• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.87-93
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• 2004

A five-day work week has a great impact on the life styles of employed persons and their families. At the same time, the changes also impact on the transportation system because travel patterns, demand, and pattern of congestion change during weekends. The negative impacts on the transportation system should be examined in order to conceive measures to maintain dependable levels of service during weekends. The first step to pursue the issue is to identify the road segments heavily affected by augmented leisure trips. In this study, characteristics of highway segments are engineered by principal analysis using data from TCS database. Scores from principal analysis are employed to distinguish highway segments for leisure trips from total 197 segments considered in this study. In addition, indexes from principal analysis are proposed to identify highway segments for leisure trips.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.101-111
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• 2012

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.138-147
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• 2010

As inaccurate traffic volume prediction may result in inadequate transportation planning and design, traffic volume prediction based on traffic volume data is very important in spatial decision making processes such as transportation planning and operation. In order to improve the accuracy of traffic volume prediction, recent studies are using the geostatistical approach called kriging and according to their reports, the method shows high predictability compared to conventional methods. Thus, this study estimated traffic volume data for St. Louis in the State of Missouri, USA using the kriging method, and tested its accuracy by comparing the estimates with actual measurements. In addition, we suggested a new method for enhancing the accuracy of prediction by the kriging method. In the new method, we estimated traffic volume data: first, by applying anisotropy, which is a characteristic of traffic volume data appearing in determining variogram factors; and second, by performing co-kriging analysis using interstate highway, which is in a high spatial correlation with traffic volume data, as a secondary variable. According to the results of the analysis, the analysis applying anisotropy showed higher accuracy than the kriging method, and co-kriging performed on the application of anisotropy produced the most accurate estimates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.549-556
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• 2012

Longitudinal tunnels are defined as tunnels with length of over 1km. Because of Korea's topographical conditions and as safety measures for linear design, many tunnels are inevitably being constructed in Korea. The number of longitudinal tunnels constructed on expressways amounted to 104 as of the end of 2010 with a total length of 192km. Given the increasing demand for tunnels and the increasing length of tunnels, a safety evaluation of longitudinal tunnels needs to be conducted. As such, this study selected design elements, transportation environment and delineation system as elements to check and tried to determine factors influencing road crashes. For this, tunnels have been classified based on history of crashes; ones with crashes and ones without crashes and statistically meaningful explanatory variables were selected. By using these variables, a logit model was development in order to better grasp the factors that directly and strongly influence crashes. The result, related to crashes as well as the analysis were utility tunnel interior materials of driving lane and passing lane, which are related to driver's visibility, lateral width widening to consolidate space in a tunnel, and annual average daily traffic (AADT) per lane. These results may be used in the future as analysis indicators when drawing up plans to prevent crashes in longitudinal tunnels.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.160-168
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• 2017

In the past 10 years, the accidents caused by drowsy driving have occupied about 23% of all traffic accidents in Korea expressway network and this rate is the highest one among all accident causes. Unlike other types of accidents caused by speeding and distraction to the road, the accidents by drowsy driving should be managed differently because the drowsiness might not be controlled by human's will. To reduce the number of accidents caused by drowsy driving, researchers previously focused on the spot based analysis. However, what we actually need is a segment (link) and occurring time based analysis, rather than spot based analysis. Hence, this research performs initial effort by adapting link concept in terms of drowsy driving on highway. First of all, we analyze the accidents caused by drowsy in historical accident data along with their road environments. Then, links associate with driving time are analyzed using digital tachograph (DTG) data. To carry this out, negative binomial regression models, which are broadly used in the field, including highway safety manual, are used to define the relationship between the number of traffic accidents on expressway and drivers' behavior derived from DTG. From the results, empirical Bayes (EB) and potential for safety improvement (PSI) analysis are performed for potential risk segments of accident caused by drowsy driving on the future. As the result of traffic accidents caused by drowsy driving, the number of the traffic accidents increases with increase in annual average daily traffic (AADT), the proportion of trucks, the amount of DTG data, the average proportion of speeding over 20km/h, the average proportion of deceleration, and the average proportion of sudden lane-changing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.26-37
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• 2017

The study is to develop safety performance functions(SPFs) for urban intersections using random effects Tobit regression model and to analyze correlations between crashes and factors. Also fixed effects Tobit regression model was estimated to compare and analyze model validation with random effects model. As a result, AADT, speed limits, number of lanes, land usage, exclusive right turn lanes and front traffic signal were found to be significant. For comparing statistical significance between random and fixed effects model, random effects Tobit regression model of total crash rate could be better statistical significance with $R^2_p$ : 0.418, log-likelihood at convergence: -3210.103, ${\rho}^2$: 0.056, MAD: 19.533, MAPE: 75.725, RMSE: 26.886 comparing with $R^2_p$ : 0.298, log-likelihood at convergence: -3276.138, ${\rho}^2$: 0.037, MAD: 20.725, MAPE: 82.473, RMSE: 27.267 for the fixed model. Also random effects Tobit regression model of injury crash rate has similar results of model statistical significant with random effects Tobit regression model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.52-62
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• 2013

Design Hourly Factor(DHF) is defined as the ratio of design hourly volume(DHV) to Average Annual Daily Traffic(AADT). Generally DHV used the 30th rank hourly volume. But this case DHV is affected by holiday volumes so the road is at risk for overdesigning. Computing K factor is available for counting 8,760 hour traffic volume, but it is impossible except permanent traffic counts. This study applied three method to make DHF, using 30th rank hourly volume to make DHF(method 1), using peak hour volume to make DHF(method 2). Another way to make DHF, rank hourly volumes ordered descending connect a curve smoothly to find the point which changes drastic(method 3). That point is design hour, thus design hourly factor is able to be computed. In addition road classified 3 type for national highway using factor analysis and cluster analysis, so we can analyze the characteristic of DHF by road type. DHF which was used method 1 is the largest at any other method. There is no difference in DHF by road type at method 2. This result shows for this reason because peak hour is hard to describe the characteristic of hourly volume change. DHF which was used method 3 is similar to HCM except recreation road but 118th rank hourly volume is appropriate.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.17-27
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• 2011

This paper investigates some critical errors influencing travel demand estimation in Korea Transportation Data Base (KTDB), and through this investigation reasonable traffic analysis zone (TAZ) size and internal trips ratio are analyzed. With varying zone size, the accuracy of travel demand estimation is studied and appropriate level of zone size in KTDB is also presented. For this purpose zonal structure consisting of location of zone centroid, number of centroid connecters has been constructed by social economic index, and then some descriptive statistical analyses such as F-test, coefficient of correlation are performed. From the results, this paper shows that the optimum levels of zone system were various according to the order and capacity of roads, and also shows that the smaller TAZ, the less error in this research. In conclusion, in order to improve accuracy of traffic demand estimation it is necessary to make zone size smaller.

• 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.