based on path where all feasible paths are enumerated in advance. This kind of method needs a more computing demand for running the model compared to the link-based one. However, there are same advantages. It could describe the travel behavior more exactly, and too much computing time does not require than we expect, because we calculate the path set only one time in initial step Two numerical examples are also given in order to assess the model and to compare it with other methods.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.165-179
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• 2005

The object of this study is a development of a stochastic user equilibrium transit assignment algorithm for multiple user classes considering stochastic characteristics and heterogeneous attributes of passengers. The existing transit assignment algorithms have limits to attain realistic results because they assume a characteristic of passengers to be equal. Although one group with transit information and the other group without it have different trip patterns, the past studies could not explain the differences. For overcoming the problems, we use following methods. First, we apply a stochastic transit assignment model to obtain the difference of the perceived travel cost between passengers and apply a multiple user class assignment model to obtain the heterogeneous qualify of groups to get realistic results. Second, we assume that person trips have influence on the travel cost function in the development of model. Third, we use a C-logit model for solving IIA(independence of irrelevant alternatives) problems. According to repetition assigned trips and equivalent path cost have difference by each group and each path. The result comes close to stochastic user equilibrium and converging speed is very fast. The algorithm of this study is expected to make good use of evaluation tools in the transit policies by applying heterogeneous attributes and OD data.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.111-124
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• 1995

첨단 교통 체계(Intelligent Transport Systems)의 중요한 요소인 첨단 교통 관리 체계(Advanced Traffic Management Systems)의 성공 여부는 교통정보를 어떻게 제공하고 통제하는데 의존하다. 즉, 정보 제공 방식과 이데 대한 운전자의 반응을 정확하게 파악하고 예측하여야 ITS를 성공적으로 구축할 수 있다. 이 논문에서는 동적 차량 길잡이 장치의 효용성을 평가하기 위한 확률적 통행배정모형을 개발하는 것이다. 개발된 통행배정모형은 운전자의 동적행태조정(Dynamic Behavioural Adjustment)을 명백하게 확솔 과정(Stochastic Process)으로 표현하여 기존의 모형에 비해 통해자들의 행태를 더욱 실제적으로 반영한다. 특히, 각 통행자들에게 K개의 최소경로시간을 제공해줌으로 인하여 통행자의 노선선택에 대한 선택폭을 증가시켜준다. 통행경로의 선택폭의 증가는 쟁점으로 대두되는 문제(교통항제소에서는 차량 길잡이 보유 운전자에게 체계최적(System Optimum)와 이용자최적(User Equilibrium)중 어떠한 원칙하에 교통정보를 제공하여야 하는가에 대한 해결 방안이다. 왜냐하면 만약 교통급제소에서 운전자에게 통행정보를 체계 최상을 하기 위해 정보를 제공하고자 하면, 길잡이 장착 운전자는 더 이상 제공된 정보를 따르지 않고 자기 스스로의 경에 의해 이용자 최상을 달성하고자 할 것이다. 이 논문의 목적은 이러한 복잡한 통행자의 경로선택행위를 반영하는 확률적 평형 통행 배정 모형을 여러가지 통계기법을 도입하여 개발하는 것이다.