• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.107-118
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• 2010

Wireless communication technologies including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) enable the development of more sophisticated and effective traffic information systems. This study presents a method to estimate vehicular travel times in a vehicular ad hoc network (VANET) environment. A novel feature of the proposed method is estimating individual vehicle travel times through advanced on-board units in each vehicle, referred to as self-estimated travel time in this study. The method uses travel information including vehicle position and speed at each given time step transmitted through the V2V and V2I communications. Vehicle trajectory data obtained from the VISSIM simulator is used for evaluating the accuracy of estimated travel times. Relevant technical issues for successful field implementation are also discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1115-1122
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• 2013

Recently the demands for traffic information tend to increase, and travel time might one of the most important traffic information. To effectively estimate exact travel time, highly reliable traffic data collection is required. BIS(Bus Information System) data would be useful for the estimation of the route travel time because BIS is collecting data for the bus travel time on the main road of the city on real-time basis. Traditionally use of BIS data has been limited to the realm of bus operating but it has not been used for a variety of traffic categories. Therefore, this study estimates a route travel time on road networks in urban areas on the basis of real-time data of BIS and then eventually constructs regression models. These models use an explanatory variable that corresponds to bus travel time excluding service time at the bus stop. The results show that the coefficient of determination for the constructed regression model is more than 0.950. As a result of T-test performance with assistance from collected data and estimated model values, it is likely that the model is statistically significant with a confidence level of 95%. It is generally found that the estimation for the exact travel time on real-time basis is plausible if the BIS data is used.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.153-166
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• 1998

본 연구에서는 도시 가로망에서의 구간 통행시간을 예측하기 위하여 time-frequency 분석의 일종인 웨이브렛변환과 RBF신경망 모형을 이용한 예측모형을 개발하였다. 웨이브렛 변환을 이용한 시계열 자료 분석을 통해서 통행시간에 내재되어 있는 다양한 패턴의 특징을 추출함으로써 오전/오후의 첨두현상, 신호교차로의 현시주기 등 주기적으로 발생되는 요인들에 의해서 통행시간 시계열 자료의 패턴에 나타나는 규칙성을 분석해 내었다. 분석된 패턴정보에 대한 규명은 카오스 이론을 근간으로한 시간지연좌표를 이용하여 시계열 자료의 규칙성을 시각적으로 판별하여 예측모형 구축에 활용하도록 하였다. 또, RBF신경망을 이용하여 예측범위의 공간적/시간적 확대에 따른 모형 구축에 소요되는 시간을 최소화하도록 하였으며, 시내버스 노선의 정류장간 운행시간 예측을 통해서 기존 연구에서 제기되었던 현실세계의 단순화, 다단계 예측시 정확성 등의 문제를 해결하였다. 예측실험결과 웨이브렛 변환을 데이터의 전처리 과정에 삽입하여 링크 통행시간의 패턴정보 예측에 활용할 경우, 기존의 예측모형에 비해서 훨씬 정확한 예측이 가능한 것으로 나타났으며, RBF 신경망은 짧은 학습시간에도 불구하고 역전파 신경망보다 우수한 예측력을 갖고 있는 것으로 밝혀졌다.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• 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.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.671-680
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• 2016

This research suggested a new real-time traffic signal control algorithm using fusion data of the travel time and the occupancy rate. This research applied the travel time data of traffic information system to traffic signal operation, and developed the signal control process using the degree of saturation that was estimated from the travel time data. This algorithm estimates a queue length from the travel time based on a deterministic delay model, and includes the process to change from the queue length to the degree of saturation. In addition, this model can calculate the traffic signal timings using fusion data of the travel time and the occupancy rate based on the saturation degree. The micro simulation analysis was conducted for effectiveness evaluation. We checked that the average delay decreased by up to 27 percent. In addition, we checked that this signal control algorithm could respond to a traffic condition of oversaturation and detector breakdown effectively and usefully. This research has important contribution to apply the traffic information system to traffic signal operation sectors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.207-235
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• 2021

This paper examines the effects of ambient air pollution by ozone and particulate matter on traveling by mode of transport. We estimate the SUR model of travel time by different modes of transportation using individual level data of travel diaries. We find that, as air pollution levels rises, traveling by privately-owned vehicles increases but traveling by bus decreases. Our results also show that, when an air quality alert is issued, bus traveling increases in an effort to reduce pollution levels, but traveling by own car does not change and traveling by train declines. This suggests that alert programs may not be highly effective in reducing air pollution emissions from vehicles because voluntary switching to public transportation induced by air quality alerts is outweighed by individual effort of avoiding exposure to pollution.

• 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.