• International Journal of Highway Engineering
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• v.17 no.6
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• pp.65-73
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• 2015

PURPOSES : This study tries to develop the accident models of 4-legged signalized intersections in Busan Metropolitan city with random parameter in count model to understanding the factors mainly influencing on accident frequencies. METHODS : To develop the traffic accidents modeling, this study uses RP(random parameter) negative binomial model which enables to take account of heterogeneity in data. By using RP model, each intersection's specific geometry characteristics were considered. RESULTS : By comparing the both FP(fixed parameter) and RP modeling, it was confirmed the RP model has a little higher explanation power than the FP model. Out of 17 statistically significant variables, 4 variables including traffic volumes on minor roads, pedestrian crossing on major roads, and distance of pedestrian crossing on major/minor roads are derived as having random parameters. In addition, the marginal effect and elasticity of variables are analyzed to understand the variables'impact on the likelihood of accident occurrences. CONCLUSIONS : This study shows that the uses of RP is better fitted to the accident data since each observations'specific characteristics could be considered. Thus, the methods which could consider the heterogeneity of data is recommended to analyze the relationship between accidents and affecting factors(for example, traffic safety facilities or geometrics in signalized 4-legged intersections).

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.23-29
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• 2012

Existing pedestrian and evacuation models generally seek to find locally optimal solutions for the shortest or the least time paths to exits from individual locations considering pedestrian's characteristics (eg. speed, direction, sex, age, weight and size). These models are not designed to produce globally optimal solutions that reduce the total evacuation time of the entire pedestrians in a building when all of them evacuate at the same time. In this study, we suggest a globally optimal model for indoor pedestrian evacuation to minimize the total evacuation time of occupants in a building considering different distributions of them. We used the genetic algorithm, one of meta-heuristic techniques because minimizing the total evacuation time can not be easily solved by polynomial expressions. We found near-optimal evacuation path and time by expressing varying pedestrians distributions using chromosomes and repeatedly filtering solutions. In order to express and experiment our suggested algorithm, we used CA(cellular automata)-based simulator and applied to different indoor distributions and presented the results.

• Spatial Information Research
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• v.18 no.4
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• pp.89-98
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• 2010

Recently, the target of navigation system is moving from the cars to pedestrians. Many researches are in progress regarding pedestrian navigation, However, in most cases, the path-finding is based on the existing node/link network model. which is widely used for the car navigation, and thus showing its limitation. The reasons arc that a) unlike with a car, the paths that pedestrians take arc not limited to the roads, b) pedestrians an~ not restricted in rotation or direction, and c) they can freely move within the walkable space. No alternatives have been offered yet, especially for openspaces such as a park or square. Therefore, in this research, we suggested appropriate methods to create paths that can be used in pedestrian navigation service, by using motion-planning technology, which is used in the field of robotics for planning the motion of an object, and conducted tests for their applicability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1060-1069
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• 2019

Pedestrian-based camera self-calibration methods are suitable for video surveillance systems since they do not require complex calibration devices or procedures. However, using arbitrary pedestrians as calibration targets may result in poor calibration accuracy due to the unknown height of each pedestrian. To solve this problem in the real surveillance environments, this paper proposes a novel Bayesian approach. By assuming known statistics on the height of pedestrians, we construct a probabilistic model that takes into account uncertainties in both the foot/head locations and the pedestrian heights, using foot-head homology. Since solving the model directly is infeasible, we use variational Bayesian inference, an approximate inference algorithm. Accordingly, this makes it possible to estimate the height of pedestrians and to obtain accurate camera parameters simultaneously. Experimental results show that the proposed algorithm is robust to noise and provides accurate confidence in the calibration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.58-70
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• 2019

In the case of a pedestrian traffic accident, it has a large-scale danger directly connected by a fatal accident at the time of the accident. The domestic ITS is not used for intelligent risk classification because it is used only for collecting traffic information despite of the construction of good quality traffic infrastructure. The CNN based pedestrian detection classification model, which is a major component of the proposed system, is implemented on an embedded system assuming that it is installed and operated in a restricted environment. A new model was created by improving YOLO's artificial neural network, and the real-time detection speed result of average accuracy 86.29% and 21.1 fps was shown with 20,000 iterative learning. And we constructed a protocol interworking scenario and implementation of a system that can connect with the ITS. If a pedestrian accident prevention system connected with ITS will be implemented through this study, it will help to reduce the cost of constructing a new infrastructure and reduce the incidence of traffic accidents for pedestrians, and we can also reduce the cost for system monitoring.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.14-24
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• 2018

Pedestrian movement of passengers using smart card within stations can be divided into three types of activities - straight ride and alight, line transfer, and station transfer. Straight ride and alight is transfer activity for which the card terminal and embarking line are identical. In this case, straight ride occurs at the origin station and straight alight occurs at the destination station. Line transfer refers to activity in which the subway line embarked on by the passenger is different from that which is disembarked. Succinctly, line transfer is transfer at a middle station, rather than at origin or destination stations. Station transfer occurs when the card terminal line and embarking line are different. It appears when station transfer happens at the origin station as starting transfer, and at the destination station as destination transfer. In the case of Metropolitan smart card data, origin and destination station card terminal line number data is recorded, but subway line data does not exist. Consequently, transportation card data, as it exists, cannot adequately be used to analyze pedestrian movement as a whole in subway stations. This research uses the smart card data, with its constraints, to propose an analysis model for passenger pedestrian movement within subway stations. To achieve this, a path selection model is constructed, which links origin and destination stations, and then applied for analysis. Finally, a case study of the metropolitan subway is undertaken and pedestrian volume analyzed.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.71-76
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• 2018

The purpose of this study is to analyze the factors affecting the roundabout accidents near schools. This study gives particular attentions discussing characteristics by pedestrian accident severity using the ordered logit models. In pursuing the above, 63 roundabouts installed before 2014 are surveyed for modeling. the traffic accident data from 2014 to 2016 are collected from TAAS data set of Road Traffic Authority. Such 35variables explaining the accidents as environment, human, geometries, school and roundabout factor are selected from literature reviews. The main results are as follows. First, the ordered logit models (${\rho}^2$ of 0.272, $x^2$ of 24.723) which is statistically significant have been developed. Second, environment factor variable is analyzed to be day or night ($X_1$ ), human factor variables are evaluated to be driver gender($X_4$), older driver($X_5$), pedestrian gender($X_7$) and children pedestrian($X_8$ ). Third, geometries factor variable are analyzed to be speed limit sign($X_{16}$) and median barrier($X_{21}$), school factor variables are evaluated to be hump-type crosswalk($X_{25}$), CCTV($X_{26}$) and school zone sign($X_{27}$), roundabout factor are analyzed to be roundabout sign($X_{30}$) and number of circulatory roadway lane($X_{32}$). Finally, this study could give some implications to decreasing the accidents severity at roundabout near schools.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.569-575
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• 2013

This paper proposes a method for PDR (Pedestrian Dead-Reckoning) using a low cost IMU. Generally, GPS has been widely used for localization of pedestrians. However, GPS is disabled in the indoor environment such as in buildings. To solve this problem, this research suggests the PDR scheme with an IMU attached to the pedestrian's waist. However, despite the fact many methods have been proposed to estimate the pedestrian's position, but their results are not sufficient. One of the most important factors to improve performance is, a new calibration method that has been proposed to obtain the reliable sensor data. In addition to this calibration, the PDR method is also proposed to detect steps, where estimation schemes of step length, attitude, and heading angles are developed. Peak and zero crossings are detected to count the steps from 3-axis acceleration values. For the estimation of step length, a nonlinear step model is adopted to take advantage of using one parameter. Complementary filter and zero angular velocity are utilized to estimate the attitude of the IMU module and to minimize the heading angle drift. To verify the effectiveness of this scheme, a real-time system is implemented and demonstrated. Experimental results show an accuracy of below 1% and below 3% in distance and position errors, respectively, which can be achievable using a high cost IMU.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.114-131
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• 2023

Personal Mobility (PM)-related accidents have increased rapidly since PM use was activated. In response to the increase in these accidents, the government strengthened regulations for PM users on May 13, 2021. The number of the accidents in which the PM user was a victim decreased significantly. In contrast, the increasing number of accidents in which PM user was the offender did not decrease significantly. In most of these accidents, the PM user was the offender who crashed into pedestrians. Hence, the safety of pedestrians is threatened. Therefore, this study analyzed the factors, such as the regulations, urban built environment, and personal characteristics, affecting the severity of PM-pedestrian accidents by focusing on PM-pedestrian crashes. This study analyzed the PM-pedestrian accidents in Seoul from 2020 to 2021 using binary logistic regression model. Through these results, this study proposed the policy implications.