---
title: 'stplanr: A Package for Transport Planning'
author:
  - name: Robin Lovelace
    affiliation: University of Leeds
    address:
    - 34-40 University Road
    - LS2 9JT, UK
    email:  r.lovelace@leeds.ac.uk
  - name: Richard Ellison
    affiliation: University of Sydney
    address:
    - 378 Abercrombie Street
    - Darlington, NSW 2008, Australia
    email:  richard.ellison@sydney.edu.au
abstract: >
  Tools for transport planning should be flexible, scalable and transparent. The **stplanr** package demonstrates and provides a home for such tools, with an emphasis on spatial transport data and non-motorized modes. **stplanr** facilitates common transport planning tasks including: downloading and cleaning transport datasets; creating geographic 'desire lines from origin-destination (OD) data; route assignment, via the `SpatialLinesNetwork` class and interfaces to routing services such as CycleStreets.net; calculation of route segment attributes such as bearing and aggregate flow; and `travel watershed' analysis. This paper demonstrates this functionality using reproducible examples on real transport datasets. More broadly, the experience shows open source software can form the basis of a reproducible transport planning workflow. **stplanr**, alongside other packages and open source projects, could provide a more transparent and democratically accountable alternative to the current approach which is heavily reliant on proprietary and technically and financially inaccessible software.
bibliography:
  - references.bib
  - stplanr-citation.bib
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{stplanr: A Package for Transport Planning}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo=FALSE}
knitr::opts_chunk$set(fig.width = 7, fig.height = 5, eval = FALSE)
```

## Note

This paper has now been peer reviewed and published by the R Journal.
Please see the published version at [journal.r-project.org](https://journal.r-project.org/archive/2018/RJ-2018-053/index.html) and cite it as @lovelace_stplanr_2018.

# Introduction

Transport planning can broadly be defined as the process of designing
and evaluating transport interventions [@willumsen_modelling_2011]
usually with the ultimate aim of improving transport systems from
economic, social and environmental perspectives. This inevitably
involves a degree of subjective judgment and intuition. With the
proliferation of new transport datasets — and the increasing
availability of hardware and software to make sense of them — there is
great potential for the discipline to become more evidence-based and
scientific [@balmer_matsim-t:_2009]. Transport planners have always
undertaken a wide range computational activities
[@boyce_forecasting_2015], but with the digital revolution the demands
have grown beyond the capabilities of a single, monolithic product. The
diversity of tasks , and need for democratic accountability in public
decision making, suggests that future-proof transport planning software
should be:


-   flexible, able to handle a wide range of data formats

-   scalable, able to work at multiple geographic levels from single
    streets to large cities and regions

-   robust and reliable, tested on a range of datasets and able to work
    ’out of the box’ in a range of real-world projects

-   open source and reproducible, ensuring transparency and encouraging
    citizen science

This paper sets out to demonstrate that open source software with a
command-line interface (CLI) can provide a foundation for transport
planning software that meets each of these criteria. R provides a strong
basis for progress in this direction because it already contains
functionality used in common transport planning workflows. , and greatly
improved R’s spatial abilities [@bivand_applied_2013], work that is
being consolidated and extended in the recent package.

Building on these foundations a number of spatial packages have been
developed for applied domains including: disease mapping and modelling,
with packages such as and
[@kim_spatialepi:_2016; @brown_diseasemapping:_2016]; spatial ecology,
with the **adehabitat** family of packages [@calenge_package_2006]; and
visualisation, with packages such as **SpatialEpi**, **diseasemapping** and @RJ-2016-005. However,
there has been little prior work to develop R functionality designed
specifically for transport planning, with the notable exceptions of
[TravelR](https://r-forge.r-project.org/projects/travelr/) (a package on
R-Forge last updated in 2012) and
[gtfsr](https://github.com/ropensci/gtfsr) (a package for handling
General Transit Feed Specification (GTFS) data).

The purpose of **stplanr** is to provide a toolbox rather than a
specific solution for transport planning, with an emphasis on spatial
data and active modes. This emphasis is timely given the recent emphasis
on sustainability [@banister_sustainable_2008] and ‘Big Data’
[@zheng_big_2016] in the wider field of transport planning. A major
motivation was the lack of R packages, and open source software in
general, for transport applications. This may be surprising given the
uqiquity of transport problems;^[
Many people can think of things that could be improved on their local transport networks, especially for walking, cycling and wheel-chairs.
But most lack the evidence to communicate the issues, and potential solutions, to others.
]
R’s proficiency at handling spatial,
temporal and travel survey data that describe transport systems; and the
growing popularity of R in applied domains
[@jalal_overview_2017; @moore_why_2017]. Another motivation is the
growth in open access datasets: the main purpose of early versions of
the package was to process open origin-destination data
[@lovelace_propensity_2017].

R is already used in transport applications, as illustrated by recent
research that applies packages from other domains to transport problems.
For instance, @efthymiou_use_2012 use R to
analyse the data collected from an online survey focused on car-sharing,
bicycle-sharing and electric vehicles. @efthymiou_use_2012
also used R to collect and analyse
transport-related data from Twitter using packages including , and .
These packages were used to download, parse and plot the Twitter data
using a method that can be repeated and the results reproduced or
updated. More general statistical analyses have also been conducted on
transport-related datasets using packages including and
[@diana_studying_2012; @cerin_walking_2013]. Despite the rising use of R
for transport research, there has yet been to be a package for transport
planning.

The design of the R language, with its emphasis on flexibility, data
processing and statistical modelling, suggests it can provide a powerful
environment for transport planning research. There are many quantitative
methods in transport planning, many of which fit into the classic ‘four
stage’ transport model which involves the following steps
[@willumsen_modelling_2011]: (1) trip *generation* to estimate trip
freqency from origins; (2) *distribution* of trips to destinations; (3)
*modal split* of trips between walking, cycling, buses etc.; (4)
*assignment* of trips to the transport route network. To this we would
like to add two more stages for the big data age: (0) data processing
and exploration; and (5) validation. This sequence is not the only way
of transport modelling and some have argued that its dominance has
reduced innovation. However it is certainly a common approach and
provides a useful schema for classifying the kinds of task that
**stplanr** can tackle:

-   Accessing and processing of data on transport infrastructure and
    behaviour (stage 0)

-   Analysis and visualisation of the transport network (0)

-   Analysis of origin-destination (OD) data and the visualisation of
    resulting ‘desire lines’

-   The allocation of desire lines to roads and other guideways via
    routing services

-   The aggregation of routes to estimate total levels of flow on
    segments throughout the transport network

-   Development of models to estimate transport behaviour currently and
    under various scenarios of change

-   The calculation of ‘catchment areas’ affected by transport
    infrastructure

The automation of such tasks can assist researchers and practitioners to
create evidence for decision making. If the data processing and analysis
stages are fast and painless, more time can be dedicated to
visualisation and decision making. This should allow researchers to
focus on problems, rather than on clunky graphical user interfaces
(GUIs), and ad-hoc scripts that could be generalised. Furthermore, if
the process can be made reproducible and accessible (e.g. via online
visualisation packages such as **shiny**), this could help transport planning
move away from reliance on ‘black boxes’ [@waddell_urbansim:_2002] and
empower citizens to challenge decisions made by transport planning
authorities based on the evidence [@hollander_transport_2016]. There are
many advantages of using a scriptable, interactive and open source
language such as R for transport planning. Such an approach enables:
reproducible research; the automation and sharing of code between
researchers; reduced barriers to innovation as anyone can create new
features for the benefit of all planners; easier interaction with non
domain experts (who will lack dedicated software); and integration with
other software systems, as illustrated by the use of to generate
JavaScript for sharing interactive maps for transport planning, as used
in the publicly accessible Propensity to Cycle Tool
[@lovelace_propensity_2017]. Furthermore, R has a strong user community
which can support newcomers (*stplanr* was peer reviewed thanks to the
community surrounding ROpenSci). The advantages of using R specifically
to develop the functionality described in this paper are that it has
excellent geo-statistical capabilities [@pebesma_software_2015],
visualisation packages (e.g. **tmap**, **ggplot2**), support for logit models (which are
useful for modelling modal shift), and support for the many formats that
transport datasets are stored in (e.g. via the **haven**  and **rio** packages).

# Package structure and functionality

The package can be installed and loaded in the usual way (see the package's
[README](https://github.com/ropensci/stplanr) for dependencies and access to development versions):

```{r, eval=FALSE}
install.packages("stplanr")
```


```{r}
library(stplanr)
```

As illustrated by the message emitted when **stplanr** is loaded, it depends on \CRANpkg{sp}. This means that the spatial data classes commonly used in the package will work with generic R functions such as `summary`, `aggregate` and, as illustrated in the figures below, `plot` \citep{bivand_applied_2013}.

## Core functions and classes

The package's core functions are structured around 3 common types of spatial transport data:

- Origin-destination (OD) data, which report the number of people travelling between origin-destination pairs. This type of data is not explicitly spatial (OD datasets are usually represented as data frames) but represents movement over space between points in geographical space. An example is provided in the `flow` dataset.
- Line data, one dimensional linear features on the surface of the Earth. These are typically stored as a `SpatialLinesDataFrame`.
- Route data are special types of lines which have been allocated to the transport network. Routes typically result from the allocation of a straight 'desire line' allocated to the route network with a `route_` function. Route network represent many overlapping routes. All are typically stored as `SpatialLinesDataFrame`.

For ease of use, functions focussed on each data type have been developed with names prefixed with `od_`, `line_` and `route_` respectively. A selection of these is presented in Table 1. Additional 'core functions' could be developed, such as those prefixed with `rn_` (for working with route network data) and `g_` functions for geographic operations such as buffer creation on lat/lon projected data (this function is currently named `buff_geo`). We plan to elicit feedback on such changes before implementing them.

```{r, echo=FALSE, results='asis', message=FALSE}
# stplanr_funs = ls("package:stplanr")
# sel_core = grep(pattern = "od_|^line_|route_", x = stplanr_funs)
# core_funs = stplanr_funs[sel_core]
# args(name = core_funs[1])
fun_table <- read.csv("fun_table.csv", stringsAsFactors = FALSE, check.names = FALSE)
knitr::kable(fun_table, caption = "Selection of functions for working with or generating OD, line and route data types.")
```

With a tip of the hat to the concept of type stability (e.g. as implemented in \CRANpkg{dplyr}), we also plan to make the core functions of **stplanr** more type-stable in future releases.  Core functions, which begin with the prefixes listed above, could follow \CRANpkg{dplyr}'s lead and return only objects with the same class as that of the input. However there are limitations to this approach: it will break existing functionality and mean that output objects have a larger size than necessary (`line_bearing`, for example, does not need to duplicate the spatial data contained in its input). Instead, we plan to continue to name functions around the type of *input* data they take, but are open minded about function input-output data class conventions, especially in the context of the new class system implemented in \CRANpkg{sf}.

A class system has not been developed for each data type (this option is discussed in the final section). The most common data types used in **stplanr** are assumed to be data frames and spatial datasets.

Transport datasets are very diverse. There are therefore many other functions which have more ad-hock names. Rather attempt a systematic description of each of **stplanr**'s functions (which can be gleaned from the online manual) it is more illuminating to see how they work together, as part of a transport planning workflow. As with most workflows, this begins with data access and ends with visualisation.

## Accessing and processing transport data

Gaining access to data is often the first stage in transport research. This is often a long and protracted process which is thankfully becoming easier thanks to the 'open data' movement and packages such as **tigris** for making data access from within R easier \citep{walker_tigris:_2016}.

**stplanr** provides a variety of different functions that facilitate importing common data formats used for transport analysis into R.
Although transport analysis generally requires some transport-specific datasets, it also typically relies heavily on common sources of data including census data.
This being the case, **stplanr** also includes functions that may be useful to those not involved in transport research.
This includes the `read_table_builder` function for importing data from the Australian Bureau of Statistics (ABS) and the UK's Stats19 road traffic casualty dataset. A brief example of the latter is demonstrated below, which begins with downloading the data (warning this downloads ~100 MB of data):

```{r, eval=FALSE}
dl_stats19() # download and extract stats19 road traffic casualty data
```

```
#> [1] "Data saved at: /tmp/RtmpppF3E2/Accidents0514.csv"
#> [2] "Data saved at: /tmp/RtmpppF3E2/Casualties0514.csv"
#> [3] "Data saved at: /tmp/RtmpppF3E2/Vehicles0514.csv"  
```

Once the data has been saved in the default directory, determined by `tempdir()`, it can be read-in and cleaned with the `read_stats19_` functions (note these call `format_stats19_` functions internally to clean the datasets and add correct labels to the variables):

```{r, eval=FALSE}
ac <- read_stats19_ac()
ca <- read_stats19_ca()
ve <- read_stats19_ve()
```

The resulting datasets (representing accident, casualty and vehicle level data, respectively) can be merged and made geographic, as illustrated below:

```{r, eval=FALSE}
library(dplyr)
ca_ac <- inner_join(ca, ac)
ca_cycle <- ca_ac %>%
  filter(Casualty_Severity == "Fatal" & !is.na(Latitude)) %>%
  select(Age = Age_of_Casualty, Mode = Casualty_Type, Longitude, Latitude)
ca_sp <- SpatialPointsDataFrame(coords = ca_cycle[3:4], data = ca_cycle[1:2])
```

Now that this casualty data has been cleaned, subsetted (to only include serious cycle crashes) and converted into a spatial class system, we can analyse them using geographical datasets of the type commonly used by **stplanr**.
The following code, for example, geographically subsets the dataset to include only crashes that occured within the bounding box of a [route network dataset](https://github.com/ropensci/stplanr/blob/master/data/route_network.rda?raw=true) provided by **stplanr** (from version 0.1.7 and beyond) using the function `bb2poly`, which converts a spatial dataset into a box, represented as a rectangular `SpatialPolygonsDataFrame`:

```{r, eval=FALSE}
data("route_network") # devtools::install_github("ropensci/splanr")version 0.1.7
proj4string(ca_sp) <- proj4string(route_network)
bb <- bb2poly(route_network)
proj4string(bb) <- proj4string(route_network)
ca_local <- ca_sp[bb, ]
```

The above code chunk shows the importance of understanding geographical data when working with transport data.
It is only by converting the casualty data into a spatial data class, and adding a coordinate reference system (CRS), that transport planners and researchers can link this important dataset back to the route network.
We can now perform GIS operations on the results.
The next code chunk, for example, finds all the fatalities that took place within 100 m of the route network, using the function `buff_geo`:

```{r, echo=FALSE}
bb <- bb2poly(route_network)
load("reqfiles.RData")
```

```{r, message=FALSE}
rnet_buff_100 <- geo_buffer(route_network, width = 100)
ca_buff <- ca_local[rnet_buff_100, ]
```

These can be visualised using base R graphics, extended by \CRANpkg{sp}, as illustrated in Figure \ref{fig:fats}. This provides a good start for analysis but for publication-quality plots and interactive plots, designed for public engagement, we recommend using dedicated visualisation packages that work with spatial data such as \CRANpkg{tmap}.

```{r fats, fig.cap="Road traffic fatalities in the study area downloaded with with stplanr (crosses). Deaths that happened within 100 m of the route network are represented by circles.", out.width="50%", fig.align="center"}
plot(bb, lty = 4)
plot(rnet_buff_100, col = "grey", add = TRUE)
points(ca_local, pch = 4)
points(ca_buff, cex = 3)
```

## Creating geographic desire lines

Perhaps the most common type of aggregate-level transport information is origin-destination ('OD') data.
This can be presented either as a matrix or (more commonly) a long table
of OD pairs. An example of this type of raw data is provided
below (see `?flow` to see how this dataset was created).  

```{r}
data("flow", package = "stplanr")
head(flow[c(1:3, 12)])
```

Although the flow data displayed above describes movement over
geographical space, it contains no explicitly geographical
information. Instead, the coordinates of the origins and
destinations are linked to a separate geographical dataset
which also must be loaded to analyse the flows. This is a
common problem solved by the function `od2line`.
The geographical data is a set of points representing centroids
of the origin and destinations, saved as a
`SpatialPointsDataFrame`. Geographical data in R is best
represented as such `Spatial*` objects, which use the
`S4` object engine. This explains the close integration of
**stplanr** with R's spatial packages, especially **sp**, which
defines the `S4` spatial object system.

```{r}
data("cents", package = "stplanr")
as.data.frame(cents[1:3, -c(3, 4)])
```

We use `od2line` to combine `flow` and `cents`, to join
the former to the latter. We will visualise the
`l` object created below in the next section. 

```{r, warning=FALSE}
l <- od2line(flow = flow, zones = cents)
```

The data is now in a form that is much easier to analyse. We can plot the
data with the command `plot(l)`, which was not possible before. Because the
`SpatialLinesDataFrame` object also contains data per line, it also helps
with visualisation of the flows, as illustrated in Figure \ref{fig:lines_routes}.

## Allocating flows to the transport network

A common problem faced by transport researchers is network
allocation: converting the 'as the crow flies' lines illustrated in the
figure above into routes. These are the complex, winding
paths that people and
animals make to avoid obstacles such as buildings and to make the journey
faster and more efficient (e.g. by following the route network).

This is difficult (and was until recently near impossible using free software)
because of the size and complexity of transport networks, the complexity
of realistic routing algorithms and need for context-specificity in the routing
engine. Inexperienced cyclists, for example, would take a very different route
than a heavy goods vehicle. **stplanr** tackles this issue by using 3rd party APIs to provide
route-allocation. 

Route allocation is undertaken by \code{route\_}
functions such as \code{route\_cyclestreets}
and \linebreak \code{route\_graphhopper}.
These allocate a single OD pair, represented as a text string to be 'geo-coded', a pair of of coordinates, or two `SpatialPoints` objects, representing origins and destinations.
This is illustrated below with `route_cyclestreet`, which uses the
[CycleStreets.net API](https://www.cyclestreets.net/api/), a routing service "by cyclists for cyclists" that offers a range route strategies (primarily 'fastest', 'quietest' and 'balanced') that are based on a
detailed analysis of cyclist
wayfinding:^[An
API key is needed for this function to work. This can be requested (or purchased for large scale routing) from [cyclestreets.net/api/apply](https://www.cyclestreets.net/api/apply/). See `?route_cyclestreet` for details.
Thanks to Martin Lucas-Smith and Simon Nuttall for making this possible.]

```{r, eval=FALSE}
route_bl <- route_cyclestreets(from = "Bradford", to = "Leeds")
route_c1_c2 <- route_cyclestreets(cents[1, ], cents[2, ])
```

The raw output from routing APIs is usually provided as a JSON or GeoJSON text string. By default, `route_cyclestreet` saves a number of key variables (including length, time, hilliness and busyness variables generated by CycleStreets.net) from the attribute data provided by the API. If the user wants to save the raw output, the `save_raw` argument can be used:

```{r, eval=FALSE}
route_bl_raw <- route_cyclestreets(from = "Bradford", to = "Leeds", save_raw = TRUE)
```

Additional arguments taken by the `route_` functions depend on the routing function in question. By changing the `plan` argument of `route_cyclestreet` to `fastest`, `quietest` or `balanced`, for example, routes favouring speed, quietness or a balance between speed and quietness will be saved, respectively.

To automate the creation of route-allocated lines over many desire lines, the `line2route` function loops over each line, wrapping any `route_` function as an input. The output is a `SpatialLinesDataFrame` with the same number of dimensions as the input dataset (see the right panel in Figure \ref{fig:lines_routes}).

```
routes_fast <- line2route(l = l, route_fun = route_cyclestreet)
```

The result of this 'batch routing' exercise is illustrated in Figure \ref{fig:lines_routes}. The red lines in the left hand panel are very different from the hypothetical straight 'desire lines' often used in transport research, highlighting the importance of this route-allocation functionality. 

```{r lines_routes, out.width='50%', fig.cap='Visualisation of travel desire lines, with width proportional to number of trips between origin and destination (black) and routes allocated to network  (red) in the left-hand panel. The right hand panel shows the route network dataset generated by overline().', fig.show='hold'}
plot(route_network, lwd = 0)
plot(l, lwd = l$All / 10, add = TRUE)
lines(routes_fast, col = "red")
routes_fast$All <- l$All
rnet <- overline(routes_fast, "All", fun = sum)
rnet$flow <- rnet$All / mean(rnet$All) * 3
plot(rnet, lwd = rnet$flow / mean(rnet$flow))
```

To estimate the amount of capacity needed at each segment on the transport network, the `overline` function demonstrated above, is used to divide line geometries into unique segments and aggregate the overlapping values.
The results, illustrated in the right-hand panel of Figure \ref{fig:lines_routes}, can be used to estimate where there is most need to improve the transport network, for example informing the decision of where to build new bicycle paths.

Limitations with the `route_cyclestreet` routing API include its specificity, to one mode (cycling) and a single region (the UK and part of Europe). To overcome these limitations, additional routing APIs were added with the functions `route_graphhopper`, `route_transportapi_public` and `viaroute`. These interface to Graphhopper, TransportAPI and the Open Source Routing Machine (OSRM) routing services, respectively. The great advantage of OSRM is that it allows you to run your own routing services on a local server, greatly increasing the rate of route generation.

A short example of finding the route by car and bike between New York and Oaxaca demonstrates how `route_graphhopper` can collect geographical and other data on routes by various modes, anywhere in the world. The output, shown in Table \ref{tab:xtnyoa}, shows that the function also saves time, distance and (for bike trips)
vertical distance climbed for the trips.

```{r, eval=FALSE, out.width='\\textwidth'}
ny2oaxaca1 <- route_graphhopper("New York", "Oaxaca", vehicle = "bike")
ny2oaxaca2 <- route_graphhopper("New York", "Oaxaca", vehicle = "car")
rbind(ny2oaxaca1@data, ny2oaxaca2@data)
```

```{r, eval=FALSE, echo=FALSE}
nytab <- rbind(ny2oaxaca1@data, ny2oaxaca2@data)
nytab <- cbind(Mode = c("Cycle", "Car"), nytab)
xtnyoa <- xtable(nytab, caption = "Attribute data from the route\\_graphhopper function, from New York to Oaxaca, by cycle and car.", label = "tab:xtnyoa")
print.xtable(xtnyoa, include.rownames = FALSE)
plot(ny2oaxaca1)
plot(ny2oaxaca2, add = TRUE, col = "red")

ny2oaxaca1@data
ny2oaxaca2@data
```

|     time|    dist| change_elev|
|--------:|-------:|-----------:|
| 17522.73| 4885663|    87388.13|
|  2759.89| 4754772|          NA|

<!-- ## Calculating geographic attributes of transport routes -->

## Modelling travel catchment areas

Accessibility to transport services is a particularly important topic when considering public transport or active travel because of the frequent steep reduction in use as distances to access services (or infrastructure) increase.
As a result, the planning for transport services and infrastructure frequently focuses on several measures of accessibility including distance, but also travel times and frequencies and weighted by population.
The functions in **stplanr** are intended to provide a method of estimating these accessibility measures as well as calculating the population that can access specific services (i.e., estimating the catchment area).

Catchment areas in particular are a widely used measure of accessibility that attempts to both quantify the likely target group for a particular service, and visualise the geographic area that is covered by the service.
For instance, passengers are often said to be willing to walk up to 400 metres to a bus stop, or 800 metres to a railway station \citep{el-geneidy_new_2014}.
Although these distances may appear relatively arbitrary and have been found to underestimate the true catchment area of bus stops and railway stations  \citep{el-geneidy_new_2014,daniels_explaining_2013} they nonetheless represent a good, albeit somewhat conservative, starting point from which catchment areas can be determined.

In many cases, catchment areas are calculated on the basis of straight-line (or "as the crow flies") distances.
This is a simplistic, but relatively appealing approach because it requires little additional data and is straight-forward to understand.
**stplanr** provides functionality that calculates catchment areas using straight-line distances with the `calc_catchment` function.
This function takes a `SpatialPolygonsDataFrame` that contains the population (or other) data, typically from a census, and a `Spatial*` layer that contains the geometry of the transport facility.
These two layers are overlayed to calculate statistics for the desired catchments including proportioning polygons to account for the proportion located within the catchment area.

To illustrate how catchment areas can be calculated, **stplanr** contains some sample datasets stored in ESRI Shapefile format (a commonly used format for distributing GIS layers) that can together be used to calculate sample catchment areas.
One of these datasets (`smallsa1`) contains population data for Statistical Area 1 (SA1) zones in Sydney, Australia.
The second contains hypothetical cycleways aligned to streets in Sydney. The code below unzips the datasets and reads in the shapefiles.

```{r loadshapefiles, results='hide',message='hide'}
data_dir <- system.file("extdata", package = "stplanr")
unzip(file.path(data_dir, "smallsa1.zip"))
unzip(file.path(data_dir, "testcycleway.zip"))
sa1income <- as(sf::read_sf("smallsa1.shp"), "Spatial")
testcycleway <- as(sf::read_sf("testcycleway.shp"), "Spatial")
# Remove unzipped files
file.remove(list.files(pattern = "^(smallsa1|testcycleway).*"))
```

Calculating the catchment area is straightforward and in addition to specifying the required datasets, only a vector containing column names to calculate statistics and a distance is required.
Since proportioning the areas assumes projected data, unprojected data are automatically projected to either a common projection (if one is already projected) or a specified projection.
It should be emphasised that the choice of projection is important and has an effect on the results meaning setting a local projection is recommended to achieve the most accurate results.

```{r calccatchment, results='hide'}
catch800m <- calc_catchment(
  polygonlayer = sa1income,
  targetlayer = testcycleway,
  calccols = c("Total"),
  distance = 800,
  projection = "austalbers",
  dissolve = TRUE
)
```

By looking at the data.frame associated with the SpatialPolygonsDataFrame that is returned from the `calc_catchment` function, the total population within the catchment area can be seen to be nearly 40,000 people.
The catchment area can also be plotted as with any other `Spatial*` object using the `plot` function using the code below with the result shown in Figure \ref{fig:catchmentplot}.

```{r catchmentplot, fig.cap='An 800 metre catchment area (red) associated with a cycle path (green) using straight-line distance in Sydney.'}
plot(sa1income, col = "light grey")
plot(catch800m, col = rgb(1, 0, 0, 0.5), add = TRUE)
plot(testcycleway, col = "green", add = TRUE)
```

This simplistic catchment area is useful when the straight-line distance is a reasonable approximation of the route taken to walk (or cycle) to a transport facility.
However, this is often not the case.
The catchment area in Figure \ref{fig:catchmentplot} initially appears reasonable but the red-shaded catchment area includes an area that requires travelling around a bay to access from the (green-coloured) cycleway.
To allow for more realistic catchment areas for most situations, **stplanr** provides the `calc_network_catchment` function that uses the same principle as `calc_catchment` but also takes into account the transport network.

To use `calc_network_catchment`, a transport network needs to be prepared that can be used in conjunction with the previous datasets.
Preparation of the dataset involves using the `SpatialLinesNetwork` function to create a network from a `SpatialLinesDataFrame`.
This function combines a `SpatialLinesDataFrame` with a graph network (using the \CRANpkg{igraph} package) to provide basic routing functionality.
The network is used to calculate the shortest actual paths within the specific catchment distance.
This process involves the following code:

```{r, echo=TRUE, message=FALSE, warning=FALSE, results='hide'}
unzip(file.path(data_dir, "sydroads.zip"))
sydroads <- as(sf::read_sf(".", "roads"), "Spatial")
file.remove(list.files(pattern = "^(roads).*"))
sydnetwork <- SpatialLinesNetwork(sydroads)
```

The network catchment is then calculated using a similar method as with `calc_catchment` but with a few minor changes.
Specifically these are including the `SpatialLinesNetwork`, and using the `maximpedance` parameter to define the distance, with distance being the additional distance from the network.
In contrast to the distance parameter that is based on the straight-line distance in both the `calc_catchment` and `calc_network_catchment` functions, the `maximpedance` parameter is the maximum value in the units of the network's weight attribute.
In practice this is generally distance in metres but can also be travel times, risk or other measures.

```{r, warning=FALSE}
netcatch800m <- calc_network_catchment(
  sln = sydnetwork,
  polygonlayer = sa1income,
  targetlayer = testcycleway,
  calccols = c("Total"),
  maximpedance = 800,
  distance = 100,
  projection = "austalbers"
)
```

Once calculated, the network catchment area can be used just as the straight-line network catchment.
This includes extracting the catchment population of 128,000 and plotting the original catchment area together with the original area with the results shown in Figure \ref{fig:netcatchplot}:

```{r netcatchplot, fig.cap='A 800 metre network catchment are (blue) compared with a catchment area based on Euclidean distance (red) associated with a cycle path (green).'}
plot(sa1income, col = "light grey")
plot(catch800m, col = rgb(1, 0, 0, 0.5), add = TRUE)
plot(netcatch800m, col = rgb(0, 0, 1, 0.5), add = TRUE)
plot(testcycleway, col = "green", add = TRUE)
```

# Modelling and visualisation

## Modelling mode choice

Route-allocated lines allow estimation of *route distance* and 
*cirquity* (route distance divided by Euclidean distance).
These variables can help model the rate of flow between origins and
destination, as illustrated in the left-hand panel of Figure \ref{fig:euclidfastest}.
The code below demonstrates
how objects generated by **stplanr** can be used to undertake such analysis, with the `line_length` function used to find the distance, in meters, of lat/lon data.

```
l$d_euclidean <- line_length(l)
l$d_rf <- routes_fast@data$length
plot(l$d_euclidean, l$d_rf,
  xlab = "Euclidean distance", ylab = "Route distance")
abline(a = 0, b = 1)
abline(a = 0, b = 1.2, col = "green")
abline(a = 0, b = 1.5, col = "red")
```

```{r, echo=FALSE, message=FALSE}
l$d_euclidean <- line_length(l)
l$d_rf <- routes_fast$length
```

The left hand panel of Figure \ref{fig:euclidfastest} shows the expected strong correlation between
Euclidean ($d_E$) and fastest route ($d_{Rf}$) distance. However, some OD pairs
have a proportionally higher route distance than others, as illustrated
by distance from the black line in the above plot: this represents \emph{Circuity ($Q$)}: the ratio of network distance to Euclidean distance \citep{levinson_minimum_2009}:

$$
 Q = \frac{d_{Rf}}{d_E}
$$

An extension to the concept of cirquity is the 'quietness diversion factor' ($QDF$) of a desire line \citep{lovelace_propensity_2016}, the ratio of the route distance of a quiet route option ($d_{Rq}$) to that of the fastest:

$$
 QDF = \frac{d_{Rq}}{d_{Rf}}
$$

Thanks to the 'quietest' route option provided by `route_cyclestreet`, we can estimate average values for both metrics as follows:

```{r, eval=FALSE}
routes_slow <- line2route(l, route_cyclestreet, plan = "quietest")
```

```{r}
l$d_rq <- routes_slow$length # quietest route distance
Q <- mean(l$d_rf / l$d_euclidean, na.rm = TRUE)
QDF <- mean(l$d_rq / l$d_rf, na.rm = TRUE)
Q
QDF
```

The results show that cycle paths are not particularly direct in the study region by international standards \citep{crow_design_2007}.
This is hardly surprisingly given the small size of the sample and the short distances covered: $Q$ tends to decrease at a decaying rate with distance.
What is surprising is that $QDF$ is close to unity, which could imply that the quiet routes are constructed along direct, and therefore sensible routes.
We should caution against such assumptions, however:
It is a small sample of desire lines and, when time is explored, we find that the 'quietness diversion factor with respect to time' ($QDF_t$) is slightly larger:

```{r}
(QDFt <- mean(routes_slow$time / routes_fast$time, na.rm = TRUE))
```

## Models of travel behaviour

There are many ways of estimating flows between origins and destinations,
including spatial interaction models, the four-stage transport model
and gravity models ('distance decay'). **stplanr** aims eventually to facilitate
creation of many types of flow model. 

At present there are no functions for modelling distance decay, but this is something we would like to add in future versions of **stplanr**.
Distance decay is an especially important concept for sustainable transport
planning due to physical limitations on the ability of people to walk and
cycle large distances \citep{iacono_measuring_2010}.

We can explore the relationship between distance and the proportion of trips
made by walking, using the same object `l` generated by **stplanr**.

```{r euclidwalking1, fig.cap='Euclidean distance and walking trips', eval=FALSE}
l$pwalk <- l$On.foot / l$All
plot(l$d_euclidean, l$pwalk,
  cex = l$All / 50,
  xlab = "Euclidean distance (m)", ylab = "Proportion of trips by foot"
)
```

```{r euclidfastest, out.width='100%', fig.cap='Euclidean and fastest route distance of trips in the study area (left) and Euclidean distance vs the proportion of trips made by walking (right).', echo=FALSE}
par(mfrow = c(1, 2))
lgb <- sp::spTransform(l, CRSobj = sp::CRS("+init=epsg:27700"))
l$d_euclidean <- rgeos::gLength(lgb, byid = T)
l$d_rf <- routes_fast@data$length
plot(l$d_euclidean, l$d_rf,
  xlab = "Euclidean distance", ylab = "Route distance"
)
abline(a = 0, b = 1)
abline(a = 0, b = 1.2, col = "green")
abline(a = 0, b = 1.5, col = "red")
l$pwalk <- l$On.foot / l$All
plot(l$d_euclidean, l$pwalk,
  cex = l$All / 50,
  xlab = "Euclidean distance (m)", ylab = "Proportion of trips by foot"
)
```

Based on the right-hand panel in Figure \ref{fig:euclidfastest}, there is a clear negative relationship between distance of trips and the proportion of those trips made by walking.
This is unsurprising: beyond a certain distance (around 1.5km according
the the data presented in the figure above) walking is usually seen as too slow and other modes are considered.
According to the academic literature, this 'distance decay' is non-linear
and there have been a number of functions proposed to fit to distance decay
curves \citep{martinez_new_2013}. From the range of options we test below just two forms.
We will compare the ability of linear and log-square-root functions
to fit the data contained in `l` for walking.

```{r}
lm1 <- lm(pwalk ~ d_euclidean, data = l@data, weights = All)
lm2 <- lm(pwalk ~ d_rf, data = l@data, weights = All)
lm3 <- glm(pwalk ~ d_rf + I(d_rf^0.5),
  data = l@data, weights = All, family = quasipoisson(link = "log")
)
```

The results of these regression models can be seen using `summary()`.
Surprisingly, Euclidean distance was a better predictor of
walking than route distance, but no strong conclusions can be drawn from this finding, with such a small sample of desire lines (n = 42). The results are purely illustrative, of the kind of the possibilities created by using **stplanr** in conjuction with R's modelling capabilities (see Figure \vref{fig:euclidwalking2}).

```{r, echo=FALSE, eval=FALSE}
summary(lm1)
summary(lm2)
summary(lm3)
```

```{r euclidwalking2, fig.cap='Relationship between euclidean distance and walking', out.width="75%", fig.align="center"}
plot(l$d_euclidean, l$pwalk,
  cex = l$All / 50,
  xlab = "Euclidean distance (m)", ylab = "Proportion of trips by foot"
)
l2 <- data.frame(d_euclidean = 1:5000, d_rf = 1:5000)
lm1p <- predict(lm1, l2)
lm2p <- predict(lm2, l2)
lm3p <- predict(lm3, l2)
lines(l2$d_euclidean, lm1p)
lines(l2$d_euclidean, exp(lm2p), col = "green")
lines(l2$d_euclidean, exp(lm3p), col = "red")
```

## Visualisation

Visualisation is an important aspect of any transport study, as it enables researchers to communicate their findings to other researchers, policy-makers and, ultimately, the public. It may therefore come as a surprise that **stplanr** contains no functions for visualisation. Instead, users are encouraged to make use of existing spatial visualisation tools in R, such as **tmap**, **leaflet** and **ggmap** \citep{cheshire_spatial_2015,kahle_ggmap:_2013}.

Furthermore, with the development of online application frameworks such as **shiny**, it is now easier than ever to make the results of transport analysis and modelling projects available to the public. An example is the online interface of the Propensity to Cycle Tool (PCT). The results of the project, generated using **stplanr**, are presented at zone, desire line and Route Network levels \citep{lovelace_propensity_2016}. There is great potential to expand on the principle of publicly accessible transport planning tools via 'web apps', perhaps through new R packages dedicated to visualising transport data.

# Future directions of travel

This paper has demonstrated the great potential for R to be used for transport planning. R's flexibility, powerful GIS capabilities \citep{bivand_applied_2013} and free accessibility makes it well-suited to the needs of transport planners and researchers, especially those wanting to avoid the high costs of market-leading products. Rather than 'reinvent the wheel' (e.g. with a new class system), **stplanr** builds on existing packages and \CRANpkg{sp} classes to work with common transport data formats.

It is useful to see **stplanr**, and R for transport planning in general, as an addition tool in the transport planner's cabinet. It can be understood as one part of a wider movement that is making transport planning a more open and democratic process. Other developments in this movement include the increasing availability of open data \citep{naumova_building_2016} and the rise of open source products for transport modelling, such as [SUMO](https://www.dlr.de/ts/en/desktopdefault.aspx/tabid-9883/16931_read-41000/), [MATSim](https://www.matsim.org/) and [MITSIMLAB](https://its.mit.edu/software/mitsimlab) \citep{saidallah_comparative_2016}. **stplanr**, with its focus on GIS operations rather than microscopic vehicle-level behaviour, can complement such software and help make better use of new open data sources.

Because transport planning is an inherently spatial activity, **stplanr** occupies an important niche in the transport planning software landscape, with its focus on spatial transport data. There is great potential for development of **stplanr** in many directions.
Desirable developments include the additional of functions for modelling modal split, for examample with functions to create commonly distance decay curves which are commonly found in active travel research \citep{martinez_new_2013} and improving the computational efficiency of existing functions to make the methods more scalable for large databases.
Our priority for **stplanr** however, is to keep the focus on geographic functions for transport planning. There are many opportunities in this direction, including:

- Functions to assess the environment surrounding routes, e.g. via integration with the in-development **osmdata** package.
- Functions to match different GIS routes, perhaps building on the Hausdorf distance algorithm implemented in the \CRANpkg{rgeos} function `gDistance`.
- Additional functions for route-allocation of travel, e.g. via an interface to the OpenTripPlanner API.
- Functions for aggregating very large GPS trace datasets (e.g. into raster cells) for anonymisation and analysis/visualisation purposes.
- The creation of a class system for spatial transport datasets, such as to represent spatial route and a route networks (perhaps with classes named \code{"sr"} and \code{"srn"}). This is not a short-term priority and it would be beneficial to coincide such developments to a migration to \CRANpkg{sf} for spatial classes.

Such spatial data processing capabilities would increase the range of transport planning tasks that **stplanr** can facilitate. For all this planned development activity to be useful, it is vital that new functionality is intuitive. R has a famously steep learning curve. Implementing simple concepts such as consistent naming systems \citep{baath_state_2012} and ensuring 'type stability' can greatly improve the usability of the package. For this reason, much future work in **stplanr** will go into improving documentation and user-friendliness. 

Like much open source software **stplanr** is an open-ended project, a work-in-progress. We have set out clear motivations for developing transport planning capabilities in R and believe that the current version of **stplanr** (0.1.6) provides a major step in that direction compared with what was available a couple of years ago. But there is much more to do. We therefore welcome input on where the package's priorities should lie, how it should evolve in the future and how to ensure it is well-developed and sustained.

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# References
