13 Transportation

Prerequisites

  • This chapter uses the following packages:86
library(sf)
library(dplyr)
library(spDataLarge)
library(stplanr)      # for processing geographic transport data
library(tmap)         # map making (see Chapter 9)
library(ggplot2)      # data visualization package
library(sfnetworks)   # spatial network classes and functions 

13.1 Introduction

In few other sectors is geographic space more tangible than transportation. The effort of moving (overcoming distance) is central to the ‘first law’ of geography, defined by Waldo Tobler in 1970 as follows (Miller 2004):

Everything is related to everything else, but near things are more related than distant things.

This ‘law’ is the basis for spatial autocorrelation and other key geographic concepts. It applies to phenomena as diverse as friendship networks and ecological diversity and can be explained by the costs of transport — in terms of time, energy and money — which constitute the ‘friction of distance’. From this perspective, transport technologies are disruptive, changing spatial relationships between geographic entities including mobile humans and goods: “the purpose of transportation is to overcome space” (Rodrigue, Comtois, and Slack 2013).

Transport is an inherently spatial activity, involving moving from an origin point ‘A’ to a destination point ‘B’, through infinite localities in between. It is therefore unsurprising that transport researchers have long turned to geographic and computational methods to understand movement patterns, and how interventions can improve their performance (Lovelace 2021).

This chapter introduces the geographic analysis of transport systems at different geographic levels:

  • Areal units: transport patterns can be understood with reference to zonal aggregates, such as the main mode of travel (by car, bike or foot, for example), and average distance of trips made by people living in a particular zone, covered in Section 13.3.
  • Desire lines: straight lines that represent ‘origin-destination’ data that records how many people travel (or could travel) between places (points or zones) in geographic space, the topic of Section 13.4.
  • Nodes: these are points in the transport system that can represent common origins and destinations and public transport stations such as bus stops and rail stations, the topic of Section 13.5.
  • Routes: these are lines representing a path along the route network along the desire lines and between nodes. Routes, and the routing engines that generate them, are covered in Section 13.6.
  • Route networks: these represent the system of roads, paths and other linear features in an area and are covered in Section 13.7. They can be represented as geographic features (representing route segments) or structured as an interconnected graph, with the level of traffic on different segments referred to as ‘flow’ by transport modelers (Hollander 2016)

Another key level is agents, mobile entities like you and me and vehicles that enable use to move such as bikes and buses. These can be represented computationally in software such as MATSim and A/B Street, which represent the dynamics of transport systems using an agent-based modeling (ABM) frameworks, usually at high levels of spatial and temporal resolution (Horni, Nagel, and Axhausen 2016). ABM is a powerful approach to transport research with great potential for integration with R’s spatial classes (Thiele 2014; Lovelace and Dumont 2016), but is outside the scope of this chapter. Beyond geographic levels and agents, the basic unit of analysis in many transport models is the trip, a single purpose journey from an origin ‘A’ to a destination ‘B’ (Hollander 2016). Trips join-up the different levels of transport systems and can be represented simplistically as geographic desire lines connecting zone centroids (nodes) or as routes that follow the transport route network. In this context, agents are usually point entities that move within the transport network.

Transport systems are dynamic (Xie and Levinson 2011). While the focus of this chapter is the geographic analysis of a transport systems, it provides insights into how the approach can be used to simulate scenarios of change, in Section 13.8. The purpose of geographic transport modeling can be interpreted as simplifying the complexity of these spatio-temporal systems in ways that capture their essence. Selecting appropriate levels of geographic analysis can help simplify this complexity without losing its most important features and variables, enabling better decision making and more effective interventions (Hollander 2016).

Typically, models are designed to tackle a particular problem, such as how to improve safety or the environmental performance of transport systems. For this reason, this chapter is based around a policy scenario, introduced in the next section, that asks: how to increase cycling in the city of Bristol? Chapter 14 demonstrates a related application of geocomputation: prioritizing the location of new bike shops. There is a link between the chapters: new and effectively-located cycling infrastructure can get people cycling, boosting demand for bike shops and local economic activity. This highlights an important feature of transport systems: they are closely linked to broader phenomena and land-use patterns.

13.2 A case study of Bristol

The case study used for this chapter is located in Bristol, a city in the west of England, around 30 km east of the Welsh capital Cardiff. An overview of the region’s transport network is illustrated in Figure 13.1, which shows a diversity of transport infrastructure, for cycling, public transport, and private motor vehicles.

Bristol's transport network represented by colored lines for active (green), public (railways, black) and private motor (red) modes of travel. Black border lines represent the inner city boundary (highlighted in yellow) and the larger Travel To Work Area (TTWA).

FIGURE 13.1: Bristol’s transport network represented by colored lines for active (green), public (railways, black) and private motor (red) modes of travel. Black border lines represent the inner city boundary (highlighted in yellow) and the larger Travel To Work Area (TTWA).

Bristol is the 10th largest city council in England, with a population of half a million people, although its travel catchment area is larger (see Section 13.3). It has a vibrant economy with aerospace, media, financial service and tourism companies, alongside two major universities. Bristol shows a high average income per person but also contains areas of severe deprivation (Bristol City Council 2015).

In terms of transport, Bristol is well served by rail and road links, and has a relatively high level of active travel. 19% of its citizens cycle and 88% walk at least once per month according to the Active People Survey (the national average is 15% and 81%, respectively). 8% of the population said they cycled to work in the 2011 census, compared with only 3% nationwide.

Despite impressive walking and cycling statistics, the city has a major congestion problem. Part of the solution is to continue to increase the proportion of trips made by cycling. Cycling has a greater potential to replace car trips than walking, with typical speeds of 15-20 km/h vs 4-6 km/h for walking. Furthermore many local transport plans, including Bristol’s Transport Strategy, have ambitious plans for cycling.

In this policy context, the aim of this chapter, beyond demonstrating how geocomputation with R can be used to support sustainable transport planning, is to provide evidence for decision-makers in Bristol how best to increase the share of walking and cycling in particular in the city. This high-level aim will be met via the following objectives:

  • Describe the geographical pattern of transport behavior in the city
  • Identify key public transport nodes and routes along which cycling to rail stations could be encouraged, as the first stage in multi-model trips
  • Analyze travel ‘desire lines’ to find where many people drive short distances
  • Identify cycle route locations that will encourage less car driving and more cycling

To get the wheels rolling on the practical aspects of this chapter, we begin by loading zonal data on travel patterns. These zone-level data are small but often vital for gaining a basic understanding of a settlement’s overall transport system.

13.3 Transport zones

Although transport systems are primarily based on linear features and nodes — including pathways and stations — it often makes sense to start with areal data, to break continuous space into tangible units (Hollander 2016). In addition to the boundary defining the study area (Bristol in this case), two zone types are of particular interest to transport researchers: origin and destination zones. Often, the same geographic units are used for origins and destinations. However, different zoning systems, such as ‘Workplace Zones’, may be appropriate to represent the increased density of trip destinations in areas with many ‘trip attractors’ such as schools and shops (Office for National Statistics 2014).

The simplest way to define a study area is often the first matching boundary returned by OpenStreetMap. This can be done with a command such as osmdata::getbb("Bristol", format_out = "sf_polygon", limit = 1). This returns an sf object (or a list of sf objects if limit = 1 is not specified) representing the bounds of the largest matching city region, either a rectangular polygon of the bounding box or a detailed polygonal boundary.87 For Bristol, a detailed polygon is returned, as represented by the bristol_region object in the spDataLarge package. See the inner blue boundary in Figure 13.1: there are a couple of issues with this approach:

  • The first OSM boundary returned by OSM may not be the official boundary used by local authorities
  • Even if OSM returns the official boundary, this may be inappropriate for transport research because they bear little relation to where people travel

Travel to Work Areas (TTWAs) address these issues by creating a zoning system analogous to hydrological watersheds. TTWAs were first defined as contiguous zones within which 75% of the population travels to work (Coombes, Green, and Openshaw 1986), and this is the definition used in this chapter. Because Bristol is a major employer attracting travel from surrounding towns, its TTWA is substantially larger than the city bounds (see Figure 13.1). The polygon representing this transport-orientated boundary is stored in the object bristol_ttwa, provided by the spDataLarge package loaded at the beginning of this chapter.

The origin and destination zones used in this chapter are the same: officially defined zones of intermediate geographic resolution (their official name is Middle layer Super Output Areas or MSOAs). Each houses around 8,000 people. Such administrative zones can provide vital context to transport analysis, such as the type of people who might benefit most from particular interventions (e.g., Moreno-Monroy, Lovelace, and Ramos 2017).

The geographic resolution of these zones is important: small zones with high geographic resolution are usually preferable but their high number in large regions can have consequences for processing (especially for origin-destination analysis in which the number of possibilities increases as a non-linear function of the number of zones) (Hollander 2016).

Another issue with small zones is related to anonymity rules. To make it impossible to infer the identity of individuals in zones, detailed socio-demographic variables are often only available at a low geographic resolution. Breakdowns of travel mode by age and sex, for example, are available at the Local Authority level in the UK, but not at the much higher Output Area level, each of which contains around 100 households. For further details, see www.ons.gov.uk/methodology/geography.

The 102 zones used in this chapter are stored in bristol_zones, as illustrated in Figure 13.2. Note the zones get smaller in densely populated areas: each houses a similar number of people. bristol_zones contains no attribute data on transport, however, only the name and code of each zone:

names(bristol_zones)
#> [1] "geo_code" "name"     "geometry"

To add travel data, we will perform an attribute join, a common task described in Section 3.2.4. We will use travel data from the UK’s 2011 census question on travel to work, data stored in bristol_od, which was provided by the ons.gov.uk data portal. bristol_od is an origin-destination (OD) dataset on travel to work between zones from the UK’s 2011 Census (see Section 13.4). The first column is the ID of the zone of origin and the second column is the zone of destination. bristol_od has more rows than bristol_zones, representing travel between zones rather than the zones themselves:

nrow(bristol_od)
#> [1] 2910
nrow(bristol_zones)
#> [1] 102

The results of the previous code chunk shows that there are more than 10 OD pairs for every zone, meaning we will need to aggregate the origin-destination data before it is joined with bristol_zones, as illustrated below (origin-destination data is described in Section 13.4):

zones_attr = bristol_od |> 
  group_by(o) |> 
  summarize(across(where(is.numeric), sum)) |> 
  dplyr::rename(geo_code = o)

The preceding chunk:

  • Grouped the data by zone of origin (contained in the column o)
  • Aggregated the variables in the bristol_od dataset if they were numeric, to find the total number of people living in each zone by mode of transport88
  • Renamed the grouping variable o so it matches the ID column geo_code in the bristol_zones object

The resulting object zones_attr is a data frame with rows representing zones and an ID variable. We can verify that the IDs match those in the zones dataset using the %in% operator as follows:

summary(zones_attr$geo_code %in% bristol_zones$geo_code)
#>    Mode    TRUE 
#> logical     102

The results show that all 102 zones are present in the new object and that zone_attr is in a form that can be joined onto the zones.89 This is done using the joining function left_join() (note that inner_join() would produce here the same result):

zones_joined = left_join(bristol_zones, zones_attr, by = "geo_code")
sum(zones_joined$all)
#> [1] 238805
names(zones_joined)
#> [1] "geo_code"   "name"       "all"        "bicycle"    "foot"      
#> [6] "car_driver" "train"      "geometry"

The result is zones_joined, which contains new columns representing the total number of trips originating in each zone in the study area (almost 1/4 of a million) and their mode of travel (by bicycle, foot, car and train). The geographic distribution of trip origins is illustrated in the left-hand map in Figure 13.2. This shows that most zones have between 0 and 4,000 trips originating from them in the study area. More trips are made by people living near the center of Bristol and fewer on the outskirts. Why is this? Remember that we are only dealing with trips within the study region: low trip numbers in the outskirts of the region can be explained by the fact that many people in these peripheral zones will travel to other regions outside of the study area. Trips outside the study region can be included in regional model by a special destination ID covering any trips that go to a zone not represented in the model (Hollander 2016). The data in bristol_od, however, simply ignores such trips: it is an ‘intra-zonal’ model.

In the same way that OD datasets can be aggregated to the zone of origin, they can also be aggregated to provide information about destination zones. People tend to gravitate towards central places. This explains why the spatial distribution represented in the right panel in Figure 13.2 is relatively uneven, with the most common destination zones concentrated in Bristol city center. The result is zones_od, which contains a new column reporting the number of trip destinations by any mode, is created as follows:

zones_destinations = bristol_od |> 
  group_by(d) |> 
  summarize(across(where(is.numeric), sum)) |> 
  dplyr::select(geo_code = d, all_dest = all)
zones_od = inner_join(zones_joined, zones_destinations, by = "geo_code") |> 
  st_as_sf()

A simplified version of Figure 13.2 is created with the code below (see 12-zones.R in the code folder of the book’s GitHub repo to reproduce the figure and Section 9.2.6 for details on faceted maps with tmap):

qtm(zones_od, c("all", "all_dest")) +
  tm_layout(panel.labels = c("Origin", "Destination"))
Number of trips (commuters) living and working in the region. The left map shows zone of origin of commute trips; the right map shows zone of destination (generated by the script 13-zones.R).

FIGURE 13.2: Number of trips (commuters) living and working in the region. The left map shows zone of origin of commute trips; the right map shows zone of destination (generated by the script 13-zones.R).

13.4 Desire lines

Desire lines connect origins and destinations, representing where people desire to go, typically between zones. They represent the quickest ‘bee line’ or ‘crow flies’ route between A and B that would be taken, if it were not for obstacles such as buildings and windy roads getting in the way (we will see how to convert desire lines into routes in the next section). Typically, desire lines are represented geographically as starting and ending in the geographic (or population weighted) centroid of each zone. This is the type of desire line that we will create and use in this section, although it is worth being aware of ‘jittering’ techniques that enable multiple start and end points to increase the spatial coverage and accuracy of analyses building on OD data (Lovelace, Félix, and Carlino 2022).

We have already loaded data representing desire lines in the dataset bristol_od. This origin-destination (OD) data frame object represents the number of people traveling between the zone represented in o and d, as illustrated in Table 13.1. To arrange the OD data by all trips and then filter-out only the top 5, type (please refer to Chapter 3 for a detailed description of non-spatial attribute operations):

od_top5 = bristol_od |> 
  arrange(desc(all)) |> 
  top_n(5, wt = all)
TABLE 13.1: Sample of the top 5 origin-destination pairs in the Bristol OD data frame, representing travel desire lines between zones in the study area.
o d all bicycle foot car_driver train
E02003043 E02003043 1493 66 1296 64 8
E02003047 E02003043 1300 287 751 148 8
E02003031 E02003043 1221 305 600 176 7
E02003037 E02003043 1186 88 908 110 3
E02003034 E02003043 1177 281 711 100 7

The resulting table provides a snapshot of Bristolian travel patterns in terms of commuting (travel to work). It demonstrates that walking is the most popular mode of transport among the top 5 origin-destination pairs, that zone E02003043 is a popular destination (Bristol city center, the destination of all the top 5 OD pairs), and that the intrazonal trips, from one part of zone E02003043 to another (first row of Table 13.1), constitute the most traveled OD pair in the dataset. But from a policy perspective, the raw data presented in Table 13.1 is of limited use: aside from the fact that it contains only a tiny portion of the 2,910 OD pairs, it tells us little about where policy measures are needed, or what proportion of trips are made by walking and cycling. The following command calculates the percentage of each desire line that is made by these active modes:

bristol_od$Active = (bristol_od$bicycle + bristol_od$foot) /
  bristol_od$all * 100

There are two main types of OD pair: interzonal and intrazonal. Interzonal OD pairs represent travel between zones in which the destination is different from the origin. Intrazonal OD pairs represent travel within the same zone (see the top row of Table 13.1). The following code chunk splits od_bristol into these two types:

od_intra = filter(bristol_od, o == d)
od_inter = filter(bristol_od, o != d)

The next step is to convert the interzonal OD pairs into an sf object representing desire lines that can be plotted on a map with the stplanr function od2line().90

desire_lines = od2line(od_inter, zones_od)
#> Creating centroids representing desire line start and end points.

An illustration of the results is presented in Figure 13.3, a simplified version of which is created with the following command (see the code in 13-desire.R to reproduce the figure exactly and Chapter 9 for details on visualization with tmap):

qtm(desire_lines, lines.lwd = "all")
Desire lines representing trip patterns in Bristol, with width representing number of trips and color representing the percentage of trips made by active modes (walking and cycling). The four black lines represent the interzonal OD pairs in Table 7.1.

FIGURE 13.3: Desire lines representing trip patterns in Bristol, with width representing number of trips and color representing the percentage of trips made by active modes (walking and cycling). The four black lines represent the interzonal OD pairs in Table 7.1.

The map shows that the city center dominates transport patterns in the region, suggesting policies should be prioritized there, although a number of peripheral sub-centers can also be seen. Desire lines are important generalized components of transport systems. More concrete components include nodes, which have specific destinations (rather than hypothetical straight lines represented in desire lines). Nodes are covered in the next section.

13.5 Nodes

Nodes in geographic transport desets are points among the predominantly linear features that comprise transport networks. Broadly are two main types of transport nodes:

  1. Nodes not directly on the network such as zone centroids or individual origins and destinations such as houses and workplaces.
  2. Nodes that are a part of transport networks. Technically, a node can be located at any point on a transport network but in practice they are often special kinds of vertex such as intersections between pathways (junctions) and points for entering or exiting a transport networksuch as bus stops and train stations.91

Transport networks can be represented as graphs, in which each segment is connected (via edges representing geographic lines) to one or more other edges in the network. Nodes outside the network can be added with “centroid connectors”, new route segments to nearby nodes on the network (Hollander 2016).92 Every node in the network is then connected by one or more ‘edges’ that represent individual segments on the network. We will see how transport networks can be represented as graphs in Section 13.7.

Public transport stops are particularly important nodes that can be represented as either type of node: a bus stop that is part of a road, or a large rail station that is represented by its pedestrian entry point hundreds of meters from railway tracks. We will use railway stations to illustrate public transport nodes, in relation to the research question of increasing cycling in Bristol. These stations are provided by spDataLarge in bristol_stations.

A common barrier preventing people from switching away from cars for commuting to work is that the distance from home to work is too far to walk or cycle. Public transport can reduce this barrier by providing a fast and high-volume option for common routes into cities. From an active travel perspective, public transport ‘legs’ of longer journeys divide trips into three:

  • The origin leg, typically from residential areas to public transport stations
  • The public transport leg, which typically goes from the station nearest a trip’s origin to the station nearest its destination
  • The destination leg, from the station of alighting to the destination

Building on the analysis conducted in Section 13.4, public transport nodes can be used to construct three-part desire lines for trips that can be taken by bus and (the mode used in this example) rail. The first stage is to identify the desire lines with most public transport travel, which in our case is easy because our previously created dataset desire_lines already contains a variable describing the number of trips by train (the public transport potential could also be estimated using public transport routing services such as OpenTripPlanner). To make the approach easier to follow, we will select only the top three desire lines in terms of rails use:

desire_rail = top_n(desire_lines, n = 3, wt = train)

The challenge now is to ‘break-up’ each of these lines into three pieces, representing travel via public transport nodes. This can be done by converting a desire line into a multilinestring object consisting of three line geometries representing origin, public transport and destination legs of the trip. This operation can be divided into three stages: matrix creation (of origins, destinations and the ‘via’ points representing rail stations), identification of nearest neighbors and conversion to multilinestrings. These are undertaken by line_via(). This stplanr function takes input lines and points and returns a copy of the desire lines — see the ?line_via() for details on how this works. The output is the same as the input line, except it has new geometry columns representing the journey via public transport nodes, as demonstrated below:

ncol(desire_rail)
#> [1] 9
desire_rail = line_via(desire_rail, bristol_stations)
ncol(desire_rail)
#> [1] 12

As illustrated in Figure 13.4, the initial desire_rail lines now have three additional geometry list columns representing travel from home to the origin station, from there to the destination, and finally from the destination station to the destination. In this case, the destination leg is very short (walking distance) but the origin legs may be sufficiently far to justify investment in cycling infrastructure to encourage people to cycle to the stations on the outward leg of peoples’ journey to work in the residential areas surrounding the three origin stations in Figure 13.4.

Station nodes (red dots) used as intermediary points that convert straight desire lines with high rail usage (thin green lines) into three legs: to the origin station (orange) via public transport (blue) and to the destination (pink, not visible because it is so short).

FIGURE 13.4: Station nodes (red dots) used as intermediary points that convert straight desire lines with high rail usage (thin green lines) into three legs: to the origin station (orange) via public transport (blue) and to the destination (pink, not visible because it is so short).

13.6 Routes

From a geographical perspective, routes are desire lines that are no longer straight: the origin and destination points are the same as in the desire line representation of travel, but the pathway to get from A to B is more complex. The geometries of routes are typically (but not always) determined by the transport network.

While desire lines contain only two vertices (their beginning and end points), routes can contain any number of vertices, representing points between A and B joined by straight lines: the definition of a linestring geometry. Routes covering large distances or following intricate network can have many hundreds of vertices; routes on grid-based or simplified road networks tend to have fewer.

Routes are generated from desire lines or, more commonly, matrices containing coordinate pairs representing desire lines. This routing process is done by a range of broadly-defined routing engines: software and web services that return geometries and attributes describing how to get from origins to destinations. There are three main types of routing engine:

  • In-memory routing using R packages that enable route calculation
  • Locally hosted routing engines external to R that can be called from R
  • Remotely hosted routing engines by external entities that provide a web API that can be called from R

13.6.1 Routing engines in R package

Routing engines in R enable route networks stored as R objects in memory to be used as the basis of route calculation. Options include sfnetworks, dodgr and cppRouting packages, each of which provide their own class system to represent route networks, the topic of the next section.

While fast and flexible, native R routing options are generally harder to set-up than dedicated routing engines for realistic route calculation. Routing is a hard problem and many hundreds of hours have been put into open source routing engines that can be downloaded and hosted locally. On the other hand, R-based routing engines may be well-suited to model experiments and the statistical analysis of the impacts of changes on the network. Changing route network characteristics (or weights associated with different route segment types), re-calculating routes, and analyzing results under many scenarios in a single language has benefits for research applications.

13.6.2 Locally hosted dedicated routing engines

Locally hosted routing engines include OpenTripPlanner, OSRM and R5, and associated R packages opentripplanner, osrm and r5r (Morgan et al. 2019; Pereira et al. 2021). Locally hosted routing engines run on the user’s computer but in a process separate from R. They benefit from speed of execution and control over the weighting profile for different modes of transport. Disadvantages include the difficulty of representing complex networks locally; temporal dynamics (primarily due to traffic); and the need for specialized software. A key feature of some advanced routing engines (both locally hosted and remotely hosted) such as R5, OpenTripPlanner and Valhalla (which at the time of writing does not have an R interface to the best of our knowledge) is the provision of multi-modal routes: allowing a single journey from A to B to be made by more than one mode of transport, such as cycling from home to a bus stop, catching a bus to the city center, and then walking to the destination.

13.6.3 Remotely hosted dedicated routing engines

Remotely hosted routing engines use a web API to send queries about origins and destinations and return results generated on a powerful server running dedicated software. Routing services based on open source routing engines, such as OSRM’s publicly available service, work exactly the same when called from R as locally hosted instances, simply requiring arguments specifying ‘base URLs’ to be updated. However, the fact that external routing services are hosted on a dedicated machine (usually funded by commercial company with incentives to generate accurate routes) can can give them advantages, including:

  • Provision of routing services worldwide (or usually at least over a large region)
  • Established routing services available are usually update regularly and can often respond to traffic levels
  • Routing services usually run on dedicated hardware and software including systems such as load balancers to ensure consistent performance

Disadvantages of remote routing services include speed when batch jobs are not possible (they often rely on data transfer over the internet on a route-by-route basis), price (the Google routing API, for example, limits the number of free queries) and licensing issues. googleway and mapbox packages demonstrate this approach by providing access to routing services from Google and Mapbox, respectively. Free (but rate limited) routing service include OSRM and openrouteservice.org which can be accessed from R with the osrm and openrouteservice packages, the latter of which is not on CRAN. There are also more specific routing services such as that provided by CycleStreets.net, a cycle journey planner and not-for-profit transport technology company “for cyclists, by cyclists”. While R users can access CycleStreets routes via the package cyclestreets, many routing services lack R interfaces, representing a substantial opportunity for package development: building an R package to provide an interface to a web API can be a rewarding experience.

The wide range of R packages for computing and importing data representing routes on transport networks is a strength, meaning that the language has been increasingly used for transport research over the last few years. However, a minor disadvantage of this proliferation of package and approaches is that there are many package and function names to remember. The package stplanr tackles this problem by providing a unified interface for generating routes with the route() function. The function takes a wide range of inputs, including geographic desire lines (with the l = argument), coordinates and even text strings representing unique addresses, and returns route data as consistent sf objects.

13.6.4 Route and segment level outputs

Regardless of the routing engine used, there are two broad types of output: route level and segment level. Route level outputs contain a single feature (typically a multilinestring and associated row in the data frame representation) per origin-destination pair, meaning a single row of data per trip. Most routing engines return route level by default, although multi-modal engines generally provide outputs at the ‘leg’ level (one feature per continuous movement by a single mode of transport). Segment level data, by contrast, provides information about routes at the street segment. The cyclestreets package, for example, provides segment level data by default, including street names and level of ‘quietness’, a proxy for cycle-friendliness.

Segment level results are less common. They are more complex (with many rows of data returned for a single OD pair), making them harder to work with. Segment level results provide characteristics of individual ways on the network are important, useful when aiming to discern the most dangerous part of trip, although route level results can be converted into segment level results using the function stplanr::overline() (Morgan and Lovelace 2020). When working with segment or leg-level data, route-level statistics can be returned by grouping by columns representing trip start and end points and summarizing/aggregating columns containing segment-level data.

13.6.5 Routing: A worked example

Instead of routing all desire lines generated in the previous section, we will focus on the desire lines of policy interest. Doing routing on a subset of the data before trying to compute routes for a large number of OD pairs is often sensible: routing can be time and memory-consuming: route datasets are usually substantially larger than desire line datasets because they have more detailed geometries and often have more attributes such as road name.

We will filter-out a subset of the desire lines with a focus on estimating cycling potential based on the observation that the benefits of cycling trips are greatest when they replace car trips and that relatively short trips are more likely to be cycled than long trips (Lovelace et al. 2017). Clearly, not all car trips can realistically be replaced by cycling. Trips between 2.5 and 5 km Euclidean distance (or around 3 km, below which trips are can be made by walking or scooter, to 6-8 km of route distance) have a relatively high probability of being cycled, and the maximum distance increases when trips are made by electric bike. Considering this, and the utility of a small route dataset for testing, we will compute routes for the subset of desire lines along which a high (100+) number of car trips take place that are 2.5 to 5 km in length:

desire_lines$distance_km = as.numeric(st_length(desire_lines)) / 1000
desire_lines_short = desire_lines |> 
  filter(car_driver >= 100, distance_km <= 5, distance_km >= 2.5)

In the code above st_length() calculated the length of each desire line, as described in Section 4.2.8. The filter() function from dplyr filtered the desire_lines dataset based on the criteria outlined above, as described in Section 3.2.1. The next stage is to convert these desire lines into routes. This is done using the publicly available OSRM service with the stplanr functions route() and route_osrm() in the code chunk below:

routes_short = route(l = desire_lines_short, route_fun = route_osrm,
                     osrm.profile = "bike")
#> Most common output is sf

The output is routes_short, an sf object representing routes on the transport network that are suitable for cycling (according to the OSRM routing engine at least), one for each desire line. Note: calls to external routing engines such as in the command above only work with an internet connection (and sometimes an API key stored in an environment variable, although not in this case). In addition to the columns contained in the desire_lines object, the new route dataset contains distance (referring to route distance this time) and duration columns (in seconds), which provide potentially useful extra information on the nature of each route. We will plot desire lines along which many short car journeys take place alongside cycling routes. Making the width of the routes proportional to the number of car journeys that could potentially be replaced provides an effective way to prioritize interventions on the road network (Lovelace et al. 2017). The code chunk below plots the desire lines and routes, resulting in Figure 13.5 which shows routes along which people drive short distances:93

Routes along which many (100+) short (<5km Euclidean distance) car journeys are made (red) overlaying desire lines representing the same trips (black) and zone centroids (dots).

FIGURE 13.5: Routes along which many (100+) short (<5km Euclidean distance) car journeys are made (red) overlaying desire lines representing the same trips (black) and zone centroids (dots).

Plotting the results on an interactive map, with mapview::mapview(st_geometry(routes_short)) for example, shows that many short car trips take place in and around Bradley Stoke. It is easy to find explanations for the area’s high level of car dependency: according to Wikipedia, Bradley Stoke is “Europe’s largest new town built with private investment”, suggesting limited public transport provision. Furthermore, the town is surrounded by large (cycling unfriendly) road structures, “such as junctions on both the M4 and M5 motorways” (Tallon 2007).

There are many benefits of converting travel desire lines into routes. It is important to remember that we cannot be sure how many (if any) trips will follow the exact routes calculated by routing engines. However, route and street/way/segment level results can be highly policy relevant. Route segment results can enable the prioritization of investment in cycleways where they are most needed according to available data (Lovelace et al. 2017), for example.

13.7 Route networks

While routes generally contain data at same level as desire lines (or at the level of segments with potentially overlapping segments), route network datasets represent a more-or-less complete representation of a transport network. Each segment (roughly corresponding to a continuous section of street between junctions) in a route network is present once and only once.

Sometimes route networks are the input into a transport data analysis project; sometimes they are an output; and sometimes route networks are both an input and an output. To be specific: any routing engines requires some kind of route network input, so any transport research of routes must invariably involve route network data in the internal or external routing engines (in the latter case the route network data is not necessarily imported into R). However, route networks are also important outputs in many transport research projects: summarizing data such as the potential number of trips made on particular segments and represented as a route network, can help prioritize investment where it is most needed.

To demonstrate how to create route networks as an output derived from route level data, imagine a simple scenario of mode shift. Imagine that 50% of car trips between 0 to 3 km in route distance are replaced by cycling, a percentage that drops by 10 percentage points for every additional km of route distance so that 20% of car trips of 6 km are replaced by cycling and no car trips that are 8 km or longer are replaced by cycling. This is of course an unrealistic scenario (Lovelace et al. 2017), but is a useful starting point. In this case, we can model mode shift from cars to bikes as follows:

uptake = function(x) {
  case_when(
    x <= 3 ~ 0.5,
    x >= 8 ~ 0,
    TRUE ~ (8 - x) / (8 - 3) * 0.5
  )
}
routes_short_scenario = routes_short |> 
  mutate(uptake = uptake(distance / 1000)) |> 
  mutate(bicycle = bicycle + car_driver * uptake,
         car_driver = car_driver * (1 -uptake))
sum(routes_short_scenario$bicycle) - sum(routes_short$bicycle)
#> [1] 3980

Having created a scenario in which approximately 4000 trips have switched from driving to cycling, we can now model where this updated modeled cycling activity will take place. For this, we will use the function overline() from the stplanr package. The function breaks linestrings at junctions (were two or more linestring geometries meet), and calculates aggregate statistics for each unique route segment (Morgan and Lovelace 2020), taking an object containing routes and the names of the attributes to summarize as the first and second argument:

route_network_scenario = overline(routes_short_scenario, attrib = "bicycle")

The outputs of the two preceding code chunks are summarized in Figure 13.6 below.

Illustration of the % of car trips switching to cycling as a function of distance (left) and route network level results of this function (right).Illustration of the % of car trips switching to cycling as a function of distance (left) and route network level results of this function (right).

FIGURE 13.6: Illustration of the % of car trips switching to cycling as a function of distance (left) and route network level results of this function (right).

Another, more common, type of route network dataset is road network datasets. This type of route network dataset is available for every city worldwide from OpenStreetMap. The input dataset used to illustrate this type of route network downloaded using osmdata. To avoid having to request the data from OSM repeatedly, we will use the bristol_ways object from the spDataLarge package, which contains point and line data for the case study area (see ?bristol_ways):

summary(bristol_ways)
#>      highway       maxspeed             ref                     geometry   
#>  cycleway:1721   Length:6160        Length:6160        LINESTRING   :6160  
#>  rail    :1017   Class :character   Class :character   epsg:4326    :   0  
#>  road    :3422   Mode  :character   Mode  :character   +proj=long...:   0

The above code chunk summarized a simple feature object representing around 5,000 segments on the transport network. This an easily manageable dataset size (transport datasets can be large, but it’s best to start small).

As mentioned, route networks can usefully be represented as mathematical graphs, with nodes on the network, connected by edges. A number of R packages have been developed for dealing with such graphs, notably igraph. You can manually convert a route network into an igraph object, but the geographic attributes will be lost. To overcome this issue functionality was developed in the sfnetworks package, to represent route networks simultaneously as graphs and a set of geographic lines. We will demonstrate sfnetworks functionality on the bristol_ways object.

bristol_ways$lengths = st_length(bristol_ways)
ways_sfn = as_sfnetwork(bristol_ways)
class(ways_sfn)
#> [1] "sfnetwork" "tbl_graph" "igraph"
ways_sfn
#> # A sfnetwork with 5728 nodes and 4915 edges
#> # A directed multigraph with 1013 components with spatially explicit edges
#> # Node Data:     5,728 × 1 (active)
#> # Edge Data:     4,915 × 7
#>    from    to highway maxspeed ref                              geometry lengths
#>   <int> <int> <fct>   <fct>    <fct>                    <LINESTRING [°]>     [m]
#> 1     1     2 road    <NA>     B3130 (-2.61 51.4, -2.61 51.4, -2.61 51.…    218.
#> # … 

The output of the previous code chunk (with the final output shortened to contain only the most important 8 lines due to space considerations) shows that ways_sfn is a composite object, containing both nodes and edges in graph and spatial form. ways_sfn is of class sfnetwork, which builds on the igraph class from the igraph package. In the example below, the ‘edge betweenness’, meaning the number of shortest paths passing through each edge, is calculated (see ?igraph::betweenness for further details). The result of calculating edge betweenness for this dataset are shown Figure 13.7, which has the cycle route network dataset calculated with the overline() function as an overlay for comparison. The results demonstrate that each graph edge represents a segment: the segments near the center of the road network have the highest betweenness values, whereas segments closer to central Bristol have higher cycling potential, based on these simplistic datasets.

ways_centrality = ways_sfn |> 
  activate("edges") |>  
  mutate(betweenness = tidygraph::centrality_edge_betweenness(lengths)) 
tm_shape(ways_centrality |> st_as_sf()) +
  tm_lines(lwd = "betweenness", scale = 9, title.lwd = "Betweenness") +
  tm_shape(route_network_scenario) +
  tm_lines(lwd = "bicycle", scale = 9, title.lwd = "N0. bike trips (modeled, one direction)", col = "green")
#> Warning in CPL_transform(x, crs, aoi, pipeline, reverse, desired_accuracy, :
#> GDAL Error 1: PROJ: proj_as_wkt: DatumEnsemble can only be exported to WKT2:2019
Illustration of a small route network, with segment thickness proportional to its betweenness, generated using the igraph package and described in the text.

FIGURE 13.7: Illustration of a small route network, with segment thickness proportional to its betweenness, generated using the igraph package and described in the text.

One can also find the shortest route between origins and destinations using this graph representation of the route network with the sfnetworks package. While the methods presented in this section are relatively simple compared with what is possible with spatial network generation (e.g., with overline()) and analysis techniques (e.g., with sfnetworks), they should provide a strong starting point for further exploration and research into the area.

13.8 Prioritizing new infrastructure

This chapter’s final practical section demonstrates the policy-relevance of geocomputation for transport applications by identifying locations where new transport infrastructure may be needed. Clearly, the types of analysis presented here would need to be extended and complemented by other methods to be used in real-world applications, as discussed in Section 13.9. However, each stage could be useful on its own, and feed into wider analyses. To summarize, these were: identifying short but car-dependent commuting routes (generated from desire lines) in Section 13.6; creating desire lines representing trips to rail stations in Section 13.5; and analysis of transport systems at the route network level using graph theory and the sfnetworks package in Section 13.7.

The final code chunk of this chapter combines these strands of analysis. It overlays estimates of cycling potential from the previous section on top of a new dataset representing areas within a short distance of cycling infrastructure. This new dataset is created in the code chunk below which: 1) filters out the cycleway entities from the bristol_ways object representing the transport network; 2) ‘unions’ the individual LINESTRING entities of the cycleways into a single multilinestring object (for speed of buffering); and 3) creates a 100 m buffer around them to create a polygon:

existing_cycleways_buffer = bristol_ways |> 
  filter(highway == "cycleway") |>    # 1) filter out cycleways
  st_union() |>                       # 2) unite geometries
  st_buffer(dist = 100)               # 3) create buffer

The next stage is to create a dataset representing points on the network where there is high cycling potential but little provision for cycling:

route_network_no_infra = st_difference(
  # route_network_scenario,
  # Temporary workaround, see https://github.com/Robinlovelace/geocompr/issues/863:
  route_network_scenario |> st_set_crs(st_crs(existing_cycleways_buffer)),
  existing_cycleways_buffer
)
#> Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
#> that
#> Warning: attribute variables are assumed to be spatially constant throughout all
#> geometries

The results of the preceding code chunks are shown in Figure 13.8, which shows routes with high levels of car dependency and high cycling potential but no cycleways.

tmap_mode("view")
qtm(route_network_no_infra, basemaps = leaflet::providers$Esri.WorldTopoMap,
    lines.lwd = 5)
#> Warning in CPL_transform(x, crs, aoi, pipeline, reverse, desired_accuracy, :
#> GDAL Error 1: PROJ: proj_as_wkt: DatumEnsemble can only be exported to WKT2:2019

#> Warning in CPL_transform(x, crs, aoi, pipeline, reverse, desired_accuracy, :
#> GDAL Error 1: PROJ: proj_as_wkt: DatumEnsemble can only be exported to WKT2:2019

#> Warning in CPL_transform(x, crs, aoi, pipeline, reverse, desired_accuracy, :
#> GDAL Error 1: PROJ: proj_as_wkt: DatumEnsemble can only be exported to WKT2:2019
Potential routes along which to prioritise cycle infrastructure in Bristol to reduce car dependency. The static map provides an overview of the overlay between existing infrastructure and routes with high car-bike switching potential (left). The screenshot the interactive map generated from the `qtm()` function highlights Whiteladies Road as somewhere that would benefit from a new cycleway (right).Potential routes along which to prioritise cycle infrastructure in Bristol to reduce car dependency. The static map provides an overview of the overlay between existing infrastructure and routes with high car-bike switching potential (left). The screenshot the interactive map generated from the `qtm()` function highlights Whiteladies Road as somewhere that would benefit from a new cycleway (right).

FIGURE 13.8: Potential routes along which to prioritise cycle infrastructure in Bristol to reduce car dependency. The static map provides an overview of the overlay between existing infrastructure and routes with high car-bike switching potential (left). The screenshot the interactive map generated from the qtm() function highlights Whiteladies Road as somewhere that would benefit from a new cycleway (right).

The method has some limitations: in reality, people do not travel to zone centroids or always use the shortest route algorithm for a particular mode. However, the results demonstrate routes along which cycle paths could be prioritized from car dependency and public transport perspectives.

The results may look more attractive in an interactive map, but what do they mean? The routes highlighted in Figure 13.8 suggest that transport systems are intimately linked to the wider economic and social context.

The example of Stoke Bradley is a case in point: its location, lack of public transport services and active travel infrastructure help explain why it is so highly car-dependent. The wider point is that car dependency has a spatial distribution which has implications for sustainable transport policies (Hickman, Ashiru, and Banister 2011).

13.9 Future directions of travel

This chapter provided a taste of the possibilities of using geocomputation for transport research, and explored some key geographic elements that make-up a city’s transport system with open data and reproducible code. The results could help plan where investment is needed.

Transport systems operate at multiple interacting levels, meaning that geocomputational methods have great potential to generate insights into how they work. There is much more that could be done in this area: it would be possible to build on the foundations presented in this chapter in many directions. Transport is the fastest growing source of greenhouse gas emissions in many countries, and is set to become “the largest GHG emitting sector, especially in developed countries” (see EURACTIV.com). Because of the highly unequal distribution of transport-related emissions across society, and the fact that transport (unlike food and heating) is not essential for well-being, there is great potential for the sector to rapidly decarbonize through demand reduction, electrification of the vehicle fleet and the uptake of active travel modes such as walking and cycling. Further exploration of such ‘transport futures’ at the local level represents promising direction of travel for transport-related geographic and computational research.

Methodologically, the foundations presented in this chapter could be extended by including more variables in the analysis. Characteristics of the route such as speed limits, busyness and the provision of protected cycling and walking paths could be linked to ‘mode-split’ (the proportion of trips made by different modes of transport). By aggregating OpenStreetMap data using buffers and geographic data methods presented in Chapters 3 and 4, for example, it would be possible to detect the presence of green space in close proximity to transport routes. Using R’s statistical modeling capabilities, this could then be used to predict current and future levels of cycling, for example.

This type of analysis underlies the Propensity to Cycle Tool (PCT), a publicly accessible (see www.pct.bike) mapping tool developed in R that is being used to prioritize investment in cycling across England (Lovelace et al. 2017). Similar tools could be used to encourage evidence-based transport policies related to other topics such as air pollution and public transport access around the world.

13.10 Exercises

E1. What is the total distance of cycleways that would be constructed if all the routes presented in Figure 13.8 were to be constructed? - Bonus: find two ways of arriving at the same answer.

E2. What proportion of trips represented in the desire_lines are accounted for in the route_cycleway object? - Bonus: what proportion of trips cross the proposed routes? - Advanced: write code that would increase this proportion.

E3. The analysis presented in this chapter is designed for teaching how geocomputation methods can be applied to transport research. If you were to do this ‘for real’ for local government or a transport consultancy, what top 3 things would you do differently? E4. Clearly, the routes identified in Figure 13.8 only provide part of the picture. How would you extend the analysis to incorporate more trips that could potentially be cycled? E5. Imagine that you want to extend the scenario by creating key areas (not routes) for investment in place-based cycling policies such as car-free zones, cycle parking points and reduced car parking strategy. How could raster data assist with this work? - Bonus: develop a raster layer that divides the Bristol region into 100 cells (10 by 10) and provide a metric related to transport policy, such as number of people trips that pass through each cell by walking or the average speed limit of roads, from the bristol_ways dataset (the approach taken in Chapter 14).