Korea Transport Institute

Metakaolin is a cementitious material used as admixture to produce high strength concrete. In Korea, the utilization of this material remained mainly limited to fireproof walls but began recently to find applications as a replacement for silica fume in the manufacture of high performance concrete. In order to evaluate and compare the mechanical properties and durability of concrete using metakaolin, the following tests were conducted on concrete specimens using various replacements of silica fume and metakaolin; mechanical tests such as compressive, tensile and flexural strength tests, durability tests like rapid chloride permeability test, immersion test in acid solution, repeated freezing and thawing test and accelerated carbonation test. Strength tests revealed that the most appropriate strength was obtained for a substitution rate of metakaolin to binder ranging between 10% and 15%. It was observed that the resistance to chloride ion penetration reduced significantly as the proportion of silica fume and metakaolin binders increased. The filler effect resulting from the fine powder of both binders was seen to ameliorate substantially the resistance to chemical attacks in comparison with ordinary concrete. Durability tests also verified that concrete using metakaolin bore most of the mechanical and durability characteristics exhibited by concrete using silica fume. The tests implemented in this study confirmed that metakaolin constitutes a promising material as a substitute for the cost prohibitive silica fume.

At least two important transportation planning activities rely on planning-level crash prediction models. One is motivated by the Transportation Equity Act for the 21st Century, which requires departments of transportation and metropolitan planning organizations to consider safety explicitly in the transportation planning process. The second could arise from a need for state agencies to establish incentive programs to reduce injuries and save lives. Both applications require a forecast of safety for a future period. Planning-level crash prediction models for the Tucson, Arizona, metropolitan region are presented to demonstrate the feasibility of such models. Data were separated into fatal, injury, and property-damage crashes. To accommodate overdispersion in the data, negative binomial regression models were applied. To accommodate the simultaneity of fatality and injury crash outcomes, simultaneous estimation of the models was conducted. All models produce crash forecasts at the traffic analysis zone level. Statistically significant ( p-values < 0.05) and theoretically meaningful variables for the fatal crash model included population density, persons 17 years old or younger as a percentage of the total population, and intersection density. Significant variables for the injury and property-damage crash models were population density, number of employees, intersections density, percentage of miles of principal arterial, percentage of miles of minor arterials, and percentage of miles of urban collectors. Among several conclusions it is suggested that planning-level safety models are feasible and may play a role in future planning activities. However, caution must be exercised with such models.

Short-term prediction is one of the essential elements of intelligent transportation systems (ITS). Although fine prediction methodologies have been reported, most prediction methods with current time-series data lead to inefficient predictions when current or future time-series data either exhibit fluctuations or abruptly change. In order to deal with this problem, a dynamic multi-interval traffic volume prediction model, based on the k-nearest neighbour non-parametric regression (KNN-NPR), is introduced in this study. In an empirical study with real-world data, the input parameters of the proposed model including the k-values for the nearest neighbours in the neighbourhood and the dm-values for the number of lags were optimised according to the multi-interval prediction horizon in order to immediately capture the directionality of the future states and to minimise the prediction errors. The presented model performed effectively in terms of prediction accuracy, despite multi-interval schemes, to the same degree as applications of the real ITS, even if the time-series data abruptly varied or exhibited wide fluctuations. It can clearly be seen that the proposed methodology is one of the promising system-oriented approaches in the area of multi-interval traffic flow forecasting.

Abstract This article investigates how perceived price, airline service quality, perceived value, passenger satisfaction and airline image determine passengers’ future behavioural intentions. To test the conceptual framework, structural equation modelling using a maximum likelihood estimator was applied to data collected from Australian international air passengers. It was found that there were significant relationships between the variables except for three paths. The three insignificant paths were the relationship between ‘perceived price and passenger satisfaction’, ‘service quality and airline image’ and ‘perceived value and airline image’. Perceived price, perceived value, passenger satisfaction, and airline image were each found to have a direct effect on passengers’ future behavioural intentions.

Automatic fare collection systems using smart card technology have become popular because they provide an efficient and cost-saving alternative to the manual fare collection method. In 2004, the City of Seoul, South Korea, introduced a smart card-based transit fare scheme, which was a distance-based, integrated fare collection and calculation system. Over the years, the system was extended twice and now can provide detailed information about public transit use in the region. This information includes each trip's boarding and alighting times and locations, as well as the connected trip chains with transfers. This paper examines possibilities for using such data for transportation planning application. First, a process to generate a travel time map is presented. For this, more than 100 million trip data are used to estimate travel times among stops. It is also demonstrated that transfer data can be readily obtainable because the on- and off-boarding information reside in the data set. Although transfers are considered to be important information for public transit planning, it has not been easy to collect such information. This study illustrates that transfer data can be used to locate the critical transfer points that need improvement. It is also demonstrated that a simple data query can quickly identify these locations. In addition, transfer trip patterns between two zones are analyzed, which provides meaningful information about passengers’ transfer location choice.

In this paper we study the weighted tardiness job-shop scheduling problem, taking into consideration the presence of random shop disturbances. A basic thesis of the paper is that global scheduling performance is determined primarily by a subset of the scheduling decisions to be made. By making these decisions in an a priori static fashion, which maintains a global perspective, overall performance efficiency can be achieved. Further, by allowing the remaining decisions to be made dynamically, flexibility can be retained in the schedule to compensate for unforeseen system disturbances. We develop a decomposition method that partitions job operations into an ordered sequence of subsets. This decomposition identifies and resolves a “crucial subset” of scheduling decisions through the use of a branch-and-bound algorithm. We conduct computational experiments that demonstrate the performance of the approach under deterministic cases, and the robustness of the approach under a wide range of processing time perturbations. We show that the performance of the method is superior, particularly for low to medium levels of disturbances.

The efficiency of cities, defined here as labour productivity, adjusted for differences in industry-mix, is hypothesised to be a function of the 'effective size' of the labour market of cities, defined as the average number of jobs available in less than t minutes to workers in the city. This hypothesis is verified on a sample of 23 French cities. This effective size of the labour market is further explained by three factors: the size of the city; the average potential job-home distance (sprawl); and the average speed at which journey to work takes place. The same sample of 23 cities is used to determine the elasticities of the effective size of the labour market with respect to each of these three factors.

This article is focused on the spatial variability of earthquake strong motion and its relationship with the performance of water-distribution pipelines provided by Los Angeles Department of Water and Power (ladwp) and residential buildings. Analyses of strong-motion characteristics and their correlations with pipeline and building damage were conducted with an immense geographical information system (gis) database for the 1994 Northridge earthquake, which was collected, digitized, and organized by researchers at Cornell. There are statistically significant correlations among pipeline repair rate (repairs/km) and peak ground velocity (pgv) for cast iron, ductile iron, asbestos cement, and steel pipe. Statistically significant regressions have been developed between damage ratio, dr (percent of existing structures with damage equal to or exceeding a particular damage factor, df [percent of building replacement cost]) and the magnitudes of seismic parameters, such as pgv and spectrum intensity (si). Regressions developed between dr and scaled seismic parameters (seismic parameters normalized with respect to df) resulted in predictive equations having a very high degree of statistical significance. Algorithms to visualize damage patterns for buildings were developed and validated to choose optimal gis mesh dimensions and contour intervals. Ordinary kriging was used to develop regressions of pipeline repair rate and residential building dr associated with 90% confidence pgv and spectrum intensity (weighted average pseudovelocity). Such regressions provide an explicit means of characterizing the uncertainty embodied in the strong-motion data.

Many studies focused on the development of crash prediction models have resulted in aggregate crash prediction models to quantify the safety effects of geometric, traffic, and environmental factors on the expected number of total, fatal, injury, and/or property damage crashes at specific locations. Crash prediction models focused on predicting different crash types, however, have rarely been developed. Crash type models are useful for at least three reasons. The first is motivated by the need to identify sites that are high risk with respect to specific crash types but that may not be revealed through crash totals. Second, countermeasures are likely to affect only a subset of all crashes—usually called target crashes—and so examination of crash types will lead to improved ability to identify effective countermeasures. Finally, there is a priori reason to believe that different crash types (e.g., rear-end, angle, etc.) are associated with road geometry, the environment, and traffic variables in different ways and as a result justify the estimation of individual predictive models. The objectives of this paper are to (1) demonstrate that different crash types are associated to predictor variables in different ways (as theorized) and (2) show that estimation of crash type models may lead to greater insights regarding crash occurrence and countermeasure effectiveness. This paper first describes the estimation results of crash prediction models for angle, head-on, rear-end, sideswipe (same direction and opposite direction), and pedestrian-involved crash types. Serving as a basis for comparison, a crash prediction model is estimated for total crashes. Based on 837 motor vehicle crashes collected on two-lane rural intersections in the state of Georgia, six prediction models are estimated resulting in two Poisson (P) models and four NB (NB) models. The analysis reveals that factors such as the annual average daily traffic, the presence of turning lanes, and the number of driveways have a positive association with each type of crash, whereas median widths and the presence of lighting are negatively associated. For the best fitting models covariates are related to crash types in different ways, suggesting that crash types are associated with different precrash conditions and that modeling total crash frequency may not be helpful for identifying specific countermeasures.

The objective of this research is to develop a dynamic model to forecast multi-interval path travel times between bus stops of origin and destination. The research also intends to test the proposed model using real-world data. This research was brought about by the shortcomings of the existing real-time based short-term-prediction models, which have been widely utilised for single interval predictions. The developed model is based on the Nearest Neighbour Non-Parametric Regression using historical and current data collected by the Automatic Vehicle Location technology. In a test with real-world bus data in Seoul, Korea, the proposed multi-interval-prediction model performed effectively in terms of both prediction accuracy and computing time.

This study presents a warning information system based on an innovate methodology to estimate accident likelihood in real time. Bayesian modeling approach implemented by the probabilistic neural network (PNN) is conducted to identify hazardous traffic conditions leading to potential accident occurrence. The proposed system displays warning signs to call drivers' attention for safer and careful driving once hazardous traffic conditions are observed by evaluating accident likelihood. It is believed that the proposed system to produce effective warning information for real-time safety enhancement could be a valuable tool to highway users and operators.

In South Korea, use of the smart card to pay public transit fares has grown since its introduction in 1996. The proportion of smart card use in Seoul, South Korea, is more than 90% for buses and 75% for Metro. In 2004, the Seoul metropolitan government introduced a new smart card system that has a distance-based fare system, which requires the input of detailed user data, such as boarding time and Global Positioning System–based vehicle location. To investigate the reliability of smart card data, the number of users of every Metro station in Seoul, gathered from smart card data, was directly compared with data obtained from the Seoul Metro Company. By using two simple manipulations to include daily variations and the number of cash users, smart card data appear to be statistically similar to the surveyed data obtained from the Seoul Metro Company. By analyzing the line-specific proportions of smart card use rather than the average smart card use, the accuracy of the results is improved. From the results, it can be seen that smart card data show potential as a basis for describing the characteristics of public transit users, such as the number of transfers, boarding time, hourly trip distribution of the number of trips for different transit modes, and travel time distribution for all transit modes and user types.

Given that reliable prediction of such relatively rare-and random-events as accident occurrence will remain elusive, the most important potentially soluble factor in the development of accident management strategies is to identify and quantify the conditions affecting the nonrecurrent congestion caused by accidents once they have been known to have occurred. The objective of the research reported in this paper is to develop a method of quantifying the delay due to accidents on urban freeways, as well as to identify the causal factors affecting the total delay caused by such accidents. Binary integer programming (BIP) is applied in estimating the temporal and spatial extent of delay caused by freeway accidents, based solely on commonly available inductance loop detector data. The basic idea behind the method is to estimate the most likely temporal and spatial extent of the region of congestion caused by an accident by solving a BIP problem that is consistent with the topology of the spatio-temporal region that defines candidate speed differences between normal flow conditions and accident conditions. The procedures developed in this paper will be useful for the performance evaluation of accident management systems by quantifying accident congestion in terms of the total delay to evaluate the benefit of accident management systems accrued from efficient traffic operations. The procedures are demonstrated by a case study using accident data collected from six major freeways in Orange County, CA.

The present study reviews bridge inspection practices and bridge management programs in China, Japan, Korea, and U.S. Bridges play an essential role in transportation systems and in the economic production process. Transportation agencies should maintain bridges in acceptable conditions to provide a desirable level of service to the public within limited budgets. In addition, a number of bridges are aging rapidly in these four countries. Fortunately, since several countries have experienced deteriorated bridges due to aging, the countries considered in this study have already developed comprehensive bridge management system (BMS) and bridge inspection practices. Therefore, the goal of this paper is to search for and synthesize useful knowledge on BMSs and bridge inspection practices of the four countries. Finally, recommendations that will serve as guidance to transportation agencies for potential enhancements to BMS and bridge inspections are presented.

Unlike conventional traffic safety studies that focused on histrionic data analyses, this study attempts to identify traffic conditions that might lead to a traffic accident from real-time freeway traffic data. An innovative feature of the study is to apply the concept, real-time and preaccident, to accident studies by integrating real-time capabilities in advanced traffic management and information systems (ATMIS). In this study, the traffic conditions leading to more accidents are defined as real-time accident likelihood, and the accident likelihood is estimated by employing a nonparametric Bayesian model. The main goal of the study is to remove hazardous traffic condition prior to accident occurrence by incorporating the real-time accident likelihood into ATMIS. This study estimates real-time accident likelihood from empirical data on I-880 freeway in California, and shows its applicability as an accident precursor.

Abstract A two‐way interaction between transport and land‐use has been one of the central research topics of transport studies. Thus, it is not surprising that there have been numerous approaches taken to investigate this reciprocal relationship. This paper critically examines the existing literature to see how effectively the current models represent this mutual interaction. This examination process intends to form a prototype reference that compares and contrasts the advantages and disadvantages of the various models. This undertaking is dealt with based on theoretical, methodological and operational characteristics of a respective model. Some conclusions and suggestions are also drawn. Acknowledgement The author is grateful to two anonymous referees for invaluable comments and suggestions. Many thanks are also given to Professor Roger L. Mackett, Centre for Transport Studies, University College London, for advice and discussion. Funding was provided by the British Council and Universities UK.

Using data from the Current Population Survey for 1997 and 2001, we estimate the probability that a consumer engages in online shopping. We use variation in sales-tax rates by county to identify the effects of the sales-tax rate in the home county and adjacent counties. We also control for the sales-tax base. The estimates are consistent with the interpretation that consumers use Internet shopping to avoid sales taxes. In addition, consumers who live adjacent to counties with lower sales-tax rates are less likely to shop via the Internet, all else equal. We interpret this as evidence of cross-border shopping.

A concrete slab was constructed at Texas A&M University to investigate environmental effects in terms of temperature and moisture effects on the behavior of jointed plain concrete pavements. The slab behavior was delineated relative to equivalent linear temperature and humidity differences as a function of time. As expected, the trends of these differences were found to relate with the trends of ambient temperature and relative humidity. The slab had daily periods of tensile and compressive strain corresponding to the daily changes in the ambient temperature and relative humidity. Furthermore, the trends in the slab displacements were clearly dependent upon the changes in ambient temperature and slab temperature gradients. Drying shrinkage and creep strains primarily in the vicinity of the top surface appeared to cause an overall shift in the slab movements. This shift manifest in both the strain and vertical displacement of the slab was determined from data collected up to 2years after placement of the concrete.

Abstract Braced frames are one of the most economical and efficient seismic resisting systems yet few full‐scale tests exist. A recent research project, funded by the National Science Foundation (NSF), seeks to fill this gap by developing high‐resolution data of improved seismic resisting braced frame systems. As part of this study, three full‐scale, two‐story concentrically braced frames in the multi‐story X‐braced configuration were tested. The experiments examined all levels of system performance, up to and including fracture of multiple braces in the frame. Although the past research suggests very limited ductility of SCBFs with HSS rectangular tubes for braces recent one‐story tests with improved gusset plate designs suggest otherwise. The frame designs used AISC SCBF standards and two of these frames designs also employed new concepts developed for gusset plate connection design. Two specimens employed HSS rectangular tubes for bracing, and the third specimen had wide flange braces. Two specimens had rectangular gusset plates and the third had tapered gusset plates. The HSS tubes achieved multiple cycles at maximum story drift ratios greater than 2% before brace fracture with the improved connection design methods. Frames with wide flange braces achieved multiple cycles at maximum story drift greater than 2.5% before brace fracture. Inelastic deformation was distributed between the two stories with the multi‐story X‐brace configuration and top story loading. Copyright © 2010 John Wiley & Sons, Ltd.

PURPOSE: The goals of this study were to investigate the clinical activity, safety, and biomarkers of dacomitinib, an irreversible tyrosine kinase inhibitor of EGFR, HER2, and HER4, in recurrent and/or metastatic squamous cell carcinoma of the head and neck (R/M-SCCHN). EXPERIMENTAL DESIGN: Patients were eligible if the diseases were not amenable to curative treatment and had progressed on platinum-based chemotherapy, and were treated with dacomitinib 45 mg/day. The primary endpoint was objective response rate by RECISTv1.1. Exploratory analysis included the characterization of somatic mutation, gene copy number, gene expression, p16(INK4A) expression by IHC, and investigation of their relationship with clinical outcomes. RESULTS: Forty-eight patients were evaluable for efficacy and toxicity. Ten patients (20.8%) had partial responses and 31 patients (65%) had stable diseases. The median progression-free survival (PFS) and overall survival (OS) were 3.9 months [95% confidence interval (CI), 2.9-5.0] and 6.6 months (95% CI, 5.4-10.3). Adverse events were mostly grade 1-2. Mutations in the PI3K pathway (PIK3CA, PTEN) and high expression of inflammatory cytokines (IL6, IL8, IL1A, IL1B, IL4, and TNF) were significantly associated with shorter PFS (2.9 vs. 4.9 months without mutations, P = 0.013; 2.8 vs. 9.9 months with low expression, P = 0.004). Those harboring PI3K pathway mutations or high inflammatory cytokine expression had shorter median OS (6.1 vs. 12.5 months lacking PI3K pathway mutations and with low inflammatory cytokine expression, P = 0.005). CONCLUSIONS: Dacomitinib demonstrated clinical efficacy with manageable toxicity in platinum-failed R/M-SCCHN patients. Screening of PI3K pathway mutation and inflammatory cytokine expression may help identify which R/M-SCCHN patients are likely to gain benefit from dacomitinib.