Table 2: Sample of the OD (origin-destination) input dataset, representing the number of people who commute from locations within and between administrative zones (MSOAs). Note ‘Car’ refers to people who drive as their main mode of travel per OD pair, rather than people who travel to work as a passenger in a car. likelihood of cycling (Iacono et al., 2008) — and including terms to capture the interaction between distance and hilliness. Model fit is illustrated in Figure 2; see the Appendix for details and for the underlying equations. We also developed equations to estimate commuting mode share among groups not represented in the between-zone (‘interzonal’) OD data, e.g. those commuting within a specific MSOA (this is within-zone or ‘intrazonal’ travel), or those with no fixed workplace. This model of baseline propensity to cycle formed the basis of three of the four scenarios (Government Target, Go Dutch and Ebikes), as described in more detail in the next section. Four scenarios were developed to a range of explore cycling futures. These can be framed in terms of the removal of different infrastructural, cultural and technological barriers that currently prevent cycling being the natural mode of choice for trips of short to medium distances. They are not predictions of the future. They are snapshots indicating how the spatial distribution of cycling may shift as cycling grows based on current travel patterns. At a national level, the first two could be seen as shorter-term and the second two more 3.3 Scenarios of cycling uptake ## [1] FALSE 13 Number of commuters by main mode Area of residence Area of workplace Total Cycle Walk Car Other E02002361 E02002361 109 2 59 39 9 E02002361 E02002362 7 1 0 4 2 E02002361 E02002363 38 0 4 24 10 E02002361 E02002364 15 1 0 10 4 E02002361 E02002366 29 1 10 11 7