Nottingham Bus Route Optimisation Instances
Description
The present datasets contain instance data and results which were presented in the publications "An Adaptive Scaled Network for Public Transport Route Optimisation" by Philipp Heyken Soares, Christine L. Mumford, Kwabena Amponsah and Yong Mao, 2019 in "Public Transport" (Springer), and "Optimising Bus Routes with Fixed Terminal Nodes: Comparing Hyper-heuristics with NSGAII on Realistic Transportation" by Leena Ahmed, Philipp Heyken Soares, Christine L. Mumford, and Yong Mao at the GECCO 2019 conference. The data sets can be found in the folder Instance_Data_ASN ("Adaptive Scaled Network...") and Instance_Data_OBR ("Optimising Bus Routes..."). Both contain the instance data sets of the respective publications in the subfolder. These include: - X_Nodes.csv/.txt: Contains information on the network nodes (node IDs, longitude and latitude coordinates, and if the respective node is a potential terminal (1-yes, 0-no) ) - X_TravelTimes.csv/.txt: Contains a symmetrical matrix with the travel times between directly connected nodes in minutes (see ASN section 2.4). Travel time between not directly connected nodes is given as infinite. - X_Demand.csv/.txt: Contains the travel demand as a symmetrical matrix with the number of trips from each node to each other node (see ASN section 2.6). - X_RealRoutes.txt: Contains the real-world bus routes (see ASN section 4) of the study area as lists of node IDs as given in the coordinates file. (Only in reduced instances) - The "Results"-folder contains subfolder the results of the experiments shown in the respective papers. In addition to the instance data, we also provide a python program to evaluate route sets with the here provided instance data in the given format (see folder Evaluation_Program). Finally, we also include a collection of maps showing the nodes and links of the instance networks as HTML-files (see folder Maps). More detailed information can be found in the README files. The zip file you find in the data collection contains the entire data set to simplify the download.
Files
Steps to reproduce
For detailed information on how these instance data sets were generated and how they can be reproduced for other study areas, please see the paper "An Adaptive Scaled Network for Public Transport Route Optimisation" by Philipp Heyken Soares, Christine L. Mumford, Kwabena Amponsah and Yong Mao, 2019 in "Public Transport" (Springer)