The activity of commuting to and from a place ofwork affects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the effects of changes to working patterns and locations and inform decision making. In this paper we outline an agent-based software framework that combines realworld data from multiple sources to simulate the actions of commuters. We demonstrate the framework using data supplied by an employer based in the City of Edinburgh UK. We demonstrate that the BDI-inspired decision making framework used is capable of forecasting the transportation modes to be used. Finally we present a case study, demonstrating the use of the framework to predict the impact of moving state within the organisation to a new work site.
This project uses gradle to pull and link external libraries. Tested with gradle version 4.3.1
Distribution Link: https://downloads.gradle.org/distributions/gradle-4.3.1-all.zip
Run the command:
$ gradle build
Alternatively, if you would like to build an eclipse project solution, Run:
$ gradle eclipse
Included is testData.csv showing an example of our problem files.
The above usefulData.py script is an example of how we scraped our initial travel survey data.
If you have any questions about how the above code works, please contact N.Urquhart@napier.ac.uk