• Title/Summary/Keyword: Traffic state estimation

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Analysis of Moment Effect of Bridge Design Live Load KL-510 by Statistical Analysis of WIM Data of Expressway (고속도로 WIM 데이터의 통계분석을 통한 교량 설계활하중 KL-510의 모멘트 효과 분석)

  • Paik, Inyeol;Jeong, Kilhwan
    • Journal of Korean Society of Steel Construction
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    • v.29 no.6
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    • pp.467-477
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    • 2017
  • The live load effect of KL-510 of the current Korean bridge design code is examined by comparing with that of the multiple trucks of which the weights are statistically estimated from measured traffic data as well as with those of the related live load models. The truck weight data measured on the expressway before and after overweight enforcement are used to obtain the truck weights following the same procedures in deciding the live load model of the design codes and the results are compared with the load effect of KL-510. KL-510 yields a very uniform loading effect compared with the multiple truck effects when the weights are estimated from the data which contains some of the heavy trucks over the operational weight limit. KL-510 yields consistent results with the live load of AASHTO LRFD and shows less variation than the past load model DB-24 over the span lengths considered in this study. As a result of this research, the actual truck combinations equivalent to the notional KL-510 load model are constructed and it can be applied to the evaluation of the existing bridge and the calibration of the load factor of the permit vehicle.

Dynamic OD Estimation with Hybrid Discrete Choice of Traveler Behavior in Transportation Network (복합 통행행태모형을 이용한 동적 기.종점 통행량 추정)

  • Kim, Chae-Man;Jo, Jung-Rae
    • Journal of Korean Society of Transportation
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    • v.24 no.6 s.92
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    • pp.89-102
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    • 2006
  • The purpose of this paper is to develop a dynamic OD estimating model to overcome the limitation of depicting teal situations in dynamic simulation models based on static OD trip. To estimate dynamic OD matrix we used the hybrid discrete choice model(called the 'Demand Simulation Model'), which combines travel departure time with travel mode and travel path. Using this Demand Simulation Model, we deduced that the traveler chooses the departure time and mode simultaneously, and then choose his/her travel path over the given situation In this paper. we developed a hybrid simulation model by joining a demand simulation model and the supply simulation model (called LiCROSIM-P) which was Previously developed. We simulated the hybrid simulation model for dependent/independent networks which have two origins and one destination. The simulation results showed that AGtt(Average gap expected travel time and simulated travel time) did not converge, but average schedule delay gap converged to a stable state in transportation network consisted of multiple origins and destinations, multiple paths, freeways and some intersections controlled by signal. We present that the hybrid simulation model can estimate dynamic OD and analyze the effectiveness by changing the attributes or the traveler and networks. Thus, the hybrid simulation model can analyze the effectiveness that reflects changing departure times, travel modes and travel paths by demand management Policy, changing network facilities, traffic information supplies. and so on.

Driver Assistance System for Integration Interpretation of Driver's Gaze and Selective Attention Model (운전자 시선 및 선택적 주의 집중 모델 통합 해석을 통한 운전자 보조 시스템)

  • Kim, Jihun;Jo, Hyunrae;Jang, Giljin;Lee, Minho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.16 no.3
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    • pp.115-122
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    • 2016
  • This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.