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paper/paper.bib

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@@ -195,8 +195,8 @@ @Article{Lovelace
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}
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@MastersThesis{terHoeven2024master,
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author = {ter Hoeven, Ewout},
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author = {{Ter Hoeven}, Ewout},
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school = {Delft University of Technology},
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title = {When do autonomous vehicles solve orexacerbate different urban mobility problems?},
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title = {When do autonomous vehicles solve or exacerbate different urban mobility problems?},
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year = {2024},
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}

paper/paper.md

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@@ -102,6 +102,7 @@ Detailed explanations of the mathematics and algorithms behind *UXsim* are descr
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Various examples and demos are available as Python scripts and Jupyter notebooks in the GitHub repository of *UXsim*.
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This section introduces three examples to illustrate some of the key features of *UXsim*: simplicity, controllability, and scalability.
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They are also available at the GitHub repository in executable notebooks.
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## Simple example
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average delay of trips: 62.6 s
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delay ratio: 0.385
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```
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The simulation of 810 vehicles for 20 minutes duration was finished in 0.06 seconds.
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The simulation of 810 vehicles for 20 minutes duration was finished in 0.06 seconds a standard office PC.
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It also outputs the figures shown in \autoref{simple_example_res} that visualize the network traffic states.
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In the figures, vehicles travel from left to right.
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The comparison of *UXsim* results and the reference values are shown in \autoref{chicago_compare}.
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Because these values were computed by different methods, we cannot draw precise conclusions about the accuracy of *UXsim*, but at least we can say that the results are well correlated and *UXsim* outputs plausible results.
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![Comparison of *UXsim* results and the reference values. left: traffic volume, right: travel time. \label{chicago_compare}](chicago_compare.png){ width=75% }
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![Comparison of *UXsim* results and the reference values. left: traffic volume, right: travel time. \label{chicago_compare}](chicago_compare.png){ width=80% }
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# Acknowledgements
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The author would like to thank contributions from [Ewout ter Hoeven](https://github.com/EwoutH) and various feedbacks from they and other users.
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The author would like to thank contributions from [Ewout ter Hoeven](https://github.com/EwoutH) and various feedbacks from they, the reviewers, and other users.
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# References

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