[Minor] Docs: fix small typos and broken links

docs/source/development/softwaredevelopment.rst

@@ -15,7 +15,7 @@ Software Design and requirements
 The most important piece of AequilibraE's backend is, without a doubt, `numpy <http://numpy.org>`__.
 
 Whenever vectorization is not possible through the use of NumPy functions, compiled code is developed in order to
-accelerate computation. All compiled code is written in `Cython <www.cython.org>`_.
+accelerate computation. All compiled code is written in `Cython <https://cython.org/>`_.
 
 We have not yet found an ideal source of recommendations for developing AequilibraE, but a good initial take can be
 found in `this article. <http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001745>`__

docs/source/getting_started.rst

@@ -102,7 +102,7 @@ On MacOS one can use brew as per
 Hardware requirements
 ---------------------
 
-AequilibraE's requirements depend heavily of the size of the model you are using
+AequilibraE's requirements depend heavily on the size of the model you are using
 for computation. The most important
 things to keep an eye on are:
 

docs/source/history.rst

@@ -9,18 +9,18 @@ The very early days
 It all started when `Pedro <https://www.xl-optim.com/>`_ was a student at `UCI-ITS <https://www.its.uci.edu/>`_  and
 needed low level access to outputs of standard algorithms used in transportation modeling (e.g. path files from traffic
 assignment) and had that denied by the maker of the commercial software he normally used. There, the
-`first scratch of a traffic assignment procedure <www.xl-optim.com/python-traffic-assignment>`_ was born.
+`first scratch of a traffic assignment procedure <https://www.xl-optim.com/python-traffic-assignment>`_ was born.
 After that, there were a couple of scripts developed to implement synthetic gravity models (calibration and application)
-that were develop for a government think-tank in Brazil `IPEA <https://www.ipea.gov.br/>`_.
+that were developed for a government think-tank in Brazil `IPEA <https://www.ipea.gov.br/>`_.
 Around the same time, another student needed a piece of code that transformed a GIS link layer into a proper graph,
 where each link would become the connection between two nodes.
 So there were three fundamental pieces that would come to be part of AequilibraE.
 
 The first take on a release software
 ------------------------------------
 
-Having all those algorithms at hand, it made sense combining them into something more people could use, and by them it
-seemed that QGIS was the way to go, so I developed the
+Having all those algorithms at hand, it made sense combining them into something more people could use, and by then it
+seemed that QGIS was the way to go, so Pedro developed the
 `very first version of AequilibraE <http://www.xl-optim.com/introducing_aequilibrae>`_.
 
 It was buggy as hell and there was very little, if any, software engineering built into it, but it put Aequilibrae on
@@ -30,7 +30,7 @@ The first reasonable version
 ----------------------------
 
 The first important thing Pedro noticed after releasing AequilibraE was that the code was written in procedural style,
-even though it would make a lot more sense doing it in a Object-Oriented fashion, which let me down the path of
+even though it would make a lot more sense doing it in an Object-Oriented fashion, which let him down the path of
 creating the objects (graph, assignment results, matrix) that the software still relies on and were the foundation
 blocks of the proper API that is in the making. That
 `version was released in 2016 <http://www.xl-optim.com/new-version-of-aequilibrae>`_
@@ -43,11 +43,11 @@ A few distinct improvements deserve to be highlighted.
 * The separation of the GUI and the Python library in `two repositories <http://www.xl-optim.com/separating-the-women-from-the-girls>`_
 * Introduction of Unit Tests and automatic testing using `Travis (replaced with GitHub Actions) <https://travis-ci.org/AequilibraE/aequilibrae>`_
 * Development of proper documentation, more software engineering with style-checking (Flake8 and Black)
-* Re-development of the the most basic algorithms (path-finding, IPF, etc) with highly parallelized code optimized for the transportation use case
+* Re-development of the most basic algorithms (path-finding, IPF, etc) with highly parallelized code optimized for the transportation use case
 
 Release of AequilibraE 1.0
 --------------------------
 
 On its 9th anniversary (16/12/2023), AequilibraE saw the release of its landmark
-`version 1.0 <https://www.outerloop.io/blog20231216_aequilibrae1.0>`_
+`version 1.0 <https://www.outerloop.io/blog/20231216_aequilibrae1.0/>`_
 which is the first to include a suite of Public Transport tools, making AequilibraE a fully-featured software.

docs/source/modeling_with_aequilibrae/modeling_concepts.rst

@@ -9,7 +9,7 @@ workflows to AequilibraE.
 
 Although modeling with AequilibraE should feel somewhat familiar to seasoned modelers, especially
 those used to programming, the mechanics of some of AequilibraE procedures might be foreign to
-some of users, so this section of the documentation will include discussions of the mechanics
+some users, so this section of the documentation will include discussions of the mechanics
 of some of these procedures and some light discussion on its motivation.
 
 Further, many AequilibraE users are new to the *craft*, so we have elected to start

docs/source/modeling_with_aequilibrae/project.rst

@@ -18,7 +18,7 @@ projects, such as `SQLite <https://sqlite.org/index.html>`_,
 <https://numpy.org/>`_, as well as reasonably new industry standards such as the
 `Open-Matrix format <https://github.com/osPlanning/omx>`_.
 
-impressive performance, portability, self containment and open-source character
+Impressive performance, portability, self containment and open-source character
 of these pieces of software, along with their large user base and wide
 industry support make them solid options to be AequilibraE's data backend.
 
@@ -62,7 +62,7 @@ not need to worry about its existence until it is automatically created.
 
 The **demand_database.sqlite** is envisioned to hold all the demand-related
 information, and it is not yet structured within the AequilibraE code, as there
-is no pre-defined demand model available for use with AequilibraE. This detabase
+is no pre-defined demand model available for use with AequilibraE. This database
 is not created with the model, but we recommend using this concept on
 your demand models.
 
@@ -76,7 +76,7 @@ Package components: A conceptual view
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 As all the components of an AequilibraE model based on open-source software and
-open-data standards, modeling with AequilibraE is a little different than
+open-data standards, modeling with AequilibraE is a little different from
 modeling with commercial packages, as the user can read and manipulate model
 components outside the software modeling environments (Python and QGIS).
 

docs/source/modeling_with_aequilibrae/public_transport.rst

@@ -8,7 +8,7 @@ into its database. The Transit module has been updated in version 0.9.0. More de
 the **public_transport.sqlite** are discussed on a nearly *per-table* basis below, and 
 we recommend understanding the role of each table before setting an AequilibraE model 
 you intend to use. If you don't know much about GTFS, we strongly encourage you to take
-a look at the documentation provived by `Google <https://developers.google.com/transit/gtfs>`_.
+a look at the documentation provided by `Google <https://developers.google.com/transit/gtfs>`_.
 
 .. toctree::
     :maxdepth: 1

docs/source/support.rst

@@ -10,7 +10,7 @@ Free support
 ------------
 
 There are two mechanisms to obtain free support that also allow you to check whether your question was
-already asked in the past, with the the first option being the most often used as of December/2023:
+already asked in the past, with the first option being the most often used as of December/2023:
 
 1. Joining the `AequilibraE Google Group <https://groups.google.com/forum/#!forum/aequilibrae>`_
    and sending your question there.

docs/source/validation_benchmarking/ipf_performance.rst

@@ -4,7 +4,7 @@ IPF Performance
 It is quite common to have zones with different growth rates. To improve obtaining
 a trip matrix, which satisfies both trip-end constraints, we can use iterative methods,
 such as the iterative proportional fitting (IPF). In this section, we compare the 
-runtime of AquilibraE's current implementation of IPF, 
+runtime of AequilibraE's current implementation of IPF, 
 with a general IPF algorithm, available `here <https://github.com/joshchea/python-tdm/blob/master/scripts/CalcDistribution.py>`_.
 
 The figure below compares the :ref:`AequilibraE's IPF runtime` with one core with the benchmark Python
@@ -19,7 +19,7 @@ core to speed up the fitting process.
     :align: center
     :alt: AequilibraE's IPF runtime
 
-As IPF is an embarassingly-parallel workload, we have implemented our version in Cython, taking full advantage
+As IPF is an embarrassingly-parallel workload, we have implemented our version in Cython, taking full advantage
 of parallelization and observing the impact of array orientation in memory. AequilibraE's
 IPF allows the user to choose how many cores are used for IPF in order to speed up the fitting process, which
 is extremely useful when handling models with lots of traffic zones.
@@ -31,7 +31,7 @@ speeding up the runtime in barely five times using five cores.
     :align: center
     :alt: number of cores used in IPF
 
-These tests were ran on a Threadripper 3970x (released in 2019) workstation with 32 cores (64 threads) @ 3.7 GHz
+These tests were run on a Threadripper 3970x (released in 2019) workstation with 32 cores (64 threads) @ 3.7 GHz
 and 256 Gb of RAM. With 32 cores in use, performing IPF took 0.105s on a 10,000 zones matrix,
 and 0.224 seconds on a 15,000 matrix. The code is provided below for convenience
 

docs/source/validation_benchmarking/traffic_assignment.rst

@@ -3,13 +3,13 @@
 Traffic Assignment
 ==================
 
-Similar to other complex algorthms that handle a large amount of data through
+Similar to other complex algorithms that handle a large amount of data through
 complex computations, traffic assignment procedures can always be subject to at
 least one very reasonable question: Are the results right?
 
 For this reason, we have used all equilibrium traffic assignment algorithms
 available in AequilibraE to solve standard instances used in academia for
-comparing algorithm results, some of which have are available with highly
+comparing algorithm results, some of which achieve
 converged solutions (~1e-14). Instances can be downloaded `here <https://github.com/bstabler/TransportationNetworks/>`_.
 
 Sioux Falls
@@ -170,7 +170,7 @@ Regional), as that instance has a comparable size to real-world models.
     :alt: Algorithm convergence comparison
 |
 
-Not surprinsingly, one can see that Frank-Wolfe far outperforms the Method of
+Not surprisingly, one can see that Frank-Wolfe far outperforms the Method of
 Successive Averages for a number of iterations larger than 25, and is capable of
 reaching 1.0e-04 just after 800 iterations, while MSA is still at 3.5e-4 even
 after 1,000 iterations.