• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.7-15
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• 2011

The travel time value is important factor for the evaluation of feasibility the transportation facility investment. The existing method for calculation of the travel time for each mode uses daily average trip purpose. So the value of travel time is constant because it is estimated with only daily average proportion. This daily constant time value can distort the results of future demands of toll roads or economic appraisals for the projects. The proportion of the trip purpose varies by time periods. Accordingly the value of travel time also varies by time periods. In this study, times periods are classified as morning peak, evening peak, business time off-peak, and non-business time off-peak. And trip purpose proportions are sorted by each time period from raw data of Seoul household trip study, then the value of travel time for each time period is estimated with these sorted purpose proportions. A case study of Seoul Jung-gu and Yongsan-gu performed with newly estimated time value by time periods. The result of benefit calculation with the daily constant time value is overestimated approximately annual 2.5 billion Won compared by time values by time periods. The demands of toll roads are also overestimated with the existing daily constant time value by daily 3,500 vehicles and total revenue of toll roads are overestimated by annually 1 billion Won. In conclusion, the value of travel time by each time period enables the more precise economic evaluation of the transportation facility investment projects, mode choice behavior, and route choice behavior especially for toll roads.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.209-221
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• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Information and Communications Magazine
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• v.25 no.7
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• pp.10-15
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• 2008

최근 고속도로의 길이와 운전 차량 수가 빠른 속도로 증가하고 있어 운전자들에게 고속도로 교통상황를 신속하고 정확하게 제공하는 것이 중요한 문제로 대두되고 있다. 고속도로통행료수납시스템(TCS: Toll Collection Systrem)은 전국 고속도로를 주행하는 차량의 통행 정보를 실시간으로 제공하므로 교통 상황 예측에 유용하게 활용될 수 있다. TCS 자료는 차량이 입구영업소를 통과한 후 출구영업소를 통과하는 데 소요된 시간으로서, 운전한 시간, 휴게소 체류시간 등을 모두 포함한 통행시간으로 운전자의 운전 특성, 통행 목적, 피로의 정도에 따라 편차가 크게 나타난다. TCS 자료의 통행시간을 기초로 예측된 정보는 이러한 불확실성을 포함하고 있기 때문에 이를 활용하기 다양한 데이터처리 기법이 필요하다. 본 논문에서는 TCS 자료의 효율적인 전처리 및 교통 예측 기법 현황에 대하여 기술하고 향후 발전 방향을 제시하였다.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.672-675
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• 2009

BMS(Bus Management System)의 핵심인 버스도착예정시간을 산출하는 데 있어서 기존 대부분의 도시에서는 시계열 모형의 이동평균법, 칼만필터링 등으로 버스도착예정시간을 예측하고 있으나 이는 급격한 통행량의 변화 또는 급작스러운 사고, 신호체계 등에 적응 할 수 없다. 따라서 본 논문에서는 주변 도로의 통행량에 따른 버스의 정류장 도착시간을 예측하는 방법을 제안 한다. 주변 도로의 통행량과 실제 버스의 통행시간을 실측하여 기록, 학습하고 모델링하여 미래의 버스의 운행시간을 예측하는 방법이다. 또, 이동평균법에 의한 버스도착시간 예측결과와 본 논문에서 제안하는 결과와 비교, 분석하였다.

• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.136-141
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• 2003

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.43-54
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• 2000

이 연구는 버스정보 시스템 설계에 필요한 운행시격 결정과 통행시간 예측을 위한 알고리즘 개발을 다룬다. 운행시격 결정 문제는 버스와 같은 대중교통 수단을 운영하는데 중요한 요소 중에 하나이다. 기존 연구는 버스 운행비용과 승객비용의 합을 최소로 하는 운행시 격을 찾는데 초점을 두고 이다. 이때 승객비용이란 승객 대기비용과 승객 교통비용의 합으로 이루어진다. 그런데 우리나라와 같이 버스회사 수입이 전액 운행수입에만 의존하는 경우엔 이러한 접근 방식이 타당하지 않다. 기존의 방식과 다르게 승객비용으로 승객 이탈비용을 사용하여 버스의 최적 운행시 격을 구하는 것이 이 연구의 목적이다. 먼저 정류장이 하나인 경우에 대해 해석적 방법으로 풀고, 정류장이 여러 개인 경우에 대해서는 시뮬레이션 기법을 적용한다. 또한 이 연구는 신뢰성이 높고 정확한 통행시간 예측정보를 산출하기 위해 2 단계 예측 기법과 전문가시스템을 이용하는 자료융합 알고리즘을 개발한다. 정확한 정보를 제공하려면 교통정보 수집원을 통해 얻는 자료가 정확해야 하고, 또한 교통상황 변화에 따라 실시간으로 통행시간을 예측하는 것이 필요하다. 이 연구는 AVL(Automatic Vehicle Location)시스템을 이용한 버스정보시스템에서 실시간 데이터와 과거 데이터를 융합하여 통행시간을 예측하는 알고리즘을 개발한다. AVL 데이터를 수집하는 과정에서는 경제성을 고려하여 데이터를 수집한다. 그리고, 버스의 운행관리와 정확한 도착예정시간을 예측하기 위해 AVL시스템을 통해 얻은 데이터의 패턴을 분석하고 유고상황을 감지한다.

• 한국ITS학회:학술대회논문집
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• 2004.11a
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• pp.154-162
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• 2004

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.71-79
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• 2011

Traditionally, a dynamic network model is considered as a tool for solving real-time traffic problems. One of useful and practical ways of using such models is to use it to produce and disseminate forecast travel time information so that the travelers can switch their routes from congested to less-congested or uncongested, which can enhance the performance of the network. This approach seems to be promising when the traffic congestion is severe, especially when sudden incidents happen. A consideration that should be given in implementing this method is that travel time information may affect the future traffic condition itself, creating undesirable side effects such as the over-reaction problem. Furthermore incorrect forecast travel time can make the information unreliable. In this paper, a network-wide travel time prediction model under incidents is developed. The model assumes that all drivers have access to detailed traffic information through personalized in-vehicle devices such as car navigation systems. Drivers are assumed to make their own travel choice based on the travel time information provided. A route-based stochastic variational inequality is formulated, which is used as a basic model for the travel time prediction. A diversion function is introduced to account for the motorists' willingness to divert. An inverse function of the diversion curve is derived to develop a variational inequality formulation for the travel time prediction model. Computational results illustrate the characteristics of the proposed model.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.835-845
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• 2013

Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.