• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.77-86
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• 2005

Level of service(LOS) is a quantify measure describing operational conditions within a traffic stream, generally, in terms of such service measures as speed, travel time, freedom to measures, traffic interruptions, comfort and convenience. The LOS is leveled by highway facilities according to measure of effectiveness(MOE) and then used to evaluate performance capacity. The current evaluation of a urban road is performed by only a aspect of traffic operation without any concepts of safety. Therefore, this paper presents a method for evaluation of risk order for urban road with new MOE, user cost analysis, considering both smooth traffic operation(congestion) and traffic safety(accident). The user coat is included traffic accident cast by traffic safety and traffic congestion cost by traffic operation. First of all, a number of traffic accident and accident rate by highway geometric is inferred from urban road traffic accident prediction model (Poul Greibe(2001)) Secondly, a user cost is inferred as traffic accident cast and traffic congestion cost is putting together. Thirdly, a method for evaluation of a urban road is inferred by user cost analysis. Fourthly a accident rate by segment predict with traffic accidents and data related to the accidents in $1996{\sim}1998$ on 11 urban road segments, Gwang-Ju, predicted accident rate. Traffic accident cost predict using predicted accident rate, and, traffic congestion cost predict using predicted average traffic speed(KHCM). Fifthly, a risk order are presented by predicted user cost at each segment in urban roads. Finally, it si compared and evaluated that LOS of 11 urban road segments, Gwang-Ju, by only a aspect of traffic operation without any concepts of safety and risk order by a method for evaluation of urban road in this paper.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.

• Journal of Korean Society of Transportation
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• v.15 no.4
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• pp.53-71
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• 1997

This paper presents a methodology for estimating average travel speed using volume and occupancy data from single magnetic loop detectors for signalized arterials. Three methods were developed and evaluated using field data: VPLUSKO method, fuzzy control method, and neural network method. While the VPLUSKO method is easy to apply, it results poor performances compared to other methods. The neural network method showed the best performances among the candidate methods. This method revealed the weakness in transferability, however. From limited cases of field test, it was concluded that the method of the fuzzy control application showed reasonable performance of estimation. It was also demonstrated that the fuzzy control method has the capability of transferability.

• 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.53-65
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• 2004

A series of accidents, which are non-recurrent and non-anticipated, are called incidents. These incidents make standard traffic flows interrupt, which result in the decrease of road capacity and a number of social and economic costs, such as the traffic congestion and air pollution. In order to prevent the hazard of incidents, domestic and foreign traffic management center are likely to opt auto-sense system with algorithms of auto-incident sense. However, it is evaluated that the algorithms have a low function with frequent wrong alarms, even if they accurately ry to speculate the incidents. In the case of bottleneck which has lack of road capacity, compared with other roads, due to inefficient road structured over-capacity of the demand of on-off ramp, the incidents regularly take place. Nonetheless, it can be more difficult to speculate the auto-incidents sense owing to similar incidents, such as the queue of in-out flows of cars and the change of road line. Throughout this research, the function of the model has improved excluding near road line in the module of the incidents which is based on the auto-incidents algorithms during the sense of the congestion of ramp areas.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.17-30
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• 2002

Incident duration prediction is one of the most important steps of the overall incident management process. An accurate and reliable estimate of the incident duration can be the main difference between an effective incident management operation and an unacceptable one since, without the knowledge of such time durations, traffic impact can not be estimated or calculated. This research presents several multiple linear regression models for incident duration prediction using data consisting of 384 incident cases. The main source of various incident cases was the Traffic Incident Reports filled out by the Motorist Assistant Units of the Korea Highway Corporation. The models were proposed separately according to the time of day(daytime vs. nighttime) and the fatality/injury incurred (fatality/injury vs. property damage only). Two models using an integrated dataset, one with an intercept and the other without it, were also calibrated and proposed for the generality of model application. Some findings are as follows ; ?Variables such as vehicle turnover, load spills, the number of heavy vehicles involved and the number of blocked lanes were found to significantly affect incident duration times. ?Models, however, tend to overestimate the duration times when a dummy variable, load spill, is used. It was simply because several of load spill incidents had excessively long clearance times. The precision was improved when load spills were further categorized into "small spills" and "large spills" based on the size of vehicles involved. ?Variables such as the number of vehicles involved and the number of blocked lanes found not significant when a regression model was calibrated with an intercept. whereas excluding the intercept from the model structure signifies those variables in a statistical sense.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.46-56
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• 2021

Predicting the incident clearance time is important for eliminating the high transportation costs and congestion from non-repetitive congestion caused by incidents. In this study, the factors influencing the clearance time suitable for domestic road conditions were analyzed, using a training dataset for predicting the incident clearance time using artificial neural networks. In a previous study, the under-prediction problem for high incident clearance time was used. In the present study, over-sampling training data applied using the SMOGN technique was obtained and applied to the model as a solution. As a result, the DNN model applying the SMOGN technique could compensate for the limitations of the previously developed prediction model by predicting the clearance time with the highest accuracy among the models developed in the research process with MAE = 18.3 minutes.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Proceedings of the KOR-KST Conference
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• 1996.02a
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• pp.29-58
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• 1996

현재 서울은 교통사고문제, 교통혼잡문제와 자동차로부터 배출되는 유해가스에 의한 대기환경오염문제를 갖고 있다. 본 논문의 목적은 대기환경오염을 악화시키는 자동차의 주된 배출오염가스인 일산화탄소, 탄화수소 및 질소화합물량을 교차로 교통량을 중심으로 어떻게 계산하는가에 있다. 연구의 대상지역은 서울시의 송파구 교차로들과 도심지역의 링크들을 선택하였다. 그리고 교통량, 지체시간, 링크길이, 정지회수, 운행속도, 주행속도 등을 고려하여, 제작차 배출가스 허용기준, 총량기준, 불량차 기준, 속도기준 등에 의한 계수산정 및 TRAF-NETSIM에 의한 시뮬레이션을 통하여 일정 도로상의 제한된 범위내의 배출량 산출을 시도하였다. 본 연구의 결과에 의하면 첫째, 어느 방법이든 실제배출량의 정확한 값을 표현 할 수는 없는 것이고, 단지 상대적인 비교에 의하면 배출가스량의 수준을 추정 할 수 있었다. 또한, 시뮬레이션 배출율표를 우리 현실에 맞는 자료에 의해 수정할 수 있다면, 그 결과는 실제량에 보다 근접할 수 있을 것이다. 둘째, 서울도로의 현재 혼잡상태에서 속도의 저감에 배출량이 민감한 반응을 보이고 있다는 사실이다. 셋째, 교통량변화에 따른 배출량의 변화가 일산화탄소에서 가장 심하게 나타남을 알 수 있었고, 신호운영상의 옵셋값이 잘못 설정되면 탄화수소는 35%, 일산화탄소는 40%, 질소산화물은 75%까지 초과발생할 수 있으며, 유해배출가스량을 최소화시키는 측면에서의 신호최적화를 위해서는 현재의 Stop Penalty는 상향 조정되어야 함이 밝혀졌다. 앞으로는 자동차로 인한 대기환경오염 농도의 저감을 위해서는 도로별 자동차 유해가스 배출 총량규제 방안도 고려해 볼 가치가 있으며, 이를 위해 환경공학과 교통공학의 다학제적 공동연구가 지속적으로 필요하며 교통정책에 반영되어야 할 것이다.분석, 리용수 학모형대이십일세기상해항공운량진행예측, 제출발전상해항공운수적전 략목표급발전중점. 예측2020년 상해항공항총객운탄토량4300만인/년, 화운량달120만돈; 2050년객운량장달18150만인차/년, 화운량518만돈. 사, 발전상해민항기출경제정책, 제출위료$\ll$진흥상해, 개발포동, 복무전국, 면향세계$\gg$ 화도이십일세기중기국민경제달도중등발달국가수 평굉관전략목표적실현, 제료필수재지도사상상파교통운수진정방도전략 산업지위, 환응재관리체제상채취과단유효적개혁조시, 재기출경제정책 상급여대력부지. 오, 전략목표, 위파상해건설성위태평양서안최대적경제, 김융, 무역적중심, 요구상해항공항성위화동지구통향세계각지항선망출발참, 구성대외개방선면축심, 실현국제항선적함접화국내항반적전항, 형성다축심복사식항선망; 가강기장건설, 개피포동제이국제기장건설, 괄응포동개발경제발전적수요. 부화개시일은 각 5월 26일과 5월 22일이었다. 11. 6월 중순에 애벌레를 대상으로 처리한 Phenthoate EC가 96.38%의 방제가로 약효가 가장 우수하였고 3월중순 및 4월중순 월동후 암컷을 대상으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압주입시험에서도 확인된다.. It was resulted from increase of weight of single cocoon. "Manta"2.5ppm produced 22.2kg of cocoon. It is equal to 9% increase i

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.497-507
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• 2015

Nonrecurrent congestion of freeway was primarily caused by incident. The main cause of incident was known as a traffic accident. Therefore, accurate prediction of traffic incident clearance time is very important in accident management. Traffic accident data on freeway during year 2008 to year 2014 period were analyzed for this study. KNN(K-Nearest Neighbor) algorithm was hired for developing incident clearance time prediction model with the historical traffic accident data. Analysis result of accident data explains the level of accident significantly affect on the incident clearance time. For this reason, incident clearance time was categorized by accident level. Data were sorted by classification of traffic volume, number of lanes and time periods to consider traffic conditions and roadway geometry. Factors affecting incident clearance time were analyzed from the extracted data for identifying similar types of accident. Lastly, weight of detail factors was calculated in order to measure distance metric. Weight was calculated with applying standard method of normal distribution, then incident clearance time was predicted. Prediction result of model showed a lower prediction error(MAPE) than models of previous studies. The improve model developed in this study is expected to contribute to the efficient highway operation management when incident occurs.