DOI: 10.5281/zenodo.8287366
title: PassengerRequestDataset

description: <strong>Passenger Request Dataset for Urban Transport Study in Shenzhen, China</strong> 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. <strong>Road Network Details:</strong> 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: <code>streets</code>, which portrays the entire road network in graph format. <code>N_coord</code>, showing the geographical coordinates of each node. Here, the first column gives the latitude and the second, the longitude. <strong>Passenger Requests:</strong> 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 <code>request_gamma_000</code>, <code>request_gamma_025</code>, <code>request_gamma_050</code>, <code>request_gamma_075</code>, and <code>request_gamma_100</code> contain details of passenger requests when γ equals 0, 0.25, 0.5, 0.75, and 1, respectively. The columns in this data are: <code>time</code>, indicating when the request was made. <code>orig</code>, which points to the starting node on the graph. <code>dest</code>, the destination node on the graph. <strong>Usage Instructions:</strong> For those using Matlab, the data can be accessed with the code: load('path/to/shzn_Streets');<br> load('path/to/shzn_Coord'); <br> And if you need to fetch passenger request data for γ = 0: load('path/to/request_gamma_000');<br> <strong>Visualization:</strong> 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); <strong>Citation:</strong> If you decide to utilize this dataset for your research, we'd appreciate a reference to our paper. The citation details are: @INPROCEEDINGS{ZHU2022,<br> author={Zhu, Pengbo and Sirmatel, Isik Ilber and Trecate, Giancarlo Ferrari and Geroliminis, Nikolas},<br> booktitle={2022 European Control Conference (ECC)}, <br> title={Idle-vehicle Rebalancing Coverage Control for Ride-sourcing systems}, <br> year={2022},<br> volume={},<br> number={},<br> pages={1970-1975},<br> 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,<br> author={Zhu, Pengbo and Ferrari-Trecate, Giancarlo and Geroliminis, Nikolas},<br> booktitle={2023 European Control Conference (ECC)}, <br> title={Data-enabled Predictive Control for Empty Vehicle Rebalancing}, <br> year={2023},<br> volume={},<br> number={},<br> pages={1-6},<br> doi={10.23919/ECC57647.2023.10178140}}<br>