Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices

Oh, Jun-Seok; Hasan, Raed Abdullah; Irshaid, Hafez; Joo, Shinhye; Lee, Sang‐Woo; Al‐Fuqaha, Ala

doi:10.1016/j.jth.2018.05.094

Journal of Transport & Health

2018

DOI: 10.1016/j.jth.2018.05.094

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Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices

Jun-Seok Oh

Raed Abdullah Hasan

Hafez Irshaid

et al.

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Cited by 5 publications

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“…In addition, the Geohash method was used in the study of Oh et al ( 54 ), which is close to the idea of Singh’s research, to assess spatial movement patterns of smart card transaction data for multi-modal transportation networks. However, to date, the Geohash method has not been used for detecting travel behavior data, especially for recognizing activity or trip information ( 55 ).…”

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confidence: 99%

User Activity and Trip Recognition using Spatial Positioning System Data by Integrating the Geohash and GIS Approaches

Irshaid¹,

Hasan

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Analyzing travel behavior in transportation networks within a city is significant to understand the user’s activity and travel pattern in relation to making improved city plans for the future. Unlike the traditional travel diary survey, GPS data have helped researchers to analyze Big Data with enriched travel information in an automated way. The focus of this research was to identify user activity and travel pattern from GPS data logs. We proposed three different approaches, including Geohash clustering, the GIS-based approach, and Combined Geohash–GIS approach, for automatic user activity and trip recognition in a continuous and aggregate manner. We developed different individual models considering different dwell times for the above three approaches. We considered three different testing scenarios based on specified tolerance levels, including simple, moderate, and critical testing to identify trip only, activity only, and sequential activity–trip analysis. In comparison with other approaches, the Combined Geohash–GIS approach considering 5 min dwell time accurately classified data with about 95% accuracy. The proposed Combined Geohash–GIS approach could significantly enhance the efficiency and accuracy of GPS travel surveys by correctly recognizing user activity and trip patterns. This proposed combined approach could serve as a foundation for a future model system of full-scale travel information identification with GPS data.

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confidence: 99%

User Activity and Trip Recognition using Spatial Positioning System Data by Integrating the Geohash and GIS Approaches

Irshaid¹,

Hasan

et al. 2020

Transportation Research Record: Journal of the Transportation R

Self Cite

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Which transport policies increase physical activity of the whole of society? A systematic review

Żukowska¹,

Gobis²,

Krajewski³

et al. 2022

Journal of Transport & Health

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Prioritizing Influential Factors on Tourist Decision-Making for Choosing Destinations Using Parsimonious Analytical Hierarchy Process (PAHP)

Jamal Mahdi,

Esztergár-Kiss

2023

Tourism Planning & Development

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Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices

Cited by 5 publications

References 0 publications

User Activity and Trip Recognition using Spatial Positioning System Data by Integrating the Geohash and GIS Approaches

User Activity and Trip Recognition using Spatial Positioning System Data by Integrating the Geohash and GIS Approaches

Which transport policies increase physical activity of the whole of society? A systematic review

Prioritizing Influential Factors on Tourist Decision-Making for Choosing Destinations Using Parsimonious Analytical Hierarchy Process (PAHP)

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