MA-SSR: A Memetic Algorithm for Skyline Scenic Routes Planning Leveraging Heterogeneous User-Generated Digital Footprints

Most existing trip planning work ignores the issue of planning detailed travel routes between points of interest, leaving the task to online map services or commercial Global Positioning System (GPS) navigators. However, such a service or navigator cannot meet the diverse requirements of users. Particularity, in this paper, the authors aim at planning travel routes that not only minimize the distance but provide high-quality sceneries along the way as well. To this end, the authors propose a novel two-phase framework to plan travel routes efficiently in a large road network considering multiple criteria, i.e., both the quality of the scenic view and the travel distance. In the first phase, the authors enrich the edges and assign a proper scenic view score for each of them by extracting relevant information from heterogeneous digital footprints of geotagged images and check-ins. In the second phase, on the top of the enriched road network, given users’ trip queries, the authors employ the concept of skyline operator and propose a memetic algorithm (MA) to discover a set of equally optimal routes with diverse travel distances for users to pick from. Finally, to validate the efficiency and effectiveness of the proposed framework, the authors conduct extensive experiments on three real-world datasets from the Bay Area in the city of San Francisco, CA, USA, which contain a road network with 3771 nodes and 5940 edges crawled from OpenStreetMap, more than 31 000 geotagged images generated by 1571 Flickr users in one year; and 110 214 check-ins left by 15 680 Foursquare users in six months. Results demonstrate that the MA yields high-quality solutions that are reasonably close to the optima but within desirable computation time and considerably better than the baseline solutions obtained by genetic algorithms.


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  • Accession Number: 01646276
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 22 2017 4:45PM