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 ).

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.
• 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 .
• 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 ).
  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 ).ABM is a powerful approach to transport research with great potential for integration with R’s spatial classes (Thiele 2014; Lovelace and Dumont ), 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.

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 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.