• 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 Intelligence and Information Systems
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• v.15 no.3
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• pp.137-150
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• 2009

This paper proposes the hybrid heuristic method to apply the opportunity time to solve the vehicle routing and scheduling problem with time constraints(VRSPTW). The opportunity time indicates the idle time which remains after the vehicle performs the unloading service required by each customer's node. In this proposed heuristic, we add the constraints to VRSPTW model for the opportunity time. We also obtain the initial solution by applying the cost evaluation function to the insertion strategy considering the opportunity time. In addition, we improve the former result by applying the opportunity time to the tabu search strategy by swapping the customer's node. Finally, we suggest the construction strategies of initial routing which can efficiently acquire the nearest optimal solution from various types of data in terms of geographical condition, scheduling horizon and vehicle capacity. Our experiment show that our heuristic can get the nearest optimal solution more efficiently than the Solomon's I1 heuristic.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.2
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• pp.16-28
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• 2004

A seam-line determination algorithm is proposed to determine image border-line in mosaicing using the transformation of gray value differences and dynamic programming. Since visually good border-line is the one along which pixel differences are as small as possible, it can be determined in association with an optimal path finding algorithm. A well-known effective optimal path finding algorithm is the Dynamic Programming (DP). Direct application of the dynamic programming to the seam-line determination causes the distance effect, in which seam-line is affected by its length as well as the gray value difference. In this paper, an adaptive cost transform algorithm with which the distance effect is suppressed is proposed in order to utilize the dynamic programming on the transformed pixel difference space. Also, a figure of merit which is the summation of fixed number of the biggest pixel difference on the seam-line (SFBPD) is suggested as an evaluation measure of seamlines. The performance of the proposed algorithm has been tested in both quantitively and visually on various kinds of images.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.147-156
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• 2009

We present a methodology for modeling and solving the transit frequency design problem with variable demand. The problem is described as a bi-level model based on a non-cooperative Stackelberg game. The upper-level operator problem is formulated as a non-linear optimization model to minimize net cost, which includes operating cost, travel cost and revenue, with fleet size and frequency constraints. The lower-level user problem is formulated as a capacity-constrained stochastic user equilibrium assignment model with variable demand, considering transfer delay between transit lines. An efficient algorithm is also presented for solving the proposed model. The upper-level model is solved by a gradient projection method, and the lower-level model is solved by an existing iterative balancing method. An application of the proposed model and algorithm is presented using a small test network. The results of this application show that the proposed algorithm converges well to an optimal point. The methodology of this study is expected to contribute to form a theoretical basis for diagnosing the problems of current transit systems and for improving its operational efficiency to increase the demand as well as the level of service.

• 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.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.61-72
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• 2011

In the benefits of ITS, there are intangible gains from real-time traffic information as well as classical gains such as travel time saving. These intangible gains are difficult to be estimated by existing transportation investment appraisal commonly used in SOC investment. The major reason is not because of the absence of methodology but because of the absence of generalized values of particular benefits from real time traffic information. This research explores the value of real-time traffic information on VMS that is the most representative of ITS services, by using CVM with Double Bounded Dichotomous Choice Question. Willingness-To-Pay (WTP) functions of drivers are built with survival functions using various types of probability distribution functions such as Exponential, Log-logistic, and Weibull functions. The results reveal that Log-logistic distribution is the most appropriate distribution model to estimate WTP, and the estimated coefficients are stable through LR (Likelihood Ratio) test. For the further study, it is recommended to perform statistical tests of temporal and spatial transferability that is not examined in this research due to the lack of data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.538-542
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• 2018

Echo generated in digital hearing aid vent give rise to user's discomfort. For cancelling feedback echo in vent, it is required to estimate vent impulse response exactly. The vent impulse response has time varying and sparse characteristics. The IPNLMS has been known a useful adaptive algorithm to estimate vent impulse response with these characteristics. In this paper, subband sparse adaptive filter which applying IPNLMS to subband hearing aid structure is proposed to cancel echo of vent by estimating sparse vent impulse response. In the propose method, the decomposition of input signal to subband can pre-whiten each subband signal, so adaptive filter convergence speed can be improved. And the poly phase component decomposition of adaptive filter increases sparsity of each components, and the better echo cancellation can be possible without additional computation. To derive coefficients update equation of the adaptive filter, by defining the cost function based weight NLMS is defined, and the coefficient update equation of each subband is derived. For verifying performances of the adaptive filter, convergence speed, and steady state error by white signal input, and echo cancelling results by real speech input are evaluated by comparing conventional adaptive filters.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.1 s.15
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• pp.51-63
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• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.