What are successful projects?

Revenue to capital ratios

Slowman, et al. (2014) investigated what the recipe for success was for sustainable travel, especially with respect to the share of revenue and capital spend. They found that the optimal spend on revenue was variable, depending on the size of the project and stage, but a general 40:60 revenue:capital ratio being a good rule of thumb. As shown in Figure 2, revenue is expected to be curved over the period of the project; for example, early high revenue spend to build culture, low revenue spend in the mid-point during the capital build and a high revenue spend at the end to increase uptake of infrastructure. There is also the expectation that government grants may be more revenue heavy as the Local Authority would front more of the capital costs but the overall project spend would still resemble the 40:60 revenue: capital rule of thumb. Figure 2 shows the optimal benefit to cost ratio modelled from hypothetical data using real world examples.

Figure 2 – Benefit to cost ratio by revenue to capital ratios
Figure 2 – Benefit to cost ratio by revenue to capital ratios

Source: (Slowman, et al., 2014)

Transport for London (TfL) international comparison

In 2014, TfL commissioned an international comparison of cycling infrastructure best practice. Much of the differences between the UK and international practice relate to the treatment of cyclists at junctions and crossings. In many jurisdictions, their legislation allows turning on the nearside on a red light as well as cyclists and pedestrians crossing together at informal crossings. It is argued these policies reduce the delays in cycling which is one of the significant factors in cycling uptake. While in the US all vehicles can turn right on a red light (barring NYC), in certain areas in France they have instead opted to give this exemption to only cyclists. Other legislative differences include creating ‘home-zone’ areas where cyclists have the priority in traffic over motor vehicles, or allowing cyclists to travel against the traffic flow in a one way street.

TfL’s research looks at a range of options and considers if they would be legally permissible in the UK and if they would be worth implementing. It also notes important differences between UK cycle infrastructure and the infrastructure of successful cycling cities, which they attribute to being caused by not taking cycle traffic seriously. These include:

  • Part-time cycle lanes, where vehicles can use or park on the lane at certain times of the day, were found very rarely.
  • Cyclists Dismount signs only found in the UK.
  • Token cycle lanes which were too narrow were only found in the UK.
  • They did not find cyclists having to give way to motor traffic at side street crossings or car park accesses etc. in international cities.
  • They did not find any arbitrary or abrupt ends to cycle lanes/tracks.
  • They did not observe any cycle lanes/tracks ending with a hazardous merge into busy general carriageways.

Smarter Choices Smarter Places – Scotland

In Scotland a sustainable travel pilot programme, Smarter Choices Smarter Places, showed promising results (Transport Scotland, 2013). The programme was estimated to have cost £15 million in total and saved residents £9 million a year in travel savings, between £10.6-£46 million in health savings depending on the model and £0.9 million worth of emissions reduced over the course of the programme from 2009-2013. Table 1 shows the changes in the Smarter Choices Smarter Places (SCSP) areas with most of the benefits coming from increased walking and reduced car driving

Table 1: Percentage point change in trip mode shares
SCSP Walking Cycling Bus Car driver Car passenger Train Taxi
Barrhead +14.8 +0.3 -0.6 -18.9 +1.6 +0.2 +2.8
Dumfries +7.6 +0.7 -0.9 -7.4- -1.3 +0.2 +0.8
Dundee +2.4 +0.8 -4.3 -1.9 +2.7 +0.3 -0.1
Glasgow East End +5.1 -0.4 -6.5 -1.6 +3.5 -1.1 +0.5
Kirkintilloch/Lenzie +5.1 -0.3 +7.4 -11.4 +1.3 -1.0 _1.4
Kirkwall +0.3 -0.5 -0.1 -3.1 +3.0 0.0 -0.1
Larbert/Stenhousemuir +21.4 +0.4 +0.8 -19.4 -5.0 -0.1 +2.3

Recommendations from the Sustainable Travel Towns

In England, a similar programme was undertaken through the Sustainable Travel Towns (Urban Transport, 2011). The Sustainable Travel Towns succeeded in increasing walking and cycling with a reduction in car driving. Darlington was also a Cycling Demonstration Town which resulted in higher cycling uptake than the average. There were also several recommendations that came from the Sustainable Travel Towns programme (Sloman, et al., 2010):

Interventions targeted at specific modes are most effective when accompanied by improvements in quality. This was evidenced by the failure of personal travel planning and other promotional work to reverse the decline in bus use in Darlington in the absence of service improvements.

Delivery of effective Smarter Choice Programmes is staff-intensive. The teams delivering the programmes in the three towns were between six and 10 full-time equivalent staff, and all the towns acknowledged that these were not upper limits and they could readily have made use of greater capacity. It took time to recruit an effective team and bring new recruits ‘up to speed’ (with recruitment of a full team typically taking between six months and a year). This pointed to the importance of planning for a long-term programme (i.e. at least the length of the programmes in the three towns), rather than expecting to achieve results within a couple of years.

This is echoed by an investigation of success in the Netherlands by Harms, et al. (2015). They found that improving the organization and implementation of cycling policies seems to positively impact the effectiveness of cycling policy. Specifically, formulating and implementing interventions that can be measured and monitored; having a high degree of adaptability of policy, allowing opportunities for experimental measures; and having high levels of citizen participation and the presence of strong leaders (like mayors or other public figures). This suggests that an established team with local knowledge and connection would likely be more successful than a prescribed intervention.

Case study: Seville

Seville achieved significant success in increasing modal share between 2004-2011, spending €32 million to build 120km of continuous segregated cycling network. The network was built on the premise of being accessible to everyone and visible to all from the road, with its quick building (the first 77km built over 2 years in 2006) being considered part of the success as the cycle routes were not taken over by mopeds or pedestrians instead. The resulting modal shift was around 9% of the all journeys being made by bike in 2011, up 5% in 2007 and negligible figures pre-program (Marqués, et al., 2015).

Cycling Demonstration Towns (CDT), Cycling Cities and Towns (CCT)

In tandem with the Sustainable Travel Town Programme the CDT programme was developed which was subsequently expanded to the CCT programme (Sustrans, 2017). The average result by 2011 was 24%-29% increase in cycling in the towns as counted by automatic cycle counters. Figures 3-4 and Table 2 show the changes in the target areas.

“The results vary across the towns. The analysis has not identified a clear pattern of which factors determine the extent of impact, but obvious factors that differed between the towns included the nature and extent of delivery (including the capital and revenue split), the target groups, the profile and extent of support for the initiatives that were introduced, changes in political support at different stages of the programme, baseline levels of cycling and baseline levels of car dependence, amongst other factors. The varied degrees of success are not necessarily surprising, as we know that travel behaviour is complex and difficult to influence, and that cycling is strongly influenced by contextual issues.” (Sustrans, 2017)

Figure 3 – Change in counts recorded by automatic cycle counters in six Cycling Demonstration Towns
Figure 3 – Change in counts recorded by automatic cycle counters in six Cycling Demonstration Towns

Showing change relative to base line for cycling demonstration towns phase an cycling city and towns phase in Aylesbury, Darlington, Exeter, Brighton and Hove, Derby and Lancaster with Morcambe Source: (Sustrans, 2017)

Figure 4 – Change in counts recorded by automatic cycle counters in 12 Cycling Cities and Towns
Figure 4 – Change in counts recorded by automatic cycle counters in 12 Cycling Cities and Towns

Showing change against base line in 2007, 2008, 2009, 2010 and 2011 in Blackpool, Colchester, Southport, Greater Bristol, Leighton, Stoke-on-Trent, Cambridge, Southend, Woking, Chester, Shrewsbury and York Source: (Sustrans, 2017)

Table 2 – Total change in counts recorded by automatic cycle counters in six Cycling Demonstrations Towns and 12 Cycling Cities and Towns
CDTs/CCTs Count in final year compared to baseline* Absolute change in average daily count per counter between baseline and final year* Number of automatic counters showing an increase in cycling
All CDTs~ - 129% 81 (of 118)
Aylesbury 106% +4 (68 to 72) 9 (of 19)
Brighton and Hove 119% +97 (503 to 600) 7 (of 13)
Darlington 159% +29 (50 to 79) 12 (of 19)
Derby 117% 15 (85 to 100) + 10 (of 15)
Exeter 145% +44 (99 to 143) 21 (of 26)
Lancaster w Morecambe 129% 49 (170 to 220) +22 (of 26)
All CCTs~ 124% - 137 (of 193)
Blackpool 109% +7 (87 to 95) 4 (of 9)
Cambridge 109% +44 (495 to 540) 9 (of 17)
Chester 121% +34 (163 to 197) 6 (of 10)
Colchester 119% +21 (111 to 132) 9 (of 14)
Greater Bristol 140% +104 (260 to 364) 29 (of 31)
Leighton 135% 14 (40 to 55) + 5 (of 13)
Shrewsbury 115% +17 (118 to 135) 16 (of 21)
Southend 117% +32 (185 to 217) 4 (of 7)
Southport 130% 15 (50 to 65) +10 (of 10)
Stoke-on-Trent 162% +19 (31 to 51) 13 (of 17)
Woking 126% +26 (99 to 125) 8 (of 10)
York 106% 13 (209 to 222) +24 (of 34)

Baseline=2005 for all CDTs except Brighton and Hove, for which it is 2006; baseline=2007 for all CCTs except Cambridge and Southport, for which it is 2009. ‘Final year’=2011 for all CDTs, and for all CCTs except Blackpool and Southend, for which it is 2010. For ‘count in final year compared to baseline’, baseline=100%. Change figures reported are from the analysis without the use of a factor for poor weather conditions (see full monitoring report for figures illustrating adjusted data).

~ Percentage changes for ‘all CDTs’ and ‘all CCTs’ are the unweighted mean of the percentage change values for each town

Source : (Sustrans, 2017)