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ZENODO
Other ORP type . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other ORP type . 2022
License: CC BY
Data sources: Datacite
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Replication package for the paper: Vehicular crowd-sensing with high-mileage vehicles: investigating spatio-temporal coverage dynamics in historical cities with complex urban road networks

Authors: Starace, Luigi Libero Lucio; Rocco di Torrepadula, Franca; Di Martino, Sergio; Mazzocca, Nicola;

Replication package for the paper: Vehicular crowd-sensing with high-mileage vehicles: investigating spatio-temporal coverage dynamics in historical cities with complex urban road networks

Abstract

This package includes supplementary materials and a KNIME workflow to replicate our case study investigating the road-network coverage achievable by different-sized fleets of taxis in the cities of Porto (PT) and Rome (IT). As follows, we give detailed instructions to reproduce our results. Download and install the KNIME Analytics Platform (version 4.3). Download and install KNOT: KNime mObility Toolkit, the custom KNIME extension we deleveloped (version 1.0.1). To enable visualization of the results on an interactive map, download and install the third-party KNIME Spatial Processing Nodes extension; Download the provided KNIME workflow files (rome-coverage-experiment.knwf and porto-coverage-experiment.knwf), and import them into your KNIME instance. If you want to re-execute the provided workflow from scratch, follow these additional steps: Download the provided data (porto-taxi-trajectories-dataset.csv and porto.osm files for the Porto experiments, taxi_febryary.txt and rome.osm for the Rome experiments); Note that the the trajectory dataset is downloaded from https://archive.ics.uci.edu/ml/datasets/Taxi+Service+Trajectory+-+Prediction+Challenge,+ECML+PKDD+2015, which is based on the work Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L., ”Predicting Taxi–Passenger Demand Using Streaming Data”. In: IEEE Transactions on Intelligent Transportation Systems, vol.14, no.3, pp.1393-1402, September (2013). The Rome trajectory dataset has been downloaded from https://crawdad.org/roma/taxi/20140717/ The map-matching procedure requires an OSRM instance capable of serving routing requests over the city of Porto. For your convenience, we prepared a virtual machine image with OSRM installed and configured (OSRM-Porto-Ubuntu-20.04LTS.ova).You can run this virtual machine with VirtualBox and login with the credentials Username: User; Password: user. After login, it is possible to start the OSRM instance by opening a terminal and running the following command: osrm-routed --max-matching-size 3000 OSRM-data/porto/porto.osrm Take note of the local IP address of the virtual machine (you may use the ifconfig command to get it). To run the Rome experiments, it is necessary to add the Rome road network (which can be downloaded from geofabrik) to the OSRM instance. For more detail, refer to the official OSRM docs. Make sure that the CSV Reader node is configured so that it reads the porto-taxi-trajectories-dataset.csv for the Porto experiments (resp. taxi_february.txt file for the Rome experiments) file you downloaded. Similarly, configure the Map Matcher node so that it reads the porto.osm (resp. rome.osm) file for you downloaded, and so that the IP address of the OSRM instance is correct. Run the KNIME workflows.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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