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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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
Dataset . 2023
License: CC BY
Data sources: Datacite
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PassengerRequestDataset

Authors: Pengbo Zhu;

PassengerRequestDataset

Abstract

Passenger Request Dataset for Urban Transport Study in Shenzhen, China The dataset in this repository is associated with our paper titled "Idle-vehicle Rebalancing Coverage Control for Ride-sourcing systems" which can be found at this link. The study focuses on urban on-demand mobility in Shenzhen, China. There are two primary components of the dataset: the road network and passenger requests. Road Network Details: The road network specifically captures characteristics from two primary districts in Shenzhen, China, namely Futian and Luohu. It's designed as a graph where intersections are depicted as nodes, and road segments as links. These segments align with their actual geographical lengths. The two key columns in this dataset are: streets, which portrays the entire road network in graph format. N_coord, showing the geographical coordinates of each node. Here, the first column gives the latitude and the second, the longitude. Passenger Requests: This section is based on synthesized data that captures passenger requests over a period of 3 hours. The parameter γ is used to create synthetic destination distributions from given origin distributions. Its value, ranging between [0,1][0,1], indicates the balance in this Origin-Destination distribution. For instance, a smaller γ value points to a greater imbalance between the two distributions. Datasets labeled request_gamma_000, request_gamma_025, request_gamma_050, request_gamma_075, and request_gamma_100 contain details of passenger requests when γ equals 0, 0.25, 0.5, 0.75, and 1, respectively. The columns in this data are: time, indicating when the request was made. orig, which points to the starting node on the graph. dest, the destination node on the graph. Usage Instructions: For those using Matlab, the data can be accessed with the code: load('path/to/shzn_Streets'); load('path/to/shzn_Coord'); And if you need to fetch passenger request data for γ = 0: load('path/to/request_gamma_000'); Visualization: To visually represent the Shenzhen road network on Matlab, you can use the following code: graph_shenzhen = plot(shzn_Streets, 'XData', shzn_Coord(:, 1), 'YData', shzn_Coord(:, 2), 'Marker', 'none', 'LineStyle', '-', 'LineWidth', 1); Citation: If you decide to utilize this dataset for your research, we'd appreciate a reference to our paper. The citation details are: @INPROCEEDINGS{ZHU2022, author={Zhu, Pengbo and Sirmatel, Isik Ilber and Trecate, Giancarlo Ferrari and Geroliminis, Nikolas}, booktitle={2022 European Control Conference (ECC)}, title={Idle-vehicle Rebalancing Coverage Control for Ride-sourcing systems}, year={2022}, volume={}, number={}, pages={1970-1975}, doi={10.23919/ECC55457.2022.9838069}} This dataset is also featured in our paper, "Data-enabled Predictive Control for Empty Vehicle Rebalancing," which you can find at this link. The citation details are: @INPROCEEDINGS{ZHU2023, author={Zhu, Pengbo and Ferrari-Trecate, Giancarlo and Geroliminis, Nikolas}, booktitle={2023 European Control Conference (ECC)}, title={Data-enabled Predictive Control for Empty Vehicle Rebalancing}, year={2023}, volume={}, number={}, pages={1-6}, doi={10.23919/ECC57647.2023.10178140}}

Keywords

passenger request data; urban mobility; demand and supply imbalance

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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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