• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.65-72
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• 2000

Travel demand forecasting is the important process of transportation policy and planning, especially trip assignment is also important because it finds deficiency of network GIS can be applied to transportation due to its various merits. Recently Program development environment is changed to component-based and transportation-component is necessary. This study evolves in implementing trip assignment model with GIS and tries to apply the system to the Cheongju City.

• 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.7 no.2
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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.

• Journal of Korean Society of Transportation
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• v.24 no.4 s.90
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• pp.149-159
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• 2006

Traditional trip tables are estimated through large-scale surveys such as household survey, roadside interviews, and license Plate matching. These methods are, however, expensive and time consuming. This paper presents two origin-destination (OD) trip matrix estimation methods from link traffic counts in stochastic assignment, which contains perceived errors of drivers for alternatives. The methods are formulated based on the relation between link flows and OD demands in logit formula. The first method can be expressed to minimize the difference between observed link flows and estimated flows, derived from traffic assignment and be solved by gradient method. The second method can be formulated based on dynamic process, which nay describe the daily movement patterns of drivers and be solved by a recursive equation. A numerical example is used for assessing the methods, and shows the performances and properties of the models.

• Journal of Korean Society of Transportation
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• v.11 no.3
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• pp.31-48
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• 1993

본 연구에서는 다른 기종점 통행표(Trip Matrices)들을 같은 교통망(Network)에 배정하였을 때 교통분배 결과의 차이점들을 분석하고 교통분배의 민감도를 비교하였다. 전통적인 4단계 교통수요 추정에 의해서 산출된 교통배분을 비교의 기본자료로 이용했다. 또한 본 연구에서는 교통배분의 결과를 평가하기 위해 주로 사용하는 측정효과들과 교통배분의 기법들(Traffic Assignment Techniques)의 민감도도 연구조사하였다. 본 연구를 통하여 총교통량(Total Trips)과 통행길이빈도(Trip Length Frequency)제약에 의해 임의로 선출된 기종점 통행표를 이용한 교통배분의 결과는 전통적인 4단계 교통수요 측정에 의해 산출된 교통배분 및 조사교통량(Counted Traffic Volumes)에 매우 유사한 결과가 나왔다. 결론적으로 죤별 통행발생량에서의 오차는 교통배분의 본성적인 집계특성(Aggregative Nature)에 의하여 그 심각성이 감소되는 경향이 있다. 이것은 즉 앞단계(Trip Generation and Distribution Phases)에서 전통적으로 요구되어지는 정밀도가 없어도 적절한 교통배분기법을 사용함으로써 좋은 결과를 산출할 수 있다는 것을 암시한다.

• IE interfaces
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• v.15 no.4
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• pp.474-482
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• 2002

In this paper, we propose a parametric optimization approach to simultaneously determining trip distribution, mode choice, and user-equilibrium assignment. In our model, mode choice decisions are based on a binomial logit model and passenger and cargo demands are divided into appropriate mode according to the user equilibrium minimum travel time. Underlying network consists of road and rail networks combined and mode choice available is auto, bus, truck, passenger rail, and cargo rail. We provide an equivalent convex optimization problem formulation and efficient algorithm for solving this problem. The proposed algorithm was applied to a large scale network examples derived from the National Intermodal Transportation Plan (2000-2019).

• Journal of Korean Society of Transportation
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• v.34 no.6
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• pp.570-579
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• 2016

This study proposes a concept of Stepwise Transfer Coefficient(STC) which implies greater transfer cost with increasing the number of transfers. Thus, the public transport information system provides the choice sets of travel routes by the consideration of not only transportation time but also the optimum number of transfers. However, path choice problems that involve STC are found to include non additive cost, which requires additional route enumeration works. Discussions on route enumeration in actual transportation networks is very complicated, thereby warranting a theoretical examination of route search considering STC. From these points of view, this study results in a probability based transit trip assignment model including STC. This research also uses incoming link based entire route deletion method. The entire route deletion method proposed herein simplifies construction of an aggregation of possible routes by theoretically supporting the process of enumeration of the different routes from origin to destination. Conclusively, the STC reflected route based logit model is proposed as a public transportation transit trip assignment model.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.527-535
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• 2007

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.