Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea

Lee, Kwang-Sub; Eom, Jin Ki; Lee, Jun; Ko, Sangpil

doi:10.3390/su13063025

Cited by 11 publications

(4 citation statements)

References 69 publications

(156 reference statements)

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“…In particular, the concern is that inequalities in access to and use of mobile phones may be reflected in these data. The resulting research may skew attention and possibly resources towards already advantaged social groups (Grantz et al, 2020) or fail to adequately include groups such as the aging population (Guo et al, 2019;Lee et al, 2021). The question of bias is especially relevant for MPA data where datasets are assembled by commercial intermediaries.…”

Section: Theorymentioning

confidence: 99%

Assessing the representativeness of mobile phone data

Sinclair¹,

Maadi²,

Zhao³

et al. 2022

Preprint

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New forms of mobile phone data offer enormous potential for the advancement of our understanding of human activity and mobility. However, uncertainty in the representativeness of data creates ethical risks that underlying biases could impact results and mislead policy recommendations, limiting their usefulness. This paper assesses the spatial and socio-demographic representativeness of two extensive independent mobile phone application datasets over three years, covering a large and diverse city-region (Glasgow), home to over 1.8 million people. Towards this end, we advance methods in home location detection by utilising high resolution residential land use data as part of the process. Results show a very high level of representativeness across both datasets, comparable to survey-based techniques commonly used for mobility analysis in Scotland. The techniques employed to advance home location detection improve the fit substantially over a conventional technique. Our findings provide further confidence for the use of mobile phone data as an alternative data source to understand human activity, furthermore, our methods offer an improved benchmark upon which to assess the quality of similar data sources in the future.

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Section: Theorymentioning

confidence: 99%

Assessing the representativeness of mobile phone data

Sinclair¹,

Maadi²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The purpose of these surveys is to understand the impact of various factors on travel characteristics, and thus to predict the future traffic demand of residents. Shi et al discussed the temporal dynamic mobility characteristics of the elderly by bus smart card data [13]. Szeto et al visualized and uncovered the spatiotemporal travel characteristics of the elderly, and gave policy insights on the promotion of age-friendly public transport systems [1].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

Wang

Yao

et al. 2022

Sustainability

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With the aging trend in megacities, the travel behaviors of the elderly have attracted much attention. Accurate prediction of the travel behaviors of the elderly is a key link to meet the traffic demand and public facilities’ optimization. The aim of this paper was to explore the link between the travel characteristics and variables of the daily activities of the elderly. Based on a stratified sampling survey, the internal relationship between the characteristics of the elderly and their travel behavior was studied and discussed in this work. A novel grey correlation degree–genetic algorithm–back propagation (GR-GA-BP) hybrid model was proposed to predict the travel behavior of the elderly. Then, a grey correlation degree module was established and used to analyze the correlation between the individual elderly characteristics and their travel behavior. The results showed the following: (1) Both the times of weekly trips (y1) and average round-trip travel time (y2) were highly sensitive to the external environment, especially buses, subway stations, and recreational facilities. The size of the family was less sensitive to the travel behavior. (2) Referring to prediction of the times of weekly trips, the MRE of the proposed model was 23.12%, which was 15.22% less than the baseline models. (3) In terms of the prediction of round-trip travel time, the MRE of the proposed model was 7.13%, which was 14.00–69.41% lower than the baseline models. (4) The times of trips per week were 3.5. In summary, this paper provides technical support for formulating traffic demand policies and facilitates the configuration of cities for an aging society.

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“…Human trajectories have a high degree of regularity in certain spaces and times [10]. Many data sources can be used, such as smart card and mobile phone data [11][12][13][14][15]. They are valuable information that can help to understand human mobility patterns in a city [13,16,17].…”

Section: Literature Reviewmentioning

confidence: 99%

A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

Shi

Liu

Lai

et al. 2021

IJGI

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In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.

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Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea

Cited by 11 publications

References 69 publications

Assessing the representativeness of mobile phone data

Assessing the representativeness of mobile phone data

Exploration and Prediction of the Elderly Travel Behavior Based on a Novel GR-GA-BP Hybrid Model

A Data-Driven Framework for Analyzing Spatial Distribution of the Elderly Cardholders by Using Smart Card Data

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