• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.12-22
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• 2016

Walking behaviors are one of the most basic transport modes in daily life. As a result, the efforts and concerns on pedestrians are consistently increased. This study tried to reveal significant factors for pedestrian volumes through structural equation models and compare the impacts of the whole time of day, off-peak time, peak time on the pedestrian volumes. The results of the analysis show that commercial business factors, accessibility factors, walking environment factors are the most significant factors that increase pedestrian volumes. Whereas, housing factors do not contribute to increase the pedestrian volumes. In the non-peak time, the weight of commercial business factors is higher than the whole time of day, while the weight housing factors, accessibility factors, walking environment factors are lower. In the peak time, however, the weight of commercial business factors decreases rather than the whole time of day, while the weight of the other factors increase.

• Journal of KIISE
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• v.44 no.1
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• pp.44-50
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• 2017

Pedestrian detection, an important component of autonomous driving and driving assistant system, has been extensively studied for many years. In particular, image based pedestrian detection methods such as Hierarchical classifier or HOG and, deep models such as ConvNet are well studied. The evaluation score has increased by the various methods. However, pedestrian detection requires high sensitivity to errors, since small error can lead to life or death problems. Consequently, further reduction in pedestrian detection error rate of autonomous systems is required. We proposed a new method to detect pedestrians and reduce the error rate by using the Faster R-CNN with new developed pedestrian training data sets. Finally, we compared the proposed method with the previous models, in order to show the improvement of our method.

• Journal of Korean Society of Transportation
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• v.30 no.4
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• pp.7-19
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• 2012

In this study of estimating the benefits of environmental materials, the CVM method used in environmental economics was applied to estimate the value of pedestrian environment improvement. After finding the Willingness To Pay (WTP) level of residents through CVM, this study attempts to calculate quantitative benefits from the pedestrian environment improvement. In this study, a survey targeting the inhabitants in Seongbuk-gu adjacent to a business area was carried out for pedestrian environment improvement considering form of payment, willingness to pay and such by establishing and showing several virtual scenarios depicting a quiet and comfortable pedestrian environment. As a result, the willingness to pay level of the Seongbuk-gu residents was 627 won of surcharge for pedestrian environment improvement per month. Additionally, the annual total benefits by pedestrian environment improvement was estimated within a range from 1,247,516,820 won to 286,305,110 won.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.24-31
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• 2011

This study deals with the operational effectiveness of roundabout. The roundabout is currently under consideration in our country depending on the result of existing researches, that the roundabout decreases delay and is environmentally friendly compared to the signalized intersection. The purpose of the study is to analyze the operational effectiveness of the roundabout by the change of pedestrian traffic volume. In pursing the above, this study gave particular emphasis to designing a network of roundabout, developing some scenarios for analysis including both entering traffic volume and pedestrians volume, and comparatively analyzing the average controlled delay time per vehicle. In this study, VISSIM model was used as a tool for traffic simulation. The main results are as follows. First, as a result of analyzing a traffic delay based on the pedestrian traffic volume, pedestrian traffic volume was analyzed to have a great impact on the roundabout operation. Second, the more pedestrian traffic volume were evaluated to indicate the more traffic delay. When the entering volumes with 1,000persons/hour (pedestrian volume) were more than 800pcph in the single-lane and 1,600pcph in the double-lane roundabout, the operational efficiencies of signalized intersections were evaluated to be better than those of roundabouts.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.7-15
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• 2010

The walkway level of service (LOS) is conceptualized using pedestrian flow rate, speed, and density according to the Korean Highway Capacity Manual (KHCM). However, as it is based on the data of commuters at peak hours, it needs to be reevaluated considering various trip purposes and a pleasant pedestrian environment. Thus, the authors aimed to investigate and analyze the characteristics of each group: pedestrians during the peak hour and those during the non-peak hour. Then they were verified statistically for the walkway evaluation criteria. In this study, the authors investigated pedestrian speed and flow in a complex transit center walkway with diverse trip purposes by peak and non-peak hour. Then the authors statistically verified the differences between the groups. A model was built for estimating pedestrian density by speed and flow; it was used to calculate the walkway capacity (67.3p/m/m) corresponding to LOS E. The authors established new criteria for LOS, applying the LOS from the HCM. These standards can then be used as the design standards for pedestrian walkways.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.840-860
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• 2023

Aiming at the serious occlusion and slow tracking speed in pedestrian target tracking and recognition in complex scenes, a target tracking method based on improved YOLO v5 combined with Deep SORT is proposed. By merging the attention mechanism ECA-Net with the Neck part of the YOLO v5 network, using the CIoU loss function and the method of CIoU non-maximum value suppression, connecting the Deep SORT model using Shuffle Net V2 as the appearance feature extraction network to achieve lightweight and fast speed tracking and the purpose of improving tracking under occlusion. A large number of experiments show that the improved YOLO v5 increases the average precision by 1.3% compared with other algorithms. The improved tracking model, MOTA reaches 54.3% on the MOT17 pedestrian tracking data, and the tracking accuracy is 3.7% higher than the related algorithms and The model presented in this paper improves the FPS by nearly 5 on the fps indicator.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.76-84
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• 2010

Many pedestrian simulation models for micro-scale spaces as building indoor areas have been proposed for the last decade and two models - social force model and floor field model - are getting attention. Among these, CA-based floor field model is viewed more favourable for computer simulations than computationally complex social force model. However, Kirchner's floor field model has limitations in capturing the differences in dynamic values of different agents and this study proposes an enhanced algorithm. This study improved the floor field model in order for an agent to be able to exclude the influences of its own dynamic values by changing the data structure, and, also modified the initial dynamic value problem in order to fit more realistic environment. In the simulations, real 3D building data stored in a spatial DBMS were used considering future integration with indoor localization sensors and real time applications.

• Spatial Information Research
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• v.18 no.5
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• pp.133-142
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• 2010

Many pedestrian or fire evacuation models have been studied last decades for modeling evacuation behaviors or analysing building structures under emergency situations. However, currently developed models do not consider the differences of visibility of pedestrians by obstacles such as furniture, wall, etc. The visibility of pedestrians is considered one of the important factors that affect the evacuation behavior, leading to making simulation results more realistic. In order to incorporate pedestrian's visibility into evacuation simulation, we should be able to give different walking speeds according to differences of visibility. We improved the existing floor field model based on cellular automata in order to implement the visibility. Using the space syntax theory, we showed how we split the indoor spaces depending on the different visibilities created by different levels of structural depths. Then, we improved the algorithm such that pedestrians have different speeds instead of simultaneous movement to other cells. Also, in order for developing a real time simulation system integrated w ith indoor sensors later, we present a process to build a 3D simulator using a spatial DBMS. The proposed algorithm is tested using a campus building.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.343-351
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• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.