• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.2
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• pp.26-36
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• 2008

Currently, the pedestrian signal timing model has no consideration on the characteristics of different land use patterns and pedestrian behaviors during pedestrian signal timing calculation. This study intended to propose pedestrian signal timing models that could reflect the inherent characteristics of pedestrian and land use patterns. For this study, three major variables affecting the length of signal timing were identified: walking speed, perception-reaction time, and density-delay time. Then, the representative values of each variable were estimated through the field studies. By combining this information, several pedestrian signal timing models were developed. The data in this paper can be used for future references, and the walking environments for pedestrians could be improved by applying the models suggested in this paper.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.181-190
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• 2008

This study intended to provide the inherent characteristics of weak pedestrian including walking speed and perception-reaction time and to propose pedestrian signal timing models considering those characteristics. To collect the relevant data, field studies were conducted in virtual crosswalk environments. Walking speed were found to be 0.63 m/s and 0.57 m/s for children and the elderly, respectively. However, the elderly had higher perception-reaction time than children. By considering these characteristics, two pedestrian signal timing models were developed. Sensitivity analysis results showed that the models can produce reasonable ranges of pedestrian signal time. The data in this paper can be used for future references, and the mobility of weak pedestrian may be improved by applying the models suggested in this paper.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.55-73
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• 1994

The objective of this research is to evaluate the pedestrian signal time involving green and flashing green times. The minimum pedestrian green indication should give time for pedestrian to start crossing safely, and the flashing green indication should give time to complete the crossing. An average pedestrian crossing speed of 1.1(m/s) was estimated by analyzing the field data which was slower than the 1.2(m/s) currently used. Furthermore, the study proposed that design speed for the flashing green time should be slow speed for considerations pedestrian safety, not the average speed. The 0.78-1.01(m/s) of pedestrian speed was estimated at the elementary school areas that indicated 0.2(m/s) slower than the other areas. The pedestrian starting time (perception/reaction time) and time headway from front to back of herd was estimated to determine minimum pedestrian green time. the pedestrian starting time was estimated to determine minimum pedestrian green time. The pedestrian starting time was ranged 2.52-4.29 seconds. The time interval between the pedestrian rows was found to be 1.25-1.86 seconds, which declines as the pedestrian rows increases, The equation to calculate the pedestrian signal, which declines as the pedestrian rows increases. The equation to calculate the pedestrian signal time is proposed using the pedestrian starting time, the time interval between the pedestrian rows, and pedestrian crossing speed given area types (commercial, business, mixed, and elementary school areas), number of both-directional pedestrians for a cycle, crosswalk length and width.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.57-66
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• 2006

This paper presents new pedestrian signal timings considering pedestrian demand Pedestrian characteristics, and land use which were obtained by Pedestrian characteristics field survey and pedestrian signal operation survey. Pedestrian signal timings suggested were compared to the existing pedestrian signal timings by using real field data. pedestrian characteristics field survey was conducted to collect pedestrian crossing speed data and reaction time data. Sixteen areas in Seoul were selected for the data collection. The average pedestrian crossing speed was 1.30m/sec and the 15th Percentile speed was 1.11m/sec. The average reaction time was 2.24 seconds. Pedestrian crossing speed differs by land use, road width. pedestrian age, sex, and number of Pedestrians. Reaction time also differs by road width, pedestrian age, and number of pedestrians. Statistical testing was performed to secure reliability of the collected data.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.565-573
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• 2007

• Journal of the Korea Safety Management & Science
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• v.7 no.1
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• pp.77-86
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• 2005

An investigation was conducted to evaluate both the time required and the time allowed for persons to cross streets. Currently, the local municipality uses a standardized formula to determine the time allotted for 'WALK' signals to function allowing pedestrian traffic to cross thoroughfares. The formula to determine the 'Theoretical Time(in seconds)' is the width of the street(in meter) divided by 1.2m/s. The basis of the denominator is 'normal' walking speed. Initially, 3 locations were chosen to evaluate the time between the appearance of the 'WALK' signal and the appearance of the 'DON'T WALK'. The interval between the two signals was assumed to allow a person to begin crossing the street at the appearance of the 'WALK' signal and terminate their crossing at the appearance of the 'DON'T WALK' signal. Of the 3 locations, 2 locations(elementary?middle schools and general hospital areas), the duration of the 'WALK' signal were not properly set and therefore need more time for those who use these cross walks. Specific details regarding the crossing locations and validity of the standardized formula were also presented and discussed.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.2
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• pp.49-58
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• 2019

A number of techniques have been studied to estimate the position of pedestrians in indoor space. Among them, the technique of estimating the position using only the sensors attached to the body of the pedestrian without using the infrastructure is regarded as a very important technology for special purpose pedestrians such as the firefighters. In particular, it forms a research field under the name of Pedestrian Dead Reckoning (PDR). In this paper, we focus on a method for step detection which is essential when performing PDR using Inertial Measurement Unit (IMU) mounted on a shoe. Many researches have been done to detect the stance phase where the foot contacts the ground. Most of these methods, however, have a way to detect the specific size of the sensor signal and require thresholds for these methods. This has the difficulty of changing these thresholds if the user is different. To solve this problem, we propose a stance phase detection method that does not require any threshold value. It is expected that this result will make it easier to commercialize the technology because PDR can be implemented without user-dependent parameter setting.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.111-117
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• 2007

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.271-280
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• 2023

Pedestrian Dead Reckoning (PDR) methods using initial sensors are being studied to provide the location information of smart device users in indoor environments where satellite signals are not available. PDR can continuously estimate the location of a pedestrian regardless of the walking environment, but has the disadvantage of accumulating errors over time. Unlike this, WiFi signal-based wireless positioning technology does not accumulate errors over time, but can provide positioning information only where infrastructure is installed. It also shows different positioning performance depending on the environment. In this paper, an integrated positioning technology integrating two positioning techniques with different error characteristics is proposed. A technique for correcting the error of PDR was designed by using the location information obtained through WiFi Measurement-based fingerprinting as the measurement of Extended Kalman Filte (EKF). Here, a technique is used to variably calculate the error covariance of the filter measurements using the WiFi Fingerprinting DB and apply it to the filter. The performance of the proposed positioning technology is verified through an experiment. The error characteristics of the PDR and WiFi Fingerprinting techniques are analyzed through the experimental results. In addition, it is confirmed that the PDR error is effectively compensated by adaptively utilizing the WiFi signal to the environment through the EKF to which the adaptive error covariance proposed in this paper is applied.