Real road networks on digital maps with applications in the search for optimal routes
Google Maps web mapping service allows, through its extensive API development tool, to extract, process and store updated and real-time road information such as the aerial view of a road network, the travel time and distance between two points and the geographic coordinates of intersections (Di Nata...
- Autores:
-
Viloria, Amelec
Varela Izquierdo, Noel
Ovallos, David
Pineda Lezama, Omar Bonerge
RONCALLO PICHON, ALBERTO DE JESUS
Martinez Ventura, Jairo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7712
- Acceso en línea:
- https://hdl.handle.net/11323/7712
https://doi.org/10.1007/978-981-15-7234-0_85
https://repositorio.cuc.edu.co/
- Palabra clave:
- Digraph
Road network
Google maps API
Dijkstra’s algorithm
Alternative route
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Real road networks on digital maps with applications in the search for optimal routes |
| title |
Real road networks on digital maps with applications in the search for optimal routes |
| spellingShingle |
Real road networks on digital maps with applications in the search for optimal routes Digraph Road network Google maps API Dijkstra’s algorithm Alternative route |
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Real road networks on digital maps with applications in the search for optimal routes |
| title_full |
Real road networks on digital maps with applications in the search for optimal routes |
| title_fullStr |
Real road networks on digital maps with applications in the search for optimal routes |
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Real road networks on digital maps with applications in the search for optimal routes |
| title_sort |
Real road networks on digital maps with applications in the search for optimal routes |
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Viloria, Amelec Varela Izquierdo, Noel Ovallos, David Pineda Lezama, Omar Bonerge RONCALLO PICHON, ALBERTO DE JESUS Martinez Ventura, Jairo |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Varela Izquierdo, Noel Ovallos, David Pineda Lezama, Omar Bonerge RONCALLO PICHON, ALBERTO DE JESUS Martinez Ventura, Jairo |
| dc.subject.spa.fl_str_mv |
Digraph Road network Google maps API Dijkstra’s algorithm Alternative route |
| topic |
Digraph Road network Google maps API Dijkstra’s algorithm Alternative route |
| description |
Google Maps web mapping service allows, through its extensive API development tool, to extract, process and store updated and real-time road information such as the aerial view of a road network, the travel time and distance between two points and the geographic coordinates of intersections (Di Natale et al. in understanding and using the controller area network communication protocol. Springer, New York, NY, 2012 [1]). However, trivial data required in the construction of the digraph, such as the relationship of the streets associated to those intersections and the type of direction that corresponds to each street, do not exist as an attribute in the API since they are not freely accessible or an excessive cost must be paid for the database. Therefore, a practical way to obtain this specific information is through the development of an application that allows the visual selection of the characteristic elements of a network and the extraction of the necessary data in the construction of related digraphs as a tool in the solution of road problems (Rutty et al. in Transp Res Part Transp Environ 24:44–51, 2013 [2]). This research proposes a method to build digraphs with an application in the Google Maps API in the visual extraction of elements such as vertices (intersections), edges (streets) and direction arrows (road direction), allowing the application of Dijkstra’s algorithm in search of alternative routes. |
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2021 |
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2021-01-18T20:51:10Z |
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2021-01-18T20:51:10Z |
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2021 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://hdl.handle.net/11323/7712 |
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https://doi.org/10.1007/978-981-15-7234-0_85 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
1. Di Natale M, Zeng H, Giusto P, Ghosal (2012) Understanding and using the controller area network communication protocol. Springer, New York, NY 2. Rutty M, Matthews L, Andrey J, Matto TD (2013) Eco-driver training within the City of Calgary’s municipal fleet: monitoring the impact. Transp Res Part Transp Environ 24:44–51 3. Zarkadoula M, Zoidis G, Tritopoulou E (2007) Training urban bus drivers to promote smart driving: a note on a Greek eco-driving pilot program. Transp Res Part Transp Environ 12(6):449–451 4. Strömberg HK, Karlsson ICM (2013) Comparative effects of eco-driving initiatives aimed at urban bus drivers—results from a field trial. Transp Res Part Transp Environ 22:28–33 5. Vagg C, Brace CJ, Hari D, Akehurst S, Poxon J, Ash L (2013) Development and field trial of a driver assistance system to encourage eco-driving in light commercial vehicle fleets. IEEE Trans Intell Transp Syst 14(2):796–805 6. Ferreira JC, de Almeida J, da Silva AR (2015) The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process. IEEE Trans Intell Transp Syst 16(5):2653–2662 7. Rionda A et al (2014) Blended learning system for efficient professional driving. Comput Educ 78:124–139 8. Restrepo J, Sánchez J (2004) Aplicación de la teoría de grafos y el algoritmo de Dijkstra para determinar las distancias y las rutas más cortas en una ciudad. Scientia et technica 10(26):121–126 9. Nathaniel O, Nsikan A (2017) Anapplication of Dijkstra’s Algorithm to shortest route problem. IOSR J Math 13(3):20–32 10. Saboohi Y, Farzaneh H (2009) Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Appl Energy 86(10):1925–1932 11. Hellström E, Åslund J, Nielsen L (2010) Design of an efficient algorithm for fuel-optimal look-ahead control. Control Eng Pract 18(11):1318–1327 12. Saerens B, Vandersteen J, Persoons T, Swevers J, Diehl M, Van den Bulck E (2009) Minimization of the fuel consumption of a gasoline engine using dynamic optimization. Appl Energy 86(9):1582–1588 13. Mensing F, Trigui R, Bideaux E (2011) Vehicle trajectory optimization for application in ECO-driving. In: 2011 IEEE vehicle power and propulsion conference, pp 1–6 14. Rionda Rodriguez A, Martinez Alvarez D, Paneda XG, Arbesu Carbajal D, Jimenez JE, Fernandez Linera F (2013) Tutoring system for the efficient driving of combustion vehicles. IEEE Rev Iberoam Tecnol Aprendiz RITA 8(2):82–89 15. Pañeda G et al (2016) An architecture for a learning analytics system applied to efficient driving. IEEE Rev Iberoam Tecnol Aprendiz RITA 11(3):137–145 16. Mokhtar K, Shah MZ (2006) A regression model for vessel turnaround time. Tokyo academic industry & culture integration tour, pp 10–19 17. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40 2019, pp 1201–1206 18. Amelec V (2015) Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv Sci Lett 21(5):1406–1408 19. Viloria A, Robayo PV (2016) Inventory reduction in the supply chain of finished products for multinational companies. Indian J Sci Technol 8(1) |
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Viloria, AmelecVarela Izquierdo, NoelOvallos, DavidPineda Lezama, Omar BonergeRONCALLO PICHON, ALBERTO DE JESUSMartinez Ventura, Jairo2021-01-18T20:51:10Z2021-01-18T20:51:10Z2021https://hdl.handle.net/11323/7712https://doi.org/10.1007/978-981-15-7234-0_85Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Google Maps web mapping service allows, through its extensive API development tool, to extract, process and store updated and real-time road information such as the aerial view of a road network, the travel time and distance between two points and the geographic coordinates of intersections (Di Natale et al. in understanding and using the controller area network communication protocol. Springer, New York, NY, 2012 [1]). However, trivial data required in the construction of the digraph, such as the relationship of the streets associated to those intersections and the type of direction that corresponds to each street, do not exist as an attribute in the API since they are not freely accessible or an excessive cost must be paid for the database. Therefore, a practical way to obtain this specific information is through the development of an application that allows the visual selection of the characteristic elements of a network and the extraction of the necessary data in the construction of related digraphs as a tool in the solution of road problems (Rutty et al. in Transp Res Part Transp Environ 24:44–51, 2013 [2]). This research proposes a method to build digraphs with an application in the Google Maps API in the visual extraction of elements such as vertices (intersections), edges (streets) and direction arrows (road direction), allowing the application of Dijkstra’s algorithm in search of alternative routes.Viloria, AmelecVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Ovallos, David-will be generated-orcid-0000-0003-0836-2287-600Pineda Lezama, Omar BonergeRONCALLO PICHON, ALBERTO DE JESUS-will be generated-orcid-0000-0002-1290-0132-600Martinez Ventura, Jairoapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_85DigraphRoad networkGoogle maps APIDijkstra’s algorithmAlternative routeReal road networks on digital maps with applications in the search for optimal routesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Di Natale M, Zeng H, Giusto P, Ghosal (2012) Understanding and using the controller area network communication protocol. Springer, New York, NY2. Rutty M, Matthews L, Andrey J, Matto TD (2013) Eco-driver training within the City of Calgary’s municipal fleet: monitoring the impact. Transp Res Part Transp Environ 24:44–513. Zarkadoula M, Zoidis G, Tritopoulou E (2007) Training urban bus drivers to promote smart driving: a note on a Greek eco-driving pilot program. Transp Res Part Transp Environ 12(6):449–4514. Strömberg HK, Karlsson ICM (2013) Comparative effects of eco-driving initiatives aimed at urban bus drivers—results from a field trial. Transp Res Part Transp Environ 22:28–335. Vagg C, Brace CJ, Hari D, Akehurst S, Poxon J, Ash L (2013) Development and field trial of a driver assistance system to encourage eco-driving in light commercial vehicle fleets. IEEE Trans Intell Transp Syst 14(2):796–8056. Ferreira JC, de Almeida J, da Silva AR (2015) The impact of driving styles on fuel consumption: a data-warehouse-and-data-mining-based discovery process. IEEE Trans Intell Transp Syst 16(5):2653–26627. Rionda A et al (2014) Blended learning system for efficient professional driving. Comput Educ 78:124–1398. Restrepo J, Sánchez J (2004) Aplicación de la teoría de grafos y el algoritmo de Dijkstra para determinar las distancias y las rutas más cortas en una ciudad. Scientia et technica 10(26):121–1269. Nathaniel O, Nsikan A (2017) Anapplication of Dijkstra’s Algorithm to shortest route problem. IOSR J Math 13(3):20–3210. Saboohi Y, Farzaneh H (2009) Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Appl Energy 86(10):1925–193211. Hellström E, Åslund J, Nielsen L (2010) Design of an efficient algorithm for fuel-optimal look-ahead control. Control Eng Pract 18(11):1318–132712. Saerens B, Vandersteen J, Persoons T, Swevers J, Diehl M, Van den Bulck E (2009) Minimization of the fuel consumption of a gasoline engine using dynamic optimization. Appl Energy 86(9):1582–158813. Mensing F, Trigui R, Bideaux E (2011) Vehicle trajectory optimization for application in ECO-driving. In: 2011 IEEE vehicle power and propulsion conference, pp 1–614. Rionda Rodriguez A, Martinez Alvarez D, Paneda XG, Arbesu Carbajal D, Jimenez JE, Fernandez Linera F (2013) Tutoring system for the efficient driving of combustion vehicles. IEEE Rev Iberoam Tecnol Aprendiz RITA 8(2):82–8915. Pañeda G et al (2016) An architecture for a learning analytics system applied to efficient driving. IEEE Rev Iberoam Tecnol Aprendiz RITA 11(3):137–14516. Mokhtar K, Shah MZ (2006) A regression model for vessel turnaround time. Tokyo academic industry & culture integration tour, pp 10–1917. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. ANT/EDI40 2019, pp 1201–120618. Amelec V (2015) Increased efficiency in a company of development of technological solutions in the areas commercial and of consultancy. Adv Sci Lett 21(5):1406–140819. Viloria A, Robayo PV (2016) Inventory reduction in the supply chain of finished products for multinational companies. 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