Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by DMSP/Ols Night Light Image

Akiyama, Yuki; Ueyama, Satoshi; Shibasaki, Ryosuke; Adachi, Ryuichiro

doi:10.5194/isprsannals-iii-2-77-2016

Cited by 3 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…For this purpose, a statistical summary consisting of many people and a long duration is considered rather than individual pedestrian behavior. For example, in [21], abnormal events such as disasters and road accidents were detected in a large area by analyzing mass GPS data. In [1], a smart platform for monitoring the people flow on a large campus was introduced, in which Wi-Fi tracking and environmental parameters such as the CO concentration, temperature, and humidity were used.…”

Section: Related Workmentioning

confidence: 99%

Measuring “Nigiwai” From Pedestrian Movement

et al. 2021

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The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call ''Nigiwai,'' from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.

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Section: Related Workmentioning

confidence: 99%

Measuring “Nigiwai” From Pedestrian Movement

et al. 2021

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“…However, this technique is not cost-effective as it uses unmanned vehicles and high range communication infrastructure. Other than that, author in [5] has developed an Event detection obtained from mobile phone users. It can detect both positive (i.e: festivals) and negative events (i.e: natural disasters and road accidents) by acquire location, areas and days of Events occurred from mobile phones carriers.…”

Section: Introductionmentioning

confidence: 99%

Self-select target neighboring base station assisted handover for natural disaster in LTE-A network

Yusof

Azhar

Ya’acob

2020

IJEECS

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This paper presents Self-Select Target Neighboring Base Station Assisted Handover for Natural Disaster in LTE-A Network. In this study, two parameters have been introduced which are known as received signal strength of user (RSS) and left over power of base station (LoP) in order to maintained good QoS of UE and prolong battery life of base station when there is power outage. A distance fraction coefficient (α) with value 0.2 has been introduced to RSS expression to improve the signal strength by reducing the area that the UE’s covered. Both parameters are used to calculate weighted-average score (WAS) for selection of potential target base station to avoid more users to connect to the affected base station. From the results, sRSS=0.8 gave the highest WAS with value of WAS=0.84 for users from 1 to 100 compares to other value of sRSS. Moreover, by using no natural disaster condition as reference, Condition 1 (wRSS=80%, wLoP=20% ) with the lowest percentage of improvement (3.75%.) will be chosen as handover condition as it near to base station, avoid overloaded users to the affected base station, hence prolong battery life as it only use 20% of battery usage.

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“…Author in [2] and [3] proposed a navigation system using Device-to-Device (D2D) communications and Global Positioning System (GPS) to rescue a person trapped during disaster. Researchers in [4] developed a method for event detection using mass GPS data provided from mobile phone carriers. By using GPS, it can accurately detect the location however it makes the mobile phone's battery drain faster due to the GPS requires more energy.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of handover parameter for natural disaster in LTE-A network

Yusof

Azhar

Ya’acob

2019

IJEECS

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<span>Natural disaster is an unpredictable event which can cause service communication disruption. The disruption of service communication can be divided into three cases which are power outage, broken backhaul and physical destruction by disaster. This paper focuses on power outage case. Although each base stations equipped with backup batteries, it only last for several hours. After natural disaster hit certain areas, traffic is highly congested due to panic and rescue operation of lifesaving. This scenario will drain the backup batteries quickly. In order to limit the UEs’ connection to the affected base station, this research proposed to adjust Received Signal Strength (RSS) based on parameter called as a distance fraction coefficient, α so that the affected coverage area is reduced when in disaster area situation. The parameter is added in the path loss equation and later in the RSS equation. The numerical results show the RSS improved whereby α=0.2 gives the optimal value and therefore limit the incoming traffic to the affected base station. </span>

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Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by DMSP/Ols Night Light Image

Cited by 3 publications

References 7 publications

Measuring “Nigiwai” From Pedestrian Movement

Measuring “Nigiwai” From Pedestrian Movement

Self-select target neighboring base station assisted handover for natural disaster in LTE-A network

Performance analysis of handover parameter for natural disaster in LTE-A network

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