• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.160-168
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• 2017

In the past 10 years, the accidents caused by drowsy driving have occupied about 23% of all traffic accidents in Korea expressway network and this rate is the highest one among all accident causes. Unlike other types of accidents caused by speeding and distraction to the road, the accidents by drowsy driving should be managed differently because the drowsiness might not be controlled by human's will. To reduce the number of accidents caused by drowsy driving, researchers previously focused on the spot based analysis. However, what we actually need is a segment (link) and occurring time based analysis, rather than spot based analysis. Hence, this research performs initial effort by adapting link concept in terms of drowsy driving on highway. First of all, we analyze the accidents caused by drowsy in historical accident data along with their road environments. Then, links associate with driving time are analyzed using digital tachograph (DTG) data. To carry this out, negative binomial regression models, which are broadly used in the field, including highway safety manual, are used to define the relationship between the number of traffic accidents on expressway and drivers' behavior derived from DTG. From the results, empirical Bayes (EB) and potential for safety improvement (PSI) analysis are performed for potential risk segments of accident caused by drowsy driving on the future. As the result of traffic accidents caused by drowsy driving, the number of the traffic accidents increases with increase in annual average daily traffic (AADT), the proportion of trucks, the amount of DTG data, the average proportion of speeding over 20km/h, the average proportion of deceleration, and the average proportion of sudden lane-changing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.4
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• pp.11-23
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• 2011

The objective of this paper is to develop a daily pattern clustering algorithm using historical traffic data that can reliably detect under various traffic flow conditions in urban streets. The developed algorithm in this paper is categorized into two major parts, that is to say a macroscopic and a microscopic points of view. First of all, a macroscopic analysis process deduces a daily peak/non-peak hour and emphasis analysis time zones based on the speed time-series. A microscopic analysis process clusters a daily pattern compared with a similarity between individuals or between individual and group. The name of the developed algorithm in microscopic analysis process is called "Two-step speed clustering (TSC) algorithm". TSC algorithm improves the accuracy of a daily pattern clustering based on the time-series speed variation data. The experiments of the algorithm have been conducted with point detector data, installed at a Ansan city, and verified through comparison with a clustering techniques using SPSS. Our efforts in this study are expected to contribute to developing pattern-based information processing, operations management of daily recurrent congestion, improvement of daily signal optimization based on TOD plans.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.44-52
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• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.621-624
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• 2007

본 논문에서는 교통 이력 데이터 분석을 통해 운전자에게 유용한 정보를 식별하는 방법을 제안한다. 이를 위해 차량 속도 분석을 이용한 요일, 시간 별 도로 중요도, 도로속성을 이용한 도로 중요도 결정 시스템을 개발하였다. 또한, 돌발상황 발생 시에도 그 예측 영향범위에 따른 선별적 정보제공이 가능한 시스템을 개발하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.83-85
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• 2007

현재 한국도로공사에서 운영하는 교통 관리 시스템은 일반 사용자에게 실시간 교통 정보를 제공하는 목적으로 만들어졌다. 그러므로 장기간의 교통자료를 모아서 얻은 교통 이력자료를 가지고 분석 및 연구를 수행하는데 제약이 많다. 본 논문에서는 교통 연구자에게 교통 이력자료를 가지고 분석 및 연구를 수행할 수 있는 데이터베이스를 위한 설계 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.290-292
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• 2007

현재 한국도로공사에서 운영하는 고속도로 교통관리시스템(FTMS)과 우회도로 교통정보시스템(ARTIS)은 차량 검지 장치와 CCTV 를 통해 실시간 교통 자료를 수집하고, 도로전광표지(VMS), 방송, 인터넷 등 다양한 매체로 교통 정보를 제공하는 시스템이다. 이러한 시스템들은 매일 도로에서 수집되는 엄청난 양의 교통 자료를 실시간 교통 정보 제공하는데 목적을 두고 있어, 교통 자료를 가공하고 처리하여 분석을 수행하는 연구 환경을 제공하는데 어려움이 많다. 본 논문에서는 여러 시스템으로부터 대용량의 교통 자료를 가져와 하나의 통합 데이터베이스로 구축하고, 이를 통해 얻은 교통 이력 자료를 연구할 수 있는 환경을 제안한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2465-2471
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• 2013

This paper develops a travel time prediction algorithm that can be used for real-time application. The algorithm searches for the most similar pattern in historical travel time database as soon as a series of real-time data become available. Artificial neural network approach is then taken to forecast travel time in the near future. To examine the performance of this algorithm, travel time data from Gyungbu Highway were obtained and the algorithm is applied. The evaluation shows that the algorithm could predict travel time within 4% error range if comparable patterns are available in the historical travel time database. This paper documents the detailed algorithm and validation procedure, thereby furnishing a key to generating future travel time information.

• 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.

• International Journal of Railway
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• v.5 no.1
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• pp.29-37
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• 2012

The rail sector, despite its potential for curtailing the present chaotic transport situation in Lagos, remains inefficient and underutilized. In spite of past initiatives and the current attempt by the Lagos State Government to provide a mass transit rail service in Lagos, the share of rail mode in the transport sector has not been encouraging and the railway plays an insignificant role in urban mass transit in Lagos at present. This paper sets out to assess the usage of the Lagos mass transit trains. Hence, the paper determines the passenger traffic flow along the rail route in Lagos and the number of passengers carried between 2000 and 2009 by the Lagos Mass Transit Trains (LMTT) with a view to assessing the usage of the present LMTT. This paper also discusses the historical development of Nigerian railway and describes rail transport in Lagos. Data were obtained through secondary sources coupled with review of literature. The result of this study shows that for a period of 10 years (2000-2009), Lagos mass transit trains carried a total of 9,870,101 passengers, which gives an average of 987,010 passengers annually. This suggests that the service of the Lagos mass transit train is grossly underutilized. However, LMTT contributes enormously to NRC by carrying 68.5% of the total passenger traffic of NRC between 2000 and 2009. In terms of passenger traffic flow along the route of LMTT, for a period of 1 year, Agbado station recorded the largest number of passengers (393,811), followed by Ijoko (163,652) and Iddo (120,787), while Iganmu station has the lowest number of rail commuters (16,919). This study also discloses that the major commodities hauled by Lagos district of NRC from Lagos to the northern parts of the country in 2007 are Cars, Cement, Billet and Wheat.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.