• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.887-895
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• 2018

This paper presents a braking model that can be used to design the safety distance of a train control system and a train braking system to increase the volume of traffic. For the braking model, a train set (electric multiple unit composed 6 cars) was tested. The factors that can affect the braking characteristics include the friction coefficient, braking pressure, and regenerative braking. The braking pressure was classified into service and emergency braking and reflected the characteristics of the vehicle. The external force acting on the running railway car was tested in accordance with KS R 9217, and the running resistance of the train is presented in the form of a polynomial. The dynamic behavior of the train running on a straight flat line was simulated using UM 8.3. The results were validated with experimental data, and the results were reasonable. With the validated model, a stopping distance was determined according to the initial braking speed and compared with the deceleration braking model. In addition, a safety distance for the train control system could be changed according to the frictional coefficient limits. These results are expected to be useful for analyzing the dynamic behavior of trains, and for analyzing various railway environments and improving the braking performance.