Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management
This paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emiss...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2020-02-01
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| Series: | Transport and Telecommunication |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/ttj-2020-0004 |
| _version_ | 1818343573961048064 |
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| author | Bandeira Jorge M. Tafidis Pavlos Macedo Eloísa Teixeira João Bahmankhah Behnam Guarnaccia Cláudio Coelho Margarida C. |
| author_facet | Bandeira Jorge M. Tafidis Pavlos Macedo Eloísa Teixeira João Bahmankhah Behnam Guarnaccia Cláudio Coelho Margarida C. |
| author_sort | Bandeira Jorge M. |
| collection | DOAJ |
| description | This paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance. |
| first_indexed | 2024-12-13T16:32:44Z |
| format | Article |
| id | doaj.art-97a47412faf5467f8648453b879c5db3 |
| institution | Directory Open Access Journal |
| issn | 1407-6179 |
| language | English |
| last_indexed | 2024-12-13T16:32:44Z |
| publishDate | 2020-02-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Transport and Telecommunication |
| spelling | doaj.art-97a47412faf5467f8648453b879c5db32022-12-21T23:38:29ZengSciendoTransport and Telecommunication1407-61792020-02-01211476010.2478/ttj-2020-0004ttj-2020-0004Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic ManagementBandeira Jorge M.0Tafidis Pavlos1Macedo Eloísa2Teixeira João3Bahmankhah Behnam4Guarnaccia Cláudio5Coelho Margarida C.6University of Aveiro, Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalHasselt University - Campus Diepenbeek Agoralaan Gebouw H - B-3590 Diepenbeek, Kantoor H-B103b, BelgiumUniversity of Aveiro, Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalUniversity of Aveiro, Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalUniversity of Aveiro, Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalUniversity of Salerno, Department of Civil Engineering, via Giovanni Paolo II 132, Fisciano, ItalyUniversity of Aveiro, Centre for Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, Campus Universitário de Santiago, 3810-193 Aveiro, PortugalThis paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.https://doi.org/10.2478/ttj-2020-0004ictgoogle mapscrowdsourcingnoisemissionssustainable transport |
| spellingShingle | Bandeira Jorge M. Tafidis Pavlos Macedo Eloísa Teixeira João Bahmankhah Behnam Guarnaccia Cláudio Coelho Margarida C. Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management Transport and Telecommunication ict google maps crowdsourcing noise missions sustainable transport |
| title | Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management |
| title_full | Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management |
| title_fullStr | Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management |
| title_full_unstemmed | Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management |
| title_short | Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management |
| title_sort | exploring the potential of web based information of business popularity for supporting sustainable traffic management |
| topic | ict google maps crowdsourcing noise missions sustainable transport |
| url | https://doi.org/10.2478/ttj-2020-0004 |
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