• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7329-7334
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• 2015

This is to analyze of driver behavioral and the accident characteristics in rollover. The study period was January 2011 to May 2014 and the subject of study was 102 person who were drivers visited the emergency room. Research tool includes a damage information of the vehicle, accident mechanism, damage to the patient clinical information with the injury data from the ROAD Traffic Authority. For data analysis, SPSS 18.0 was used for t-test, ANOVA and Chi-square test. Injury Severity Score average score according to the vehicle type is 6.00 points in the smaller vehicle, at high vehicle 11.78 points, from the other vehicle that showed 14.70 points. Significant differences between the three groups did not show (P=.267). Men did not use a seat belt significantly compared to women(P=.007). Vehicle type and weather, this was no correlation with whether or not use the seat belt(P=.755, P=.793). But showed a tendency to smaller size vehicles drivers do not use a seat belt, the weather could see a little more inclined to use a seat belt rather than a sunny day. Finally, in rollover accidents as in other types of accident it was confirmed that the seat belt has a great influence on the damage.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3672-3679
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• 2012

Vehicle Ad hoc Network (VANET) is next-generation network technology to provide various services using V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure). In VANET, many researchers proposed various studies for the safety of drivers. In particular, using the emergency message to increase the efficiency of traffic safety have been actively studied. In order to efficiently transmit to moving vehicle, to send a quick message to as many nodes is very important via broadcasting belong to communication range of vehicle nodes. However, existing studies have suggested a message for transmission to the communication node through indiscriminate broadcasting and broadcast storm problems, thereby decreasing the overall performance has caused the problem. In addition, theses problems has decreasing performance of overall network in various form of road and high density of vehicle node as urban area. Therefore, this paper proposed Clustering based Routing Algorithm (CBRA) to efficiently transmit emergency message in high density of vehicle as urban area. The CBRA managed moving vehicle via clustering when vehicle transmit emergency messages. In addition, we resolve linkage problem between vehicles according to various form of road. The CBRA resolve link brokage problem according to various form of road as urban using clustering. In addition, we resolve broadcasting storm problem and improving efficacy using selection flooding method. simulation results using ns-2 revealed that the proposed CBRA performs much better than the existing routing protocols.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.64-78
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• 2019

This paper explores key factors affecting taxi travel using global positioning system(GPS) data in Seoul, Korea, considering spatial dependence. We first analyzed the travel characteristics of taxis such as average travel time, average travel distance, and spatial distribution of taxi trips according to the time of the day and the day of the week. As a result, it is found that the most taxi trips were generated during the morning peak time (8 a.m. to 9 a.m.) and after the midnight (until 1 a.m.) on weekdays. The average travel distance and travel time for taxi trips were 5.9 km and 13 minutes, respectively. This implies that taxis are mainly used for short-distance travel and as an alternative to public transit after midnight in a large city. In addition, we identified that taxi trips were spatially correlated at the traffic analysis zone(TAZ) level through the Moran's I test. Thus, spatial regression models (spatial-lagged and spatial-error models) for taxi trips were developed, accounting for socio-demographics (such as the number of households, the number of elderly people, female ratio to the total population, and the number of vehicles), transportation services (such as the number of subway stations and bus stops), and land-use characteristics (such as population density, employment density, and residential areas) as explanatory variables. The model results indicate that these variables are significantly associated with taxi trips.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.47-56
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• 2022

The safe driving reward system aims to reduce the loss of life and property by reducing the occurrence of accidents by motivating safe driving and encouraging active participation by providing direct reward to vehicle drivers who have performed safe driving. In the case of the existing digital tachograph, the goal is to limit dangerous driving by recording the driving status of the vehicle whereas the safe driving reward system is a support measure to increase the effect of accident prevention and induces safe driving with financial reward when safe driving is performed. In other words, in an area where accidents due to speeding are high, direct reward is provided to motivate safe driving to prevent traffic accidents when safe driving instructions such as speed compliance, maintaining distance between vehicles, and driving in designated lanes are performed. Since these safe operation data and reward histories must be managed transparently and safely, the reward evidences and histories were constructed using the closed blockchain Hyperledger Fabric. However, while transparency and safety are guaranteed in the blockchain system, low data processing speed is a problem. In this study, the sequential block generation speed was as low as 10 TPS(transaction per second), and as a result of applying the acceleration function a high-performance network of 1,000 TPS or more was implemented.

• Land and Housing Review
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• v.13 no.3
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• pp.125-140
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• 2022

Road Deposited Sediment (RDS) is the solids formed from the wear of road, wear of vehicles, exhausts, and the input of the emissions from various sources out of the roads. RDS is seriously polluted by organic matter, nutrients, and metals. RDS plays an important role as the sink and the transport medium of the associated pollutants because RDS can be carried to the adjacent water system via stormwater runoff. In this regard, the heavy metals in RDS were investigated based on the publications. The contents of the metals in RDS were highly variable. The concentration of Cr, Ni, Cu, Fe, Zn, As, Cd, and Pb in urban RDS in various regions was in a range of 3.16-3,410, 1.15-1,382, 20.2-9,069, 2,980-124,853, 81-2,550, 2.3-214, 0.19-21.3, and 15.21-1,125 mg/kg, respectively. The anthropogenic enrichment of the metals in RDS was confirmed by the high concentration of Cu, Zn, Cd, and Pb. The contents of the metals were higher in industrial and traffic areas than in residential areas, while they were generally increased with decreasing particle size. It is believed that this study's results would contribute to quantifying the metals' load via RDS and establishing control strategies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.44-53
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• 2023

Recently, serious parking problems are occurring due to the difficulty of securing sufficient parking space, and it may lead to other traffic or social problems. In order to solve the parking problem in areas and districts beyond a certain range, a study on-roads and off-street parking lots reflecting regional characteristics is necessary. Therefore, this study establishing a parking demand calculation model for use as a basic study in establishing on-road and off-road characteristics. In order to conduct the study, Dong-fu, Daegu Metropolitan City was divided into dongs, and parking facilities and parking demand were investigated. The survey time was divided into daytime and nighttime on weekdays, and the types of vehicles were divided into three types: passenger car, small trucks and buses, large trucks and buses. As explanatory variables for calculating parking demand, the total floor area of buildings for each of six purposes was used, including detached houses, apartment houses, neighborhood living facilities, cultural and assembly facilities, business facilities, and sales facilities. As a result of the correlation analysis, among the six explanatory variables, the total area of neighborhood living facilities showed a significant correlation with on- and off-street parking demand. A regression analysis model was constructed using the total area of neighborhood living facilities as an explanatory variable, and statistically significant results were obtained.