• Title/Summary/Keyword: Traffic Flow Prediction

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Study on Influencing Factors of Traffic Accidents in Urban Tunnel Using Quantification Theory (In Busan Metropolitan City) (수량화 이론을 이용한 도시부 터널 내 교통사고 영향요인에 관한 연구 - 부산광역시를 중심으로 -)

  • Lim, Chang Sik;Choi, Yang Won
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.35 no.1
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    • pp.173-185
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    • 2015
  • This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

Time Series Analysis for Traffic Flow Using Dynamic Linear Model (동적 선형 모델을 이용한 교통 흐름 시계열 분석)

  • Kim, Hong Geun;Park, Chul Young;Shin, Chang Sun;Cho, Yong Yun;Park, Jang Woo
    • KIPS Transactions on Computer and Communication Systems
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    • v.6 no.4
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    • pp.179-188
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    • 2017
  • It is very challenging to analyze the traffic flow in the city because there are lots of traffic accidents, intersections, and pedestrians etc. Now, even in mid-size cities Bus Information Systems(BIS) have been deployed, which have offered the forecast of arriving times at the stations to passengers. BIS also provides more informations such as the current locations, departure-arrival times of buses. In this paper, we perform the time-series analysis of the traffic flow using the data of the average trvel time and the average speed between stations extracted from the BIS. In the mid size cities, the data from BIS will have a important role on prediction and analysis of the traffic flow. We used the Dynamic Linear Model(DLM) for how to make the time series forecasting model to analyze and predict the average speeds at the given locations, which seem to show the representative of traffics in the city. Especially, we analysis travel times for weekdays and weekends separately. We think this study can help forecast the traffic jams, congestion areas and more accurate arrival times of buses.

Relationships Between Average Travel Speed, Time-Delayed Rate, and Volume on Two-lane Highways with Simulation Data (2차로도로 평균 통행속도-총지체율-교통량 관계 곡선 재정립)

  • Moon, Jae-Pil;Kim, Yong-Seok
    • International Journal of Highway Engineering
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    • v.14 no.6
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    • pp.131-138
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    • 2012
  • PURPOSES : Two-lane highways have one lane in each direction, and lane changing and passing maneuvers take place in the opposing lane depending on the availability of passing sight distance. 2001 Korea Highway Capacity Manual (KHCM) is classified into two classes of two-lane highways (Type I, II), and average travel speed and time-delayed rate are used as measures of effectiveness (MOEs). However, since existing two-lane highways have both uninterrupted and interrupted traffic flow-system elements, a variety of free-flow speeds exhibits in two-lane highways. In addition, it is necessary to check if the linear-relationship between volumes and time-delayed rate is appropriate. Then, this study is to reestablish the relationship between average travel speed, time-delayed rate, and flow. METHODS : TWOPAS model was selected to conduct this study, and the free-flow speeds of passenger cars and the percentage of following vehicles observed in two-lane highways were applied to the model as the input. The revised relationships were developed from the computer simulation. RESULTS : In the revised average travel speed vs. flow relationship, the free-flow speed of 90km/h and 70km/h were added. It shows that the relationship between time delayed-rate and flow appeared to be appropriate with the log-function form and that there was no difference in time-delayed rate between the free flow speeds. In addition to revise the relationships, the speed prediction model and the time-delayed rate prediction model were also developed. CONCLUSIONS : The revised relationships between average travel speed, time-delayed rate, and flow would be useful in estimating the Level of Service(LOS) of a two-lane highway.

Strategies for Providing Detour Route Information and Traffic Flow Management for Flood Disasters (수해 재난 시 우회교통정보 제공 및 교통류 관리전략)

  • Sin, Seong-Il;Jo, Yong-Chan;Lee, Chang-Ju
    • Journal of Korean Society of Transportation
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    • v.25 no.6
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    • pp.33-42
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    • 2007
  • This research proposes strategies about providing detour route information and traffic management for flood disasters. Suggested strategies are based on prevention and preparation concepts including prediction, optimization, and simulation in order to minimize damage. Specifically, this study shows the possibility that average travel speed is increased by proper signal progression during downpours or heavy snowfalls. In addition, in order to protect the drivers and vehicles from dangerous situations, this study proposes a route guidance strategy based on variational inequalities such as flooding. However, other roads can have traffic congestion by the suggested strategies. Thus, this study also shows the possibility to solve traffic congestion of other roads in networks with emergency signal modes.

Travel Time Forecasting in an Interrupted Traffic Flow by adopting Historical Profile and Time-Space Data Fusion (히스토리컬 프로파일 구축과 시.공간 자료합성에 의한 단속류 통행시간 예측)

  • Yeo, Tae-Dong;Han, Gyeong-Su;Bae, Sang-Hun
    • Journal of Korean Society of Transportation
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    • v.27 no.2
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    • pp.133-144
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    • 2009
  • In Korea, the ITS project has been progressed to improve traffic mobility and safety. Further, it is to relieve traffic jam by supply real time travel information for drivers and to promote traffic convenience and safety. It is important that the traffic information is provided accurately. This study was conducted outlier elimination and missing data adjustment to improve accuracy of raw data. A method for raise reliability of travel time prediction information was presented. We developed Historical Profile model and adjustment formula to reflect quality of interrupted flow. We predicted travel time by developed Historical Profile model and adjustment formula and verified by comparison between developed model and existing model such as Neural Network model and Kalman Filter model. The results of comparative analysis clarified that developed model and Karlman Filter model similarity predicted in general situation but developed model was more accurate than other models in incident situation.

A Basic Study on Prediction Module Development of Collision Risk based on Ship's Operator's Consciousness (선박운항자 의식 기반 충돌 위험도 예측 모듈 개발에 관한 연구)

  • Park, Young-Soo;Park, Sang-Won;Cho, Ik-Soon
    • Journal of Navigation and Port Research
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    • v.39 no.3
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    • pp.199-207
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    • 2015
  • In ports of Korea, the marine traffic flow is congested due to a large number of vessels coming in and going out. In order to improve the safety and efficiency of these vessels, South Korea is operating with a Vessel Traffic Service System, which is monitoring its waters for 24 hours. However despite these efforts of the VTS (Vessel Traffic Service) officers, collisions are occurring continuously, the risk situation is analyzed that occurs once in about 20 minutes, the risk may be greater. It investigated to reduce these accidents by providing a safety standard for collision danger in a timely manner. Thus, this study has developed a risk prediction module to predict risk in advance. This module can avoid collision risk to adjust the speed and course of ship using a risk evaluation model based on ship operator's risk perspective. Using this module, the ship operators and VTS officers can easily be identified risks in complex traffic situations, so they can take an appropriate action against danger in near future including course and speed change. To verify the effectiveness of this module, this paper predicted the risk of each encounter situation and confirmed to be capable of identifying a risk changes in specific course and speed changes at Busan coastal water.

Accurate prediction of lane speeds by using neural network

  • Dong hyun Pyun;Changwoo Pyo
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.5
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    • pp.9-15
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    • 2023
  • In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

The Study for Estimating Traffic Volumes on Urban Roads Using Spatial Statistic and Navigation Data (공간통계기법과 내비게이션 자료를 활용한 도시부 도로 교통량 추정연구)

  • HONG, Dahee;KIM, Jinho;JANG, Doogik;LEE, Taewoo
    • Journal of Korean Society of Transportation
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    • v.35 no.3
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    • pp.220-233
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    • 2017
  • Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

Comparison of RANS, URANS, SAS and IDDES for the prediction of train crosswind characteristics

  • Xiao-Shuai Huo;Tang-Hong Liu;Zheng-Wei Chen;Wen-Hui Li;Hong-Rui Gao;Bin Xu
    • Wind and Structures
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    • v.37 no.4
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    • pp.303-314
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    • 2023
  • In this study, two steady RANS turbulence models (SST k-ω and Realizable k-ε) and four unsteady turbulence models (URANS SST k-ω and Realizable k-ε, SST-SAS, and SST-IDDES) are evaluated with respect to their capacity to predict crosswind characteristics on high-speed trains (HSTs). All of the numerical simulations are compared with the wind tunnel values and LES results to ensure the accuracy of each turbulence model. Specifically, the surface pressure distributions, time-averaged aerodynamic coefficients, flow fields, and computational cost are studied to determine the suitability of different models. Results suggest that the predictions of the pressure distributions and aerodynamic forces obtained from the steady and transient RANS models are almost the same. In particular, both SAS and IDDES exhibits similar predictions with wind tunnel test and LES, therefore, the SAS model is considered an attractive alternative for IDDES or LES in the crosswind study of trains. In addition, if the computational cost needs to be significantly reduced, the RANS SST k-ω model is shown to provide relatively reasonable results for the surface pressures and aerodynamic forces. As a result, the RANS SST k-ω model might be the most appropriate option for the expensive aerodynamic optimizations of trains using machine learning (ML) techniques because it balances solution accuracy and resource consumption.

En-Route Trajectory calculation using Flight Plan Information for Effective Air Traffic Management

  • Kim, Yong-Kyun;Jo, Yun-Hyun;Yun, Jin-Won;Oh, Taeck-Keun;Roh, Hee-Chang;Choi, Sang-Bang;Park, Hyo-Dal
    • Journal of Information Processing Systems
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    • v.6 no.3
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    • pp.375-384
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    • 2010
  • Trajectory modeling is foundational for 4D-Route modeling, conflict detection and air traffic flow management. This paper proposes a novel algorithm based Vincenty's fomulas for trajectory calculation, combined with the Dijkstra algorithm and Vincenty's formulas. Using flight plan simulations our experimental results show that our method of En-route trajectory calculation exhibits much improved performance in accuracy.