12.2 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 12.1, which shows a diversity of transport infrastructure, for cycling, public transport, and private motor vehicles.

Figure 12.1: Bristol’s transport network represented by colored lines for active (green), public (railways, black) and private motor (red) modes of travel. Blue border lines represent the inner city boundary and the larger Travel To Work Area (TTWA).

Bristol is the 10^th largest city council in England, with a population of half a million people, although its travel catchment area is larger (see Section 12.3).It has a vibrant economy with aerospace, media, financial service and tourism companies, alongside two major universities.Bristol shows a high average income per capita 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 because of the speed of this mode, around 3-4 times faster than walking (with typical speeds of 15-20 km/h vs 4-6 km/h for walking).There is an ambitious plan to double the share of cycling by 2020.

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 to decide 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.