• Spatial Information Research
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• v.22 no.6
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• pp.81-90
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• 2014

This study analyzed an impact relationship between the variable of street based on the walking enhancement factors and the street revitalization. In detail, walking enhancement factors include a physical environment, accessibility and a density which was derived from previous studies. In addition, This study also analyzed the diversity of the profession which was emphasized by Jacobs(1961) and the influence of the specific space on street revitalization that was emphasized by Ray Oldenburg (1989) and Richard MacCormac (1983). The anlaysis by types showed the walking enhancement factors, including street envirnoment, accessibility, density and diversity, which were emphasized by Jacobs(1961) possessed similararites between weekdays and weekends. On contrast, the spatial distribution of stores showed a major difference of influences on street revitalization between weekdays and weekends as Ray Oldenburg (1989) and Richard MacComac (1983) has insisted.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.1-15
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• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.321-331
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• 2018

This study examined the life safety factor of residents living in a university village. The results of the study were as follows. First, previous research results and social safety-related research and practical system for a wide range of exterior space, including the driveway of the living space was a relative lack. Based on the survey results, the psychological factors that affect the life safety environment of the living space was found in the exterior space environment. Second, the living safety factors in the exterior space of a one-room residence can be shown by four types, such as occupant monitoring, residential surveillance and area classification, external public space utilization, and pleasant environment maintenance in the architectural planning dimension. Third, the results of research on the exterior space of the university one-room village, and life safety environment of exterior space, such as design of pedestrian street, revealed a very poor resident population and one-room buildings in most areas.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.69-83
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• 2020

The purpose of this study is to classify the types of street parking in low-rise residential areas. First of all, prior research was reviewed to examine the background of occurance and the process of change of low-rise residential areas. and derive factors that affect the street environment. Next, based on the selection criteria, the residential area of Bangi 2-dong, Songpa-gu was selected as the site of the case and the status analysis was conducted on streets, buildings, and on-road parking. The summary of the results is on-road parking usually occurs on residential streets where social consensus is difficult to reach, suggesting that alternatives to on-road parking in residential areas are needed. Based on the results of the previous analysis, street types were classified considering the characteristics of streets, structures and street parking. Then, the characteristics of each type of street were analyzed and implication for improving the street environment were suggested. In addition, the results of the classification of street types confirmed that different street parking occurred depending on the width of the street and the use of the lower floors, even if it was the same area, and that a solution was needed accordingly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.711-718
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• 2013

Traffic pavement markings are one of primary transportation facilities that provide drivers with various road information directly. Thus, a clear understanding of traffic pavement markings is utmost important to improve traffic safety as well as to establish a proper traffic culture. However, no past studies examined drivers' understanding of traffic pavement markings in Korea. Hence, this study investigated drivers' understanding of traffic pavement markings through an elaborated administrated survey, and analyzed the relationship between various drivers' characteristics and understanding regarding pavement marking via cross-classification table and logistic model. The analysis results show that drivers have limited understanding regarding the purpose of the markings. Specifically, the average understanding of pavement markings is 57.41%: the lowest understanding is 25.88% for yield pavement marking, and the highest understanding is 91.18% for advanced pedestrian crosswalk pavement marking. This study also revealed that the understanding of some pavement markings are somewhat influenced by user group such as drivers with suspended or revoked driver licenses, but the overall understanding of pavement markings are not significantly affected by drivers' characteristics such as gender and driving experiences at ${\alpha}$=0.05. Thus, it might be desirable for policy makers to establish pavement marking-related policies for overall drivers rather than specific drivers.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.173-181
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• 2017

In this paper, we have studied tracking as a training stage of considering the position and the scale of a person given its previous position, scale, as well as next and forward image fraction. Unlike other learning methods, CNN is thereby learning combines both time and spatial features from the image for the two consecutive frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN. This fact confirms the importance of automatically optimized features. However, the computation time for the classification of a person using the convolutional neural network classifier is less than approximately 1/40 of the SVM computation time, regardless of the type of the used features.

• Land and Housing Review
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• v.4 no.2
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• pp.167-176
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• 2013

The City Complex Development and TOD originated from the Compact City are entrenched domestically. The New Urban Development such as these changes Structure of Urban space from the Automobile to the Public Transportation. Also, Transit-Oriented Development is drawing attention as sustainability because it has effects of Environment as Green growth. However, An empirical Study is insufficient about Influence Factors of Transit Oriented Development. Therefore This study sets up the Density spaced 1000m apart of Transit Oriented than the existing and did 7 Types considered of Accessibility, Complexity and Design Element according to the Factorial analysis. As a result, this study drew that (1) 'intraregional accessibility of public transit', (2) 'degree of development', 'pedestrian-friendly facility', (3) 'interzonal accessibility of public transit', (4) 'land-use of the city centre', 'complex using of rail station area', 'complementary public transit' are related 201 in number of the Subway stations according to the Multi-dimensional scaling.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.