• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.111-112
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• 2023

학교, 대형 쇼핑몰, 공항 등과 같은 큰 실내 공간에서는 군중의 동선과 밀도를 파악하고 관리하는 것은 안전사고와 연관되어 있어 매우 중요하다. 와이파이 센싱은 기존에 존재하던 CCTV 카메라나 센서를 활용한 혼잡도 관리보다 효율적이고 정확한 방식으로 추정하는 데 도움이 되며, 설치 및 유지보수 측면에서도 효율적이다. 본 논문에서는 실내 환경에서 군중 수를 추정하기 위해 딥 러닝을 이용한 무선랜 신호 분석 기법을 제안한다. 송수신기가 같은 공간에 위치했던 기존 연구들과는 달리 본 논문에서는 송신기와 수신기가 서로 다른 공간에 배치된 환경에서도 무선랜 수신 신호를 통해 다른 공간의 군중 수를 정확히 예측할 수 있다는 것을 실험으로 검증하였다.

• 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.

• 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.33 no.3
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• pp.256-267
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• 2015

The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.58-64
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• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.141-148
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• 2009

An inappropriate evaluation of capacity leads to the incorrect and impractical result due to the transfer of error to the analysis and the evaluation on highway system. The traffic accident which reduces the capacity of road temporarily generates unpredictable congestion, causing difficulties in congestion management. Therefore, this research aims on the measurement of the capacity of the road in accordance to the speed at the accident which is a basic factor when performing analysis. Based on the given approach, the behavior of a vehicle in highway is understood to develop model of critical gap and model of maximum flow rate with respect to the speed of traffic flow. With the established model, the reduction rate of the capacity in highway system at the accident is measured. The result shows that the capacity is reduced by 37% when the speed of the traffic flow is 40km/h. Although the developed model can't be verified clearly, this research has shown that the reduction rate of the capacity in road system has a close relation to the speed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.159-167
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• 2009

The vehicles play a significant role in modern people's life as economy grows. The development of car navigation system(CNS) provides various convenience because it shows the driver where they are and how to get to the destination from the point of source. However, the existing map-based CNS does not consider any environments such as traffic congestion. Given the same starting point and destination, the system always provides the same route and the required time. This paper proposes a path planning method with traffic prediction by applying historical driving information to the Fuzzy theory and Bayesian update. Fuzzy theory classifies the historical driving information into groups of leaving time and speed rate, and the traffic condition of each time zone is calculated by Bayesian update. An ellipse area including starting and destination points is restricted in order to reduce the calculation time. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with real navigation.

• 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