• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.183-193
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• 2019

This study developed a shockwave detection and prediction of their extinction point method based on continuous wavelet transform using trajectory data from probe vehicles equipped with automotive sensors.. To analyze the effectiveness of the proposed method, this paper proposed two measures which are a distance error between the extinction points of the predictor and an time-location error of the extinction points. The proposed concept was proved using the micro simulation based experiment with three exogenous variables of traffic volume, lane-close duration, market penetration of probe vehicles. The analysis results show that the proposed method is capable of detecting the traffic shockwaves as well as predicting their extinction point, and also that the accuracy of the proposed method is highly dependent on the rate of the probe vehicles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1297-1308
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• 2015

In Korea, the local traffic safety master plan has been established and implemented according to the Traffic Safety Act. Each local government is required to establish a customized traffic safety policy and share roles for improvement of traffic safety and this means that local governments lead and promote effective local traffic safety policies fit for local circumstances in substance. For implementing efficient traffic safety policies, which accord with many-sided characteristics of local governments, the prediction of community-based traffic accidents, which considers local characteristics and the analysis of accident influence factors must be preceded, but there is a shortage of research on this. Most of existing studies on the community-based traffic accident prediction used social and economic variables related to accident exposure environments in countries or cities due to the limit of collected data. For this reason, there was a limit in applying the developed models to the actual reduction of traffic accidents. Thus, this study developed a local traffic accident prediction model, based on smaller regional units, administrative districts, which were not omitted in existing studies and suggested a method to reflect traffic safety facility and policy variables that traffic safety policy makers can control, in addition to social and economic variables related to accident exposure environments, in the model and apply them to the development of local traffic safety policies. The model development result showed that in terms of accident exposure environments, road extension, gross floor area of buildings, the ratio of bus lane installation and the number of crossroads and crosswalks had a positive relation with accidents and the ratio of crosswalk sign installation, the number of speed bumps and the results of clampdown by police force had a negative relation with accidents.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.131-143
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• 2014

We propose a no-reference perceptual fog density and visibility prediction model in a single foggy scene based on natural scene statistics (NSS) and perceptual "fog aware" statistical features. Unlike previous studies, the proposed model predicts fog density without multiple foggy images, without salient objects in a scene including lane markings or traffic signs, without supplementary geographical information using an onboard camera, and without training on human-rated judgments. The proposed fog density and visibility predictor makes use of only measurable deviations from statistical regularities observed in natural foggy and fog-free images. Perceptual "fog aware" statistical features are derived from a corpus of natural foggy and fog-free images by using a spatial NSS model and observed fog characteristics including low contrast, faint color, and shifted luminance. The proposed model not only predicts perceptual fog density for the entire image but also provides local fog density for each patch size. To evaluate the performance of the proposed model against human judgments regarding fog visibility, we executed a human subjective study using a variety of 100 foggy images. Results show that the predicted fog density of the model correlates well with human judgments. The proposed model is a new fog density assessment work based on human visual perceptions. We hope that the proposed model will provide fertile ground for future research not only to enhance the visibility of foggy scenes but also to accurately evaluate the performance of defog algorithms.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.209-221
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• 2009

Travel time estimation under given traffic conditions is important for providing drivers with travel time prediction information. But the present expressway travel time estimation process cannot calculate a reliable travel time. The objective of this study is to estimate the path travel time spent in a through lane between origin tollgates and destination tollgates on an expressway as a prerequisite result to offer reliable prediction information. Useful and abundant toll collection system (TCS) data were used. When estimating the path travel time, the path travel time is estimated combining the link travel time obtained through a preprocessing process. In the case of a lack of TCS data, the TCS travel time for previous intervals is referenced using the linear interpolation method after analyzing the increase pattern for the travel time. When the TCS data are absent over a long-term period, the dynamic travel time using the VDS time space diagram is estimated. The travel time estimated by the model proposed can be validated statistically when compared to the travel time obtained from vehicles traveling the path directly. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variaty of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.9
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• pp.1237-1247
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• 2012

The objective of this study was to evaluate the predictions of dry matter intake (DMI) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ${\pm}33.2$ kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination ($r^2$), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision ($r^2$ of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under-or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.35-42
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• 2005

Few studies have been carried out to find out the interaction of design speeds, operating speeds, and posted speeds though they have a complementary relationships. As an attempt to find the relationships, this study measured the speeds of the free flowing vehicles at four lane rural highways. In comparison of 95th percentile speeds and inferred design speeds determined from the road design manual with the geometric features of each sites, operating speeds were constantly higher than the inferred design speeds at the sites where the inferred design speed is under 110km/h. and the reverse situation was observed at the sites where the inferred design speed is over 130km/h. In the comparison of operating speeds and posted speeds. the range of the 85th percentile speeds at the sites where posted speeds is 80km/h was distributed from 95km/h to 110km/h. and the range was distributed from the 105km/h to the 120km/h at the sites where posted speeds is 90km/h. Multiple regression analysis was used to develop prediction equations for mean. 85th. and 95th percentile speeds at approach and curve midpoint locations. At the midpoint, only posted speeds influenced the mean, 85th. At the approach locations, the mean, 85th, 95th percentile speeds were influenced by posted speeds and length of the approach tangent.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.107-122
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• 2015

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant. CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.

• International Journal of Highway Engineering
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• v.16 no.1
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• pp.11-19
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• 2014

PURPOSES : The purpose of this article is to investigate the predicted life of jointed concrete pavement (JCP) with two variables effecting on axle load spectra (ALS). The first variable is different data acquisition methods whether using high-speed weigh-in-motion (HS-WIM) or not and the other one is spectra distribution due to overweight enforcement on main-lane of expressway using HS-WIM. METHODS : Three sets of ALS had been collected i) ALS provided by Korea Pavement Research Program (KPRP), which had been obtained without using HS-WIM ii) ALS collected by HS-WIM before the enforcement at Kimcheon and Seonsan site iii) ALS collected after the enforcement at the same sites. And all ALS had been classified into twelve vehicle classes and four axle types to compare each other. Among the vehicle classes, class 6, 7, 10 and 12 were selected as the major target for comparing each ALS because these were considered as the primary trucks with a high rate of overweight loading. In order to analyze the performance of JCP based on pavement life, fatigue crack and International Roughness Index (IRI) were predicted using road pavement design program developed by KPRP and each ALS with same annual average daily traffic (AADT) was applied to design slab thickness. RESULTS : Comparison ALS of KPRP with those of HS-WIM shows that the ALS of KPRP has a low percentage of heavy spectra such as 6~9 tonnes for single axle, 18~21 tonnes for tandem axle and 27~30 tonnes for tridem axle than other two ALS of HS-WIM in most vehicle classes and axle types. It means that ALS of KPRP was underestimated. And after the enforcement, percentage of heavy spectra close to 10 tonnes per an axle are lowered than before the enforcement by the effect of overweight enforcement because the spectra are related to overweight regulation. Prediction results of pavement life for each ALS present that the ALS of HS-WIM collected before the enforcement makes the pavement life short more than others. On the other hand, the ALS of KPRP causes the longest life under same thickness of slab. Thus, it is possible that actual performance life of JCP under the traffic like ALS of HS-WIM could be short than predicted life if the pavement was designed based on ALS provided by KPRP. CONCLUSIONS : It is necessary to choose more reliable and practical ALS when designing JCP because ALS can be fairly affected by acquisition methods. In addition, it is important to extend performance life of the pavement in service by controlling traffic load such as overweight enforcement.

• Journal of Korean Society of Transportation
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• v.21 no.4
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• pp.133-144
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• 2003

The current procedure of estimating accident reduction benefit shows fixed accident rates for each level of roads without considering the various characteristics of roadway geometries, and traffics. In this study, in order to solve the problems mentioned in the above, models were developed considering the characteristics of roadway alignments and traffic characteristics. The developed models can be used to estimate the accident rates on new or improved roads, In this study, only urban highways were included as a beginning stage. First of all. factors influencing accident rates were selected. Those factors such as traffic volumes. number of signalized intersections, the number of connecting roads, number of pedestrian traffic signals, existence of median barrier, and the number of road lane are also selected based upon the obtainability at the planning stage of roads. The relationship between the selected factors and accident rates shows strong correlation statistically. In this study, roads were classified into 4 groups based on number of lanes, level of roads and the existence of median barriers. The regression analysis had been performed for each group with actual data associated with traffic, roads. and accidents. The developed regression models were verified with another data set. In this study, in order to develop the proposed models, only data on a limited area were used. In order to represent whole area of the country with the developed models. the models should be re-analyzed with vast data.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.