A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias
{"title":"Robust Routing Using Electrical Flows","authors":"A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias","doi":"10.1145/3567421","DOIUrl":null,"url":null,"abstract":"Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - a) support multiple (unknown) preferences that the user may have; and b) robust to changes in network conditions. We formulate a new quantification of the latter in this paper, and propose a novel method to produce alternative routes based on concepts from electrical flows and their decompositions. Our method is fundamentally different from the main techniques that produce alternative routes in road networks, which are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity, and the robustness (the connectivity between the origin and destination) of the induced set of routes.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Spatial Algorithms and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3567421","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - a) support multiple (unknown) preferences that the user may have; and b) robust to changes in network conditions. We formulate a new quantification of the latter in this paper, and propose a novel method to produce alternative routes based on concepts from electrical flows and their decompositions. Our method is fundamentally different from the main techniques that produce alternative routes in road networks, which are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity, and the robustness (the connectivity between the origin and destination) of the induced set of routes.
期刊介绍:
ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.