Predicting travel time within catchment area using Time Travel Voronoi Diagram (TTVD) and crowdsource map features
A catchment is a geographical area from which a business, service or organisation attracts its customers. A catchment area is a common way to ensure equal access to services such as hospitals, schools, libraries, ambulances, fire brigades, and shopping centres. Users will usually go to the service p...
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Veröffentlicht in: | Information processing & management 2022-05, Vol.59 (3), p.102922, Article 102922 |
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description | A catchment is a geographical area from which a business, service or organisation attracts its customers. A catchment area is a common way to ensure equal access to services such as hospitals, schools, libraries, ambulances, fire brigades, and shopping centres. Users will usually go to the service provider which is closest to their location instead of going further afield. In a time-sensitive environment where travelling time is limited, an incorrect decision might lead to serious consequences. In ambulance management, an incorrect dispatch that causes a unit late to arrive may lead to life and death situation. In this paper, we propose a Computational Geometry-based approach in determining catchment area, named Time Travel Voronoi Diagram (TTVD), not only by calculating the geographical location as used in most earlier work, but also through predicting the time travel to destination. This method can be used as a predictive analytic tool to support emergency dispatching, such as ambulance services. We utilise road features and the associated speed restrictions from crowdsource map platform in our prediction. Our simulation shows that a realistic catchment can be predicted using time-based distance with road features.
•Propose a Computational Geometry approach to predict the time-based catchment area.•Evaluate realistic road obstacles and crowdsource trajectories for realistic estimation model.•Evaluate the model using ambulance stations and road data in metropolitan Melbourne. |
doi_str_mv | 10.1016/j.ipm.2022.102922 |
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•Propose a Computational Geometry approach to predict the time-based catchment area.•Evaluate realistic road obstacles and crowdsource trajectories for realistic estimation model.•Evaluate the model using ambulance stations and road data in metropolitan Melbourne.</description><subject>Ambulance services</subject><subject>Ambulances</subject><subject>Computational geometry</subject><subject>Crowdsource</subject><subject>Emergency vehicles</subject><subject>Geographical locations</subject><subject>Predictions</subject><subject>Road obstacles</subject><subject>Services</subject><subject>Shopping centers</subject><subject>Simulation</subject><subject>Speed reduction</subject><subject>Time travel</subject><subject>Time Travel Voronoi Diagram</subject><subject>Travel</subject><subject>Travel time</subject><subject>Voronoi graphs</subject><issn>0306-4573</issn><issn>1873-5371</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEuXxAewssYFFytiJ7UasEOUlVYJFYGs5zoS6apJiu0X8Pa7CmtVopHNnrg4hFwymDJi8WU3dppty4DztvOT8gEzYTOWZyBU7JBPIQWaFUPkxOQlhBQCFYHxC_JvHxtno-k8avdnhmkbXIf12cel6ak20yw77SI1HQ7dhz1V7oBrhj8EP_eDo3JlPbzp6VVUf82tq-oZaP3w3Ydh6i7QzG9qiiVuP4YwctWYd8PxvnpL3x4fq_jlbvD693N8tMptzETMBXAolZ9YWRrWzRtUACGAbsBbqvCi5aLDgxpalrK1CU5dCNbJuuRFCSJ6fksvx7sYPX1sMUa9SmT691FwqyXIlFCSKjVSqG4LHVm-864z_0Qz0Xq1e6aRW79XqUW3K3I4ZTPV3Dr0O1mFvk0mPNupmcP-kfwHAHoHF</recordid><startdate>202205</startdate><enddate>202205</enddate><creator>Adhinugraha, Kiki</creator><creator>Taniar, David</creator><creator>Phan, Thanh</creator><creator>Beare, Richard</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>E3H</scope><scope>F2A</scope><orcidid>https://orcid.org/0000-0003-3400-6323</orcidid><orcidid>https://orcid.org/0000-0001-5884-1409</orcidid></search><sort><creationdate>202205</creationdate><title>Predicting travel time within catchment area using Time Travel Voronoi Diagram (TTVD) and crowdsource map features</title><author>Adhinugraha, Kiki ; Taniar, David ; Phan, Thanh ; Beare, Richard</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-50265768cc4a7f8d7b00e00cd0cc0b34925de42ac996bc7eab957d6bf2a555623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Ambulance services</topic><topic>Ambulances</topic><topic>Computational geometry</topic><topic>Crowdsource</topic><topic>Emergency vehicles</topic><topic>Geographical locations</topic><topic>Predictions</topic><topic>Road obstacles</topic><topic>Services</topic><topic>Shopping centers</topic><topic>Simulation</topic><topic>Speed reduction</topic><topic>Time travel</topic><topic>Time Travel Voronoi Diagram</topic><topic>Travel</topic><topic>Travel time</topic><topic>Voronoi graphs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adhinugraha, Kiki</creatorcontrib><creatorcontrib>Taniar, David</creatorcontrib><creatorcontrib>Phan, Thanh</creatorcontrib><creatorcontrib>Beare, Richard</creatorcontrib><collection>CrossRef</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><jtitle>Information processing & management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adhinugraha, Kiki</au><au>Taniar, David</au><au>Phan, Thanh</au><au>Beare, Richard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting travel time within catchment area using Time Travel Voronoi Diagram (TTVD) and crowdsource map features</atitle><jtitle>Information processing & management</jtitle><date>2022-05</date><risdate>2022</risdate><volume>59</volume><issue>3</issue><spage>102922</spage><pages>102922-</pages><artnum>102922</artnum><issn>0306-4573</issn><eissn>1873-5371</eissn><abstract>A catchment is a geographical area from which a business, service or organisation attracts its customers. 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We utilise road features and the associated speed restrictions from crowdsource map platform in our prediction. Our simulation shows that a realistic catchment can be predicted using time-based distance with road features.
•Propose a Computational Geometry approach to predict the time-based catchment area.•Evaluate realistic road obstacles and crowdsource trajectories for realistic estimation model.•Evaluate the model using ambulance stations and road data in metropolitan Melbourne.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ipm.2022.102922</doi><orcidid>https://orcid.org/0000-0003-3400-6323</orcidid><orcidid>https://orcid.org/0000-0001-5884-1409</orcidid></addata></record> |
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subjects | Ambulance services Ambulances Computational geometry Crowdsource Emergency vehicles Geographical locations Predictions Road obstacles Services Shopping centers Simulation Speed reduction Time travel Time Travel Voronoi Diagram Travel Travel time Voronoi graphs |
title | Predicting travel time within catchment area using Time Travel Voronoi Diagram (TTVD) and crowdsource map features |
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