• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.7-18
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• 2012

This study suggested a method of forecasting market-share of each mode after introducing new urban rail transit lines. The study reflected the observed market share of presently operating urban rail transit into forecasting process in order to improve accuracy in predicting market share of each modes. For more realistic representation of the forecasting model, we categorized O/D pairs according to attributes of trip distance, access time and number of transfers. The analysis results of traveler's mode choice behavior with observed data showed that the trip distances are longer, the share of urban rail tends to be higher, and that the number of transfers is fewer and the access times are lesser, the share of urban rail also tends to be higher. Then, incremental logit model was used in estimating mode choice probabilities for O/D pairs along with rail transit lines while utilizing observed market shares of each modes and differences in transit service level. As the next step, the market share of rail transit after introducing new rail transit lines was forecasted by using incremental logit model with the intial share values calculated the previous analysis step. It also reflected changes in level of service for automobile in highway due to changes in highway systems and changes in mode shares after introducing new lines of rail transit. It can be expected that the proposed method would more realistically duplicates phenomena of mode choice behavior for rail transit and that it would be more theoretically logical than the typical existing methods using SP data and incremental logit model or using addictive logit model in this country.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.554-566
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• 2014

An objective of VMS(Variable Message Signs) is to make transportation system effective specifically for driver's path selection. The traffic solutions including a VMS problem can be modeled through Game Theory, however, the majority of the studies can not model various driver's response according to VMS information in game theory. So, this paper tries to analyze a driver's response according to VMS traffic informations through evolutionary game theory. We apply a behavior characteristics of driver to evolutionary game theory, then finds drivers are only accepting in case of the biggest pay-off, and if a traffic flow finds a balance over time, ratio of accepting information is converged as an evolutionary stable state gradually. Consequently, the strategy of the other drivers such as traffic problems can not be predicted accurately. In case, drivers repeat between groups and reasonable judgment by the experience, we expect that VMS can provide strategic information through evolutionary game theory.

• Journal of Korea Port Economic Association
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• v.33 no.3
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• pp.17-34
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• 2017

This study aims to analyze the freight transport demand and benefit for the introduction of an automated freight transport system focusing on the Global Industry and Logistics City (GILC) in Busan. In pursuit of this aim, four alternatives were calculated - using the freight volume estimating methods and included, the number of businesses, the number of employees set up, future estimated cargo volume, and switched volume from other transport modes into the GILC. Economic benefits were analyzed against social benefits and costs accordingly. The result of the freight transport demand forecast found, the cargo volume of "Alternative 2-1" to be the most advantageous, applying the number of employee unit method and proportion of employees in Gangseo-gu, Busan. In addition to the conventional analysis of direct benefit items (reduction of transport time, traffic accidents and environmental costs), this study also considered additional benefit items (congestion costs savings, and road maintenance costs in terms of opportunity cost). It also considered advanced value for money research in guidance on rail appraisal of U.K, Federal Transport Infrastructure Plan 2003 of Germany, and RailDec of the United States. The study aims to further contribute to estimating minimum cargo transport demands and assess the economic feasibility of the introduction of new intermodal automated freight transport systems in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.1-9
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• 2009

Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. However, correct bus information data, such as the present bus location, the user waiting time, the bus arrival time, etc. are not provided for the bus users because the proper bus arrival time predictive models are not used yet in most of the cities operating the bus information system, including the metropolitan City of Ulsan. Thus, the purpose in this study is to investigate real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze real-time bus traffic characteristic data on the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments which are the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for predicting the real-time bus arrival time at the bus stop of the arterial under the study.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.95-110
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• 1995

n this research neural -based model was developed to forecast link travel times , And it is also compared wiht other time series forecasting models such as Box-Jenkins model, Kalman filter model. These models are validated to evaluate the accuracy of models with real time series data gathered by the license plate method. Neural network's convergency and generalization were investigated by modifying learning rate, momentum term and the number of hidden layer units. Through this experiment, the optimum configuration of the nerual network architecture was determined. Optimumlearining rate, momentum term and the number of hidden layer units hsow 0.3, 0.5, 13 respectively. It may be applied to DRGS(dynamic route guidance system) with a minor modification. The methods are suggested at the condlusion of this paper, And there is no doubt that this neural -based model can be applied to many other itme series forecating problem such as populationforecasting vehicel volume forecasting et .

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.179-188
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• 2017

It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.177-182
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• 2007

최근 텔레매틱스 분야에서 GPS 수신기를 장착한 probe car를 통해 교통 정보를 수집하는 방법에 대한 연구가 활발히 진행되고 있다. 이 방법은 기존에 교통 정보를 수집하기 위해 활용되고 있던 고정식 검지기들에 비해 수집되는 정보가 높은 신뢰성을 가지고, 도로 환경에 민감하지 않으며, 낮은 유지비용으로 운용할 수 있다는 장점을 가지고 있다. 하지만, probe car는 자신의 위치 정보를 교통 정보 센터로 전송해 주어야 하기 때문에 프라이버시가 노출될 수 있고, 주차되어 있는 시간에는 통행 정보를 보내줄 수가 없다. 이런 이유로 대중 교통차량이나 상업용 차량이 주로 probe car로 활용되어지게 되는데, 그 수가 많지 않을뿐더러 운행 구간이 고르게 분포되지 않아 probe car가 지나지 않는 구간, 즉 교통 정보 누락 구간이 존재할 수 있는 문제점을 가지고 있다. 본 논문에서는 교통 정보 누락 구간의 처리를 위해 과거의 이력 정보로 대체하는 방법, 주변 도로의 구간 정보로 예측하는 방법, 회귀 분석을 통한 예측 방법 등을 기술하고 실제 probe car들로 수집된 서울시 강남대로 구간의 자료로 각 방법에 대한 실험을 실시하여 각각의 방법에 대한 결과를 비교 분석한다.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.2 s.2
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• pp.105-112
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• 1993

The conventional method for solving transportation problems were mainly based on numerical methods, where the understanding of outputs is not easy. Some difficulties come from the seperation of three key steps-the preparation of input for transportation traffic simulation, and model output interpretation. GSIS can help to eliminate some of thoses difficulties by combining graphics, database, and transportation plaining models. As pilot study, this study shows an application of Geovision GSIS to TRANPLAN transportation planning model that is based on four-step travel demand forecasting procedure. Accrued benefits and procedure are presented.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.527-536
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• 2022

The purpose of this study is to predict changing traffic environments and related economic effects by reflecting the changed KTDB and socio-economic indicators pertaining to Mulgeum station, a general railway stop, when it is confirmed as a KTX stop. To analyze the data of this study, socioeconomic indicators and the general status of transportation facility operations were investigated with reference to related statistical data, centered on the country overall and on Yangsan city in particular. In addition, we investigated and referenced the railroad facility construction plan and train operation plan, which are national high-level plans related to land development and transportation network construction. Currently, there are only ITX trains (4 times/day) and Mugunghwa trains (29 times/day) that stop at Mulgeum station in Yangsan, meaning that passengers cannot use KTX trains in the Yangsan area. In particular, the need for a KTX stop at Mulgeum station has been continuously raised because train users in the Yangsan area have inconvenient transportation in that they must travel 40 minutes to Ulsan station or 30 minutes to Gupo station to use the KTX. As a result of analyzing railroad transportation demand that will change in the future as the KTX stop at Mulgeum station is confirmed, the number of passengers boarding and arriving at Mulgeum station is predicted to be 1,674 passengers/day by 2025. In addition, the numbers of train passengers that are converted from Ulsan and Gupo stations due to the stop at Mulgeum station are predicted to be 594 passengers/day boarding and 562 passengers/day arriving by 2025. In the future, if Yangsan citizens use the KTX Mulgeum station, the access time to Mulgeum station can be shortened to 22 minutes from 65 minutes, and it is predicted that the inconvenience of transferring between railroads will be resolved, with the waiting time for transfers reduced by up to a maximum of 40 minutes. Therefore, the economic effect of creating a KTX stop at Mulgeum station was analyzed to be B/C=1.823 when general railroad operating costs are not taken into account and B/C=2.127 when general railroad operating costs are considered. In conclusion, when using KTX trains to visit the Seoul Metropolitan Area, it takes 2 hours and 43 minutes to use Mulgeum station without using Ulsan station or Gupo station, which is considered to be very effective for reducing travel times and improving the economic feasibility of this development; it is also expected that Yangsan city will be able to improve accessibility and mobility to the Seoul Metropolitan Area by breaking free from the disgrace of being a remote location given its link to KTX in the future.