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SmarterROUTES-a data-driven context-aware solution for personalized dynamic routing and navigation

Jelle De Bock (UGent) and Steven Verstockt (UGent)
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Abstract
SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route by calculating the lowest-cost path from A to B using an underlying network of weighted connections. Our implementation goes a step further and ranks a set of such routes. The ranking is the result of additional weighing based on governmental data sets, extracts of the OpenStreetMap (OSM) database, or periodically adapted extracts of web services (e.g., Representational state transfer APIs). The selected data sets and their relative contributions to the overall ranking mechanism can be dynamically adapted by the end-user. Another major contribution toward a fully personalised navigation experience is the implementation of a road scene complexity scoring mechanism. Road complexity is estimated based on the combined input from geospatial data sources, traffic data, sensor analysis, and Street View-based complexity analysis. The latter input source uses Street Viewimages as input for a Densenet Convolutional Neural Network (CNN), pre-trained on buildings using Bag-of-Words and Structure-from-motion features, outputting an image descriptor. The current version uses an adapted version of this network to predict road complexity scores. The predicted values correspond with the subjective complexity judgements of the end-users.
Keywords
ROAD NETWORKS, SELECTION, Data mining, machine learning, algorithms, dynamic routing algorithms, scalable routing engines, trajectory analysis (dealing with quality and, uncertainty), web and real-time applications

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MLA
De Bock, Jelle, and Steven Verstockt. “SmarterROUTES-a Data-Driven Context-Aware Solution for Personalized Dynamic Routing and Navigation.” ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS, vol. 7, no. 1, 2021, doi:10.1145/3402125.
APA
De Bock, J., & Verstockt, S. (2021). SmarterROUTES-a data-driven context-aware solution for personalized dynamic routing and navigation. ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS, 7(1). https://doi.org/10.1145/3402125
Chicago author-date
De Bock, Jelle, and Steven Verstockt. 2021. “SmarterROUTES-a Data-Driven Context-Aware Solution for Personalized Dynamic Routing and Navigation.” ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS 7 (1). https://doi.org/10.1145/3402125.
Chicago author-date (all authors)
De Bock, Jelle, and Steven Verstockt. 2021. “SmarterROUTES-a Data-Driven Context-Aware Solution for Personalized Dynamic Routing and Navigation.” ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS 7 (1). doi:10.1145/3402125.
Vancouver
1.
De Bock J, Verstockt S. SmarterROUTES-a data-driven context-aware solution for personalized dynamic routing and navigation. ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS. 2021;7(1).
IEEE
[1]
J. De Bock and S. Verstockt, “SmarterROUTES-a data-driven context-aware solution for personalized dynamic routing and navigation,” ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS, vol. 7, no. 1, 2021.
@article{8693108,
  abstract     = {{SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route by calculating the lowest-cost path from A to B using an underlying network of weighted connections. Our implementation goes a step further and ranks a set of such routes. The ranking is the result of additional weighing based on governmental data sets, extracts of the OpenStreetMap (OSM) database, or periodically adapted extracts of web services (e.g., Representational state transfer APIs). The selected data sets and their relative contributions to the overall ranking mechanism can be dynamically adapted by the end-user. Another major contribution toward a fully personalised navigation experience is the implementation of a road scene complexity scoring mechanism. Road complexity is estimated based on the combined input from geospatial data sources, traffic data, sensor analysis, and Street View-based complexity analysis. The latter input source uses Street Viewimages as input for a Densenet Convolutional Neural Network (CNN), pre-trained on buildings using Bag-of-Words and Structure-from-motion features, outputting an image descriptor. The current version uses an adapted version of this network to predict road complexity scores. The predicted values correspond with the subjective complexity judgements of the end-users.}},
  articleno    = {{2}},
  author       = {{De Bock, Jelle and Verstockt, Steven}},
  issn         = {{2374-0353}},
  journal      = {{ACM TRANSACTIONS ON SPATIAL ALGORITHMS AND SYSTEMS}},
  keywords     = {{ROAD NETWORKS,SELECTION,Data mining,machine learning,algorithms,dynamic routing algorithms,scalable routing engines,trajectory analysis (dealing with quality and,uncertainty),web and real-time applications}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{25}},
  title        = {{SmarterROUTES-a data-driven context-aware solution for personalized dynamic routing and navigation}},
  url          = {{http://dx.doi.org/10.1145/3402125}},
  volume       = {{7}},
  year         = {{2021}},
}

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