Modeling the Time Spent at Points of Interest Based on Google Popular Times
Location-based applications are increasingly popular as smartphones with navigation capabilities are becoming more prevalent. Analyzing the time spent by visitors at Points of Interests (POIs) is crucial in various fields, such as urban planning, tourism, marketing, and transportation, as it provide...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10223210/ |
| _version_ | 1797685403525840896 |
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| author | Ali Jamal Mahdi Tamas Tettamanti Domokos Esztergar-Kiss |
| author_facet | Ali Jamal Mahdi Tamas Tettamanti Domokos Esztergar-Kiss |
| author_sort | Ali Jamal Mahdi |
| collection | DOAJ |
| description | Location-based applications are increasingly popular as smartphones with navigation capabilities are becoming more prevalent. Analyzing the time spent by visitors at Points of Interests (POIs) is crucial in various fields, such as urban planning, tourism, marketing, and transportation, as it provides insights into human behavior and decision-making. However, collecting a large sample of behavioral data by using traditional survey methods is expensive and complicated. To address this challenge, this study explores the use of crowdsourcing tools, specifically Google Popular Times (GPT), as an alternative source of information to predict the time spent at POIs. The research applies a robust regression model to analyze the data obtained from GPT. The popularity trends of the different POI categories are used to indicate the peak hours of the time spent in the city of Budapest. Non-spatial parameters such as the rating, the number of reviewers, and the category of the POIs are utilized. Furthermore, a Geographic Information System (GIS) is applied to extract the spatial parameters such as the security and safety levels, the availability of car parking, and public transport (PT) stations. The robust linear models are statistically significant based on the p-values, thus indicating a strong relationship between the independent variables and the time spent at POIs. The weekday and weekend models present 69.5% and 73.9% of the variance in the time spent at POIs, respectively. Furthermore, it is demonstrated that the visitors’ behavior is strongly affected by the category of the POIs variable. This study shows how GPT can be utilized to better understand, analyze, and forecast people’s behavior. The solution presented in this study can serve as an essential support of activity-based models, where the time spent is a crucial parameter for scheduling and optimizing activity chains. |
| first_indexed | 2024-03-12T00:43:41Z |
| format | Article |
| id | doaj.art-ed7bb111dec14a08b2554efd718a2790 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T00:43:41Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ed7bb111dec14a08b2554efd718a27902023-09-14T23:01:39ZengIEEEIEEE Access2169-35362023-01-0111889468895910.1109/ACCESS.2023.330595710223210Modeling the Time Spent at Points of Interest Based on Google Popular TimesAli Jamal Mahdi0https://orcid.org/0000-0002-6642-1815Tamas Tettamanti1https://orcid.org/0000-0002-8934-3653Domokos Esztergar-Kiss2https://orcid.org/0000-0002-7424-4214Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Budapest, HungaryLocation-based applications are increasingly popular as smartphones with navigation capabilities are becoming more prevalent. Analyzing the time spent by visitors at Points of Interests (POIs) is crucial in various fields, such as urban planning, tourism, marketing, and transportation, as it provides insights into human behavior and decision-making. However, collecting a large sample of behavioral data by using traditional survey methods is expensive and complicated. To address this challenge, this study explores the use of crowdsourcing tools, specifically Google Popular Times (GPT), as an alternative source of information to predict the time spent at POIs. The research applies a robust regression model to analyze the data obtained from GPT. The popularity trends of the different POI categories are used to indicate the peak hours of the time spent in the city of Budapest. Non-spatial parameters such as the rating, the number of reviewers, and the category of the POIs are utilized. Furthermore, a Geographic Information System (GIS) is applied to extract the spatial parameters such as the security and safety levels, the availability of car parking, and public transport (PT) stations. The robust linear models are statistically significant based on the p-values, thus indicating a strong relationship between the independent variables and the time spent at POIs. The weekday and weekend models present 69.5% and 73.9% of the variance in the time spent at POIs, respectively. Furthermore, it is demonstrated that the visitors’ behavior is strongly affected by the category of the POIs variable. This study shows how GPT can be utilized to better understand, analyze, and forecast people’s behavior. The solution presented in this study can serve as an essential support of activity-based models, where the time spent is a crucial parameter for scheduling and optimizing activity chains.https://ieeexplore.ieee.org/document/10223210/Point of InterestGoogle popular timesGIStime spentregression model |
| spellingShingle | Ali Jamal Mahdi Tamas Tettamanti Domokos Esztergar-Kiss Modeling the Time Spent at Points of Interest Based on Google Popular Times IEEE Access Point of Interest Google popular times GIS time spent regression model |
| title | Modeling the Time Spent at Points of Interest Based on Google Popular Times |
| title_full | Modeling the Time Spent at Points of Interest Based on Google Popular Times |
| title_fullStr | Modeling the Time Spent at Points of Interest Based on Google Popular Times |
| title_full_unstemmed | Modeling the Time Spent at Points of Interest Based on Google Popular Times |
| title_short | Modeling the Time Spent at Points of Interest Based on Google Popular Times |
| title_sort | modeling the time spent at points of interest based on google popular times |
| topic | Point of Interest Google popular times GIS time spent regression model |
| url | https://ieeexplore.ieee.org/document/10223210/ |
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