12.1 Introduction

In few other sectors is geographic space more tangible than transport.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 geographic 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 geospatial activity.It involves traversing continuous geographic space between A and B, and infinite localities in between.It is therefore unsurprising that transport researchers have long turned to geocomputational methods to understand movement patterns and that transport problems are a motivator of geocomputational methods.

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

• 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 12.3.
• 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 12.4.
• Routes: these are lines representing a path along the route network along the desire lines defined in the previous bullet point.We will see how to create them in Section 12.5.
• 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 12.6.
• Route networks: these represent the system of roads, paths and other linear features in an area and are covered in Section 12.7. 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.These can be represented computationally thanks to software such as MATSim, which captures the dynamics of transport systems using an agent-based modeling (ABM) approach at high 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 most 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: they are usually represented as desire lines connecting zone centroids (nodes), they can be allocated onto the route network as routes, and are made by people who can be represented as agents.

Transport systems are dynamic systems adding additional complexity.The purpose of geographic transport modeling can be interpreted as simplifying this complexity in a way that captures the essence of transport problems.Selecting an appropriate level of geographic analysis can help simplify this complexity, to capture the essence of a transport system without losing its most important features and variables (Hollander 2016).

Typically, models are designed to solve a particular problem.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 13 demonstrates another application of geocomputation:prioritising the location of new bike shops.There is a link between the chapters because bike shops may benefit from new cycling infrastructure, demonstrating an important feature of transport systems: they are closely linked to broader social, economic and land-use patterns.