cleaning up links #2

Author: behrisch

docs/JOSS/paper.bib

@@ -9,11 +9,12 @@ @inproceedings{SUMO2018
             url = {https://elib.dlr.de/124092/}
 }
 
-@Online{tapas,
+@misc{tapas,
   accessed = {2024-04-10},
-  author   = {{TAPAS}},
+	author = {},
   title    = {TAPAS (Travel Activity Pattern Simulation)},
   url      = {https://github.com/DLR-VF/TAPAS},
+	year = {},
 }
 
 @Book{MATSim:2016,

docs/JOSS/paper.md

@@ -65,11 +65,11 @@ bibliography: paper.bib
 
 # Summary
 
-Simulating a transportation system microscopically requires a solid framework that maps real-life features into the software. The Simulation of Urban MObility (SUMO) suite [@SUMO2018] features a wide range of software tools to facilitate research and application projects alike, and to help users to set-up a traffic simulation for a chosen real or synthetic area with minimum effort. At its heart is the simulation itself (in form of a command line, as well as a GUI application), and an editor for transportation networks (netedit), which can run any scenario from a single intersection up to a multi-modal network the size of the city of Berlin, Germany. The size is only limited by the hardware and the patience of the user. A wide range of methods is built not only into the simulation itself but also into several hundred accompanying Python tools to help assess the impact of any changes to the base scenario—such as changes in travel times, capacity, emissions, and more. In addition to that, `SUMO` can be controlled externally by python scripts via libsumo or the TraCI library, thereby extending furthermore what can be done with it.
+Simulating a transportation system microscopically requires a solid framework that maps real-life features into the software. The Simulation of Urban MObility (SUMO) suite [@SUMO2018] features a wide range of software tools to facilitate research and application projects alike, and to help users to set-up a traffic simulation for a chosen real or synthetic area with minimum effort. At its heart is the simulation itself (in form of a command line, as well as a GUI application), and an editor for transportation networks (netedit), which can run any scenario from a single intersection up to a multi-modal network the size of the city of Berlin, Germany. The size is only limited by the hardware and the patience of the user. A wide range of methods is built not only into the simulation itself but also into several hundred accompanying Python tools to help assess the impact of any changes to the base scenario — such as changes in travel times, capacity, emissions, and more. In addition to that, `SUMO` can be controlled externally by python scripts via libsumo or the TraCI library, thereby extending furthermore what can be done with it.
 
 # Statement of need
 
-Microscopic simulations of multi-modal transportation networks require a description of the network, and a detailed plan how objects (motorized and non-motorized vehicles, persons, goods) move through such a network. In the case of `SUMO`, this is achieved by assigning a route to each object in the simulation network that it has to follow from origin to destination, starting at a particular moment in time. The sum of all these trips is the so called origin-destination matrix, which is generally time-dependent. `SUMO` excels especially at the detailed modelling of how individual objects move in transportation networks. It is weaker, however, when it comes to the computation of the demand itself, which can be achieved by other, more dedicated tools like TAPAS [@tapas] or MATSim [@MATSim:2016].
+Microscopic simulations of multi-modal transportation networks require a description of the network, and a detailed plan how objects (motorized and non-motorized vehicles, persons, goods) move through such a network. In the case of `SUMO`, this is achieved by assigning a route to each object in the simulation network that it has to follow from origin to destination, starting at a particular moment in time. The sum of all these trips is the so called origin-destination matrix, which is generally time-dependent. `SUMO` excels especially at the detailed modelling of how individual objects move in transportation networks. It is weaker, however, when it comes to the computation of the demand itself, which can be achieved by other, more dedicated tools like [TAPAS](https://github.com/DLR-VF/TAPAS) or MATSim [@MATSim:2016].
 
 For larger networks, an additional step is needed to transfer the demand (the time-dependent OD-matrix) into real routes that the various objects follow through the network. This is called the dynamic traffic assignment, which assumes that there is a kind of equilibrium between the demand and the supply given by the network. This fairly old (first ideas are from 1924), but still prevailing concept was formalized by Wardrop [@Wardrop:1952] and Beckmann [@BeckmannEtAl:1955], and comes in several flavours such as user equilibrium (UE), stochastic user equilibrium (SUE) etc. `SUMO` contains several tools to compute these equilibria, again in the form of python scripts that repeatedly start the simulation with changing routes, until such an equilibrium is achieved.
 
@@ -79,7 +79,7 @@ For larger networks, an additional step is needed to transfer the demand (the ti
 
 # Similar and/or related packages
 
-Especially regarding transport demand and the computation of the user equilibrium, the best-known tool for this is MATSim [@MATSim:2016]. A much less developed approach which is not simulation-based can be found in [@Ortmann2022]. Many smaller tools may exist, for a more traffic flow optimized tooling the user is referred to Martin Treiber's [movsim](https://github.com/movsim/traffic-simulation-de), or Open Traffic Sim [@opentrafficsim], or City Flow [@CityFlow]. On the other side of the spectrum, there are simulators focusing on very detailed single-vehicle models, including their sensors, such as CARLA [@Carla].
+Especially regarding transport demand and the computation of the user equilibrium, the best-known tool for this is MATSim [@MATSim:2016]. A much less developed approach which is not simulation-based can be found in @Ortmann2022. Many smaller tools may exist, for a more traffic flow optimized tooling the user is referred to Martin Treiber's [movsim](https://github.com/movsim/traffic-simulation-de), or Open Traffic Sim [@opentrafficsim], or City Flow [@CityFlow]. On the other side of the spectrum, there are simulators focusing on very detailed single-vehicle models, including their sensors, such as CARLA [@Carla].
 
 # Outlook and use cases