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Travel behaviour change evaluation procedures
Technical report
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4. Default diversion rates
4.1 Introduction
In order to value the benefits associated with a TBhC project, there needs to be an estimation of the likely impact that the project will have on mode share. The default diversion rates developed in this section are estimates of the absolute percentage point change for each of the main modes that are expected to be achieved by the main types of TBhC project.
Default diversion rates are estimated for work travel plans, school travel plans and household/community based projects. It was envisaged that there would be a number of different sets of default diversion rates for each of the TBhC project types. Individual projects would then be assessed against a set of criteria that would determine the appropriate set of diversion rates for that project. However, the number of default diversion rates actually recommended is fewer than originally anticipated because the statistical analysis of the available data on results of TBhC projects to date did not support greater disaggregation. The following sections discuss these findings in more detail.
4.2 Data collection
The starting point to estimating the default diversion rate values was the collation of information from the literature on TBhC projects that have been implemented. The information collected from the literature included:
- Before and after program mode share or the number of trips by mode
- Relative percentage change in mode shares or in the number of trips by mode
- Information relating to the reliability of the results, eg statistical significance, use of a control group
- Target population information
- Any information relating to the characteristics of a program area
- Measures that were implemented.
Unfortunately not all of this information is available for every project.
Workplace travel plans and school travel plans had the largest sample sizes, with household based programs having considerably fewer observations. Very little robust data was found on the impacts of other types of TBhC project such as teleworking and ridesharing.
The results reported in the literature are often an upper bound because they only report the observed behaviour changes for a certain subset of the population. For example, the reported diversion rate may be, say a 14% reduction in car as driver mode share amongst those who participated in the program. This fails to take account of the individuals that did not participate in the program (such individuals can be classified into two categories, those who did not respond/could not be contacted to participate in the program and those who chose not to participate in the program). The 14% diversion is an upper bound because for this same reduction to apply across the entire population requires the assumption that all non-participants would have made the same average change as those who participated. Basing default diversion rates only upon the upper bounds will result in a consistent overestimation of the likely benefits from a TBhC project.
To make a correction for this, a lower bound is calculated by adjusting the reported diversion rate for the program participation rate. The assumption for calculating the lower bound is that individuals in the target population that do not participate make no change in their travel behaviour and that they have average mode share prior to the implementation of a TBhC project.
Information about the TBhC program will disseminate throughout a community resulting in the actual diversion rate for a target population being between the upper and lower bounds. The analysis effectively assumes a mid point because it includes both upper and lower bound estimates from each case study.
4.3 Framework
For each of the types of TBhC projects there was a varying amount of information in the literature. Work travel plans had the greatest number of observations and the most detail on individual projects. The information for work travel plans and school travel plans was generally taken from a limited number of sources that presented results for a large number of different projects. The number of household based programs covered in the literature tended to be relatively small, with some discussed in numerous reports and papers.
It was initially planned to separate each of the program types into sub-groups. Characteristics of a workplace, school or household area would determine the applicable diversion rate from within a sub-group (such characteristics were the size of the company, accessibility to services and amenities, quality of public transport services, etc). After considerable evaluation this method was discarded, as there was insufficient data to determine statistically significant differences between the diversion rates that were achieved with different combinations of characteristics. This is an area for refinement in the future, once there is a more extensive database of information relating to these programs to enable statistically robust analysis.
An approach that assigned projects to default diversion rates based upon a scoring system is used in place of a statistical approach. A project’s score would be determined by, in the case of work travel plans, the measures to be implemented as part of the proposed travel plan. School Travel Plans are classified into default rates based upon whether the school is a primary or secondary school (intermediate schools were added later). Default rates for household/community based initiatives are based on the comprehensiveness of the programme and the standard of existing public transport services and cycle/walk facilities.
Development of this methodology suited work travel plans and household based initiatives. Work travel plans were divided into three sets of diversion rates, and household based initiatives into two (as there are significantly fewer examples of household initiatives). However, based upon experience with school travel plans in New Zealand and evidence from the literature, there are no identified characteristics that result in a school travel plan being more likely to achieve higher diversion rates than any other. Schools that are large or small, rural or urban, primary or secondary, in a socio-economically poor or rich area can all benefit from the implementation of a school travel plan and there did not appear to be any consistent pattern that any of these factors made a particular plan more likely to achieve higher diversion rates. From experience the only differentiation that can be made is that the modes which trips divert to differ between primary and secondary schools, providing a basis for classification of projects.
A special case is when primary and secondary schooling occurs at the same institution. It is considered that the appropriate methodology for determining diversion rates in this case is to calculate a weighted average diversion rate where the weight is the number of students in primary (secondary) to total students at the school. (Similarly for intermediate) This in effect evaluates the primary and secondary components individually. Such a method ignores any synergies (if any exist) of implementing a travel plan within a school with both primary and secondary students.
4.3.1 Workplace travel plans
A proposed project is scored against the measures listed below and then classified into a set of default diversion rates based upon the aggregate score:
- Car parking management strategies
- Public transport service improvements and/or public transport subsidies
- Improvements to walking/cycling facilities
- Promotion of ride sharing.
Evidence in the literature suggests that the most significant factors in achieving lower car as driver mode share are initiatives targeted at the availability of parking, and provision of an adequate substitute for car commuting. Parking management strategies and public transport service improvements or subsidies are the two types of measures that address these barriers most directly and are thus weighted more heavily.
Car parking management strategies
There are a wide range of measures that could fall into the category of a parking management strategy including, but not limited to, the introduction of car parking fees, parking cash out schemes and restricting/reducing the supply of car parking spaces. It is also important to consider the current parking situation, ie whether there is currently ample car parking space or whether parking availability is already constrained.
It is suggested that projects be given scores of zero, one or two for parking. A score of zero would be applicable if current parking arrangements adequately meet demand and a parking management strategy is not implemented as part of the plan. This reflects the fact that without a parking issue, and without the introduction of parking demand management there are fewer incentives for individuals to change.
If parking arrangements are already constrained in some way (ie there is a parking issue), it is more probable that individuals are seeking an alternative mode and that the travel plan will stimulate a change in mode. In this case a project should receive a score of one.
A travel plan that introduces a parking management strategy is also likely to deter people from driving to work (eg by charging for parking some individuals will consider it more attractive to travel by another mode) and should gain a score of one. Implementing a single parking management strategy is probably not as effective as the introduction of a number of measures (eg introducing parking charges, and reducing the number of car parks available for staff). However analysis of the case study data did not show sufficient evidence to justify a higher score for the introduction of a combination of parking management strategies.
It is also considered that the combination of an existing parking issue/constraint with the implementation of one or more parking management strategies should gain a score of two.
Public transport service improvement
Improvements to public transport systems could be through the provision of new bus/train routes, or through the introduction of new services along existing routes. The provision of a company shuttle bus could also count as a public transport system improvement.
Most of the evidence on work travel plans was from the United Kingdom, where companies are relatively larger than in New Zealand. As a result it is relatively easier to justify (financially) improvements to the public transport system in a work travel plan in the United Kingdom than in New Zealand. Therefore, two sets of diversion rates are estimated, one set for when there are no public transport service improvements as part of the travel plan and one set for when service improvements or subsidies are included.
If a workplace travel plan includes any such improvements to the public transport system (though it is not limited to those listed above) then a score of one is appropriate, and zero otherwise.
Public transport subsidies
Public transport subsidies could be either in the form of a subsidy to an operator or through fare subsidies. A score of one should apply if public transport subsidies are a measure to be included in a work travel plan, and zero otherwise.
Ride sharing
Ride sharing can be promoted in a number of ways, through the provision of preferential parking for car sharers or through the introduction of a ride sharing matching service or similar. It is considered that if the travel plan introduces a ride sharing measure (or a number of measures), then a score of one should apply, and a score of zero otherwise.
Improvements to cycling/walking facilities
Improving the conditions for walkers and cyclists will encourage the use of these two modes. Two common improvements to cycling and walking facilities are the improvement to onsite facilities (eg lockers, showers, bike storage etc.) and the improvement to external facilities (eg cycling paths/tracks/lanes). A score of one should apply if a cycling/walking measure is implemented and zero otherwise.
Table 7: Workplace travel plans — scoring and classification
| Parking management strategy | |||
|---|---|---|---|
| Is there a car parking constraint/issue? | No strategy | One or more parking strategies in travel plan | Parking strategies include: |
| No | 0 | 1 | Parking cash out |
| Yes | 1 | 2 | Parking charges |
| Parking management score | Reduced supply, etc | ||
| Are these attributes part of the Travel plan? (1 if yes, 0 if no) | |||
| Public transport service improvements | |||
| Public transport subsidies | |||
| Ride sharing matching service | |||
| Improved walking/cycling facilities | |||
| Total score (out of 6) | |||
| Diversion rate | Score | ||
| Low | 1 or 2 | ||
| Medium | 3 or 4 | ||
| High | 5 or 6 | ||
| Separate diversion rates are provided for projects that include public transport measures such as service improvements or fare subsidies | |||
The scoring system is intended to assign individual projects the correct magnitude of car as driver reduction. Within the case studies reviewed, the distribution of this percentage point change across the ‘to’ modes is influenced by the measures implemented by the travel plan. The experience from New Zealand workplace travel plans suggests that public transport service improvement measures are not as common as in the United Kingdom. So, for plans that do not include any public transport service improvements it is expected that the diversion to public transport will be similar to diversion to the other ‘to’ modes. For a project that does implement public transport service improvements the evidence suggests that a far greater proportion of the mode shift will be to public transport. Hence separate sets of diversion rates are derived for ‘with public transport measures’ and ‘without’.
4.3.2 Household/community based initiatives
After considerable investigation and trialling of various criteria we concluded that household programs cannot be categorised into groups with similar diversion rates based upon socio-economic or other characteristics of the area, or the measures to be implemented, because from the literature similar projects (eg TravelSMART) have been implemented in a number of different areas, and the diversion rates observed have varied from one area to the next with no consistent relationship to any of the above criteria.
It has been hypothesised within the literature that the socio-demographics of a target area influence the resulting mode shift. If this were the case, projects could be scored against a set of socio-demographic criteria and classified into different sets of diversion rates. Tests were carried out with the diversion rate survey data divided into three different sets of diversion rates (high, medium and low), and into two different sets (highest half and lowest half).
For these tests, household programs were scored against the following criteria:
- Car ownership/usage. It is intuitive to think that an area that has above average car ownership rates (or car usage) is likely to achieve a larger diversion rate from car as driver than an area with below average car ownership rates. The reasoning being that there is a high potential number of trips that can be diverted (although potential diversion also depends upon availability of suitable substitutes).
- Population density. Higher population density is likely to be associated with amenities and services being relatively more accessible. An area with higher population density has a higher potential to make more trips by environmentally friendly modes (which are more suited to journey of shorter distances). This is subject to a number of other factors, including the fact that there may already be low car usage in these areas.
- Public transport coverage/service. If an area has poor public transport service or coverage, then public transport is not a viable substitute to car use.
- Public transport usage. The existing usage of public transport could also determine the potential level of diversion away from car usage. If public transport services are already at capacity, then there is relatively less scope to achieve an increase in public transport mode share. All that might be achieved is that some individuals switch to public transport from cars, crowding out existing public transport users and forcing them to divert to car use, with no net change in travel behaviour. It may be the case that during the peak times public transport is operating at capacity but there is spare capacity in the off-peak. Evidence in the literature suggests that the majority of the change in travel behaviour occurs during off-peak periods, potentially coinciding with the times when public transport has spare capacity.
- Inner suburb. It is considered that an inner suburb is more likely to achieve a greater switch from car as driver than an outer suburb area due to the relatively higher level of public transport service, generally shorter journeys to work and relatively higher levels of congestion.
- Household size.
Findings
When we scored existing household/community projects against these criteria and assigned them to the high, medium, or low diversion rate groups accordingly there was no consistency between the default rate for the group to which a project was assigned and the actual diversion rates achieved in that project.
Perkins has undertaken a statistical analysis of the observed travel behaviour change against a set of commonly measured socio-demographic characteristics, such as those used to score the household based projects. Perkins’ paper analysed the results of the implementation of Travel Blending pilot programs in Adelaide, Australia (It should be noted that the majority of the reviewed literature focused on TravelSMART programs and not Travel Blending). The paper concluded that the characteristics that explained a significant amount of the variation in the total number of trips made by a household were unable to explain any significant amount of the change in travel behaviour, suggesting no relationship between socio-demographic characteristics and the diversion rates achieved by a project. The paper concluded that either the travel behaviour change is explained by a set of characteristics not currently measured or that the sample size was too small to determine any relationship. However, it was highlighted that those individuals who used cars the most tended to make the greater degree of positive change.
Three problems identified with the scoring approach for household/community diversion rates include:
- The (limited) statistical evidence suggests that the change in behaviour can not be explained by commonly measured socio-demographics
- The definition of a suitable average as a reference point for scoring (national average or regional average)
- The availability of the averages required to undertake the scoring process.
Given these limitations, it was decided that perhaps this approach could not be relied upon, given the uncertainty that is raised from the limited statistical evidence in the literature, and the availability of information relating to averages for the criteria to be assessed against.
It is recommended that a standard set of default diversion rates be adopted based on the average of all household/ community projects that have been undertaken and monitored. A low set of default rates is also provided based on the average of the bottom half of diversion rates achieved to account for any projects that may not implement the full range of initiatives that have become standard in Household based programs such as TravelSMART, or where public transport services or cycle/walk facilities are poor, with the decision to use the low set at the discretion of analysts. Once further information about the relationship between project area characteristics and the likely diversion rates is available, these values should be refined.
4.3.3 School travel plans
The project team’s experience and evidence from the literature suggest that there are few criteria against which to separate school travel plans into different sets of diversion rates. The most obvious way, though, is to provide a set of default diversion rates for primary schools and a separate set for secondary and intermediate schools. This approach is adopted.
One complicating factor is that of ‘combination’ schools which cover both primary and secondary schooling. In such cases, it is considered appropriate to consider the primary and secondary components separately. The primary school default values would be applied to the number of students in the primary school, and the secondary school default values are applied to the number of secondary school students. The benefits for the primary and secondary schools would be calculated separately, and then summed to form total benefits. This would in affect create a weighted average benefit.
4.4 Estimation of default values
Default diversion rates are presented as an absolute percentage point change in mode shares, chosen in preference to a relative change. The use of an absolute percentage point change is lends itself to simplified procedures, as the application of an absolute percentage point change does not require any prior knowledge of initial mode shares within a company, school or community. Use of a relative change would require the initial mode share to be known prior to the application for funding in order to estimate the likely diversion rate resulting from a project. Fortunately the evidence from analysis of TBhC projects implemented to date is that the percentage point change in mode share does not appear to be significantly related to the initial mode share.
For each project that had information on before and after project implementation mode shares, an absolute percentage point change was calculated for each mode. Percentage point changes for some projects could only be calculated for one or two modes, consequentially there are different numbers of observations for each mode. Where outliers are identified they are excluded from the analysis.
The use of percentage point changes for the default diversion rate values places a constraint on the analysis. In the derivation of default diversion rates it is necessary that the sum of the diversion rates for the ‘from’ modes equals, in magnitude, the sum of the diversion rates for the ‘to’ modes (and they will be opposite in sign). This ensures that total mode share sums to 100%.
4.4.1 Workplace travel plans
Within the literature changes in mode share were reported for car as driver, car as passenger, public transport, walking, cycling and car sharer, although not all modes were measured for every project. Thus, the number of observations for each mode varied, with the most observed for car as driver. Default diversion rate values could simply be estimated for each of these.
However, it is important to consider the benefits associated with each mode. Almost all the modes warrant their own individual diversion rate. For example, the benefits associated with cycling will differ from walking, due to different length of car journeys replaced, and the different level of physical activity. However, the distinction in benefits between car as passenger and car sharer is not so well defined. An individual who is a car sharer can also be classified as a car passenger. Primarily the difference between the two will be in average trip lengths. The difference between the average trip length for car as passenger and car sharer is considered to be marginal. It is assumed that there will be no difference in trip lengths for car sharer and car passenger trips. Hence, for evaluation purposes the two are the same, so car as passenger incorporates the diversion to car sharer.
The starting point for the estimation of the default diversion rates is with the calculation of descriptive (or summary) statistics of the data for each mode, as shown for car as driver in Table 8.
Table 8: Work travel plans – descriptive statistics for car as driver
| Statistic | Value |
|---|---|
| Mean | -7.2% |
| Standard error | 0.010 |
| Median | -0.054 |
| Mode | 0 |
| Standard deviation | 0.092 |
| Sample variance | 0.008 |
| Kurtosis | 0.068 |
| Skewness | -0.192 |
| Range | 0.443 |
| Minimum | -0.278 |
| Maximum | 0.165 |
| Sum | -5.655 |
An hypothesis test is conducted on the mean value to determine if it is significantly different from zero. It is calculated that the mean is significantly different from zero at the 5% level, with a 2 tailed p value of 3.86%. This confirms that, on average, Work Travel Plans result in a reduction in car as driver mode share.
Significance tests on the means for car as passenger, public transport, walking and cycling showed that none of these were significantly different from zero. On the face of it, this result could be regarded as negative and decrease the confidence in any default values estimated, but this is not necessarily the case. Consider a Work Travel Plan that implements subsidised bus fares (or subsidised public transport fares in general). The expected impact of this is that some staff would divert to using the bus. Staff diverting to bus will be diverted from car modes, and potentially from walking and/or cycling. Staff diverting from walking and cycling will lower these mode shares and increase public transport mode share.
As a second example consider the car as passenger mode share. An increase in car as passenger can be a positive result (if they have changed from car as driver) but a decrease in car as passenger can also be positive (if they shifted to environmentally friendly modes).
A plot of the initial mode share against percentage point change is used to determine if a relationship exists between the initial mode share and the percentage point change. This plot for car as driver is shown in Figure 7. There is clearly a range of percentage point changes at different mode shares, which suggests there is not a significant relationship between percentage point change and initial mode share.
Figure 7: Work travel plans — car as driver initial mode share vs percentage point change
The plots for each of the other modes also showed no obvious relationship between initial mode share and the percentage point change.
The first step in estimating the diversion rates is to sort the data for each project into the ascending order of car as driver. This results in the data for each project being placed in the ascending order of car as driver. The three sets of diversion rates are then estimated by calculating the mean for each third of the series (by number of observations). These are shown in Table 9.
Table 9: Work travel plans – lower, middle and upper third averages
| Car as driver | Car as passenger | Public transport | Walking | Cycling |
|---|---|---|---|---|
| 1.8% | -0.6% | 0.0% | -0.5% | -0.7% |
| -6.0% | 1.1% | 2.2% | 0.7% | 0.2% |
| -17.7% | 2.4% | 5.4% | 0.9% | 0.7% |
Table 9 shows two important points. Firstly, these results do not meet the constraint that the sum of the ‘to’ modes must equal the sum of the ‘from’ modes. To make this constraint hold, the excel ‘Goal Seek’ function is used to calculate a percentage change that when applied to the value for each mode results in the constraint being satisfied (if the value is positive it is increased, and if negative it is decreased).
A second adjustment is also made to the values in Table 9. The first set of diversion rates shows a negative impact from the travel plan, with an increase in car use and a decrease in the mode shares of environmentally friendly modes. These diversion rates are due to a few outliers where there was a significant increase in car trips. These outliers were removed for the calculation of default diversion rates.
The recommended sets of diversion rates for use in the evaluation of work travel plans are shown in Table 10 below. These diversion rates are applicable when there are Public Transport Service Improvements.
Table 10: Work travel plans — recommended diversion rates for projects with public transport service improvements
| Score | Car as driver | Car as passenger | Public transport | Walking | Cycling | |
|---|---|---|---|---|---|---|
| 1 or 2 | Low | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| 3 or 4 | Medium | -5.0% | 1.3% | 2.6% | 0.8% | 0.3% |
| 5 or 6 | High | -12.9% | 3.3% | 7.4% | 1.2% | 1.0% |
Combining the scoring approach for classification into the different sets of diversion rates with the recommended diversion rates, a sample of ten case studies from the literature were used as test cases of the framework. Nine out of the ten projects were correctly classified into the correct set, ie the diversion rate observed for car as driver was in the correct set. However, this process revealed that the distribution across the ‘to’ modes differed significantly from one project to the next.
An alternative set of distributions of the ‘to’ modes is also estimated based on the experience from the case studies reviewed. This second set of diversion rates is designed to take account of the fact that the New Zealand experience with Work travel Plans to date has not seen public transport service improvements, but also to allow for the possibility that in the future there will be such measures implemented. Estimation of the ‘without’ public transport service improvements diversion rates is based upon the ‘with’ group of diversion rates.
The estimation of medium and high sets of ‘without’ diversion rates are done by assuming that half the public transport diversions (in the default sets) is redistributed between walking and cycling. The amount that either walking or cycling receives is weighted by the relative sizes of the two in the default group of diversion rates. The resulting sets of alternative diversion rates are shown in Table 11. The same scores are used to classify projects into the high or low cases as for the ‘with public transport service improvements’ diversion rates.
Table 11: Work travel plans — recommended diversion rates for projects without PT service improvements
| Car as driver | Car as passenger | Public transport | Walking | Cycling | |
|---|---|---|---|---|---|
| Without Low | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
| Without Medium | -5.0% | 1.3% | 1.3% | 1.8% | 0.6% |
| Without High | -12.9% | 3.3% | 3.7% | 3.2% | 2.7% |
4.4.2 Household based programs
The reported results of Household based projects generally had a consistent set of modes, being car as driver, car as passenger, public transport, walking and cycling. However, one article reported only on the mode share of public transport that could be achieved through TBhC programs. The number of observations for household based project was significantly less than for Work Travel Plans, although a similar process in used to estimate the default values for the two sets of diversion rates, the average set and the low set.
There were a number of results reported for South Perth. The South Perth pilot study had data relating to the one month after and one year after project implementation. The South Perth full scale project also had results for mode share as well as the number of trips. There were a total of 22 observations. Including 4 sets of results for the South Perth experiences was considered to result in biased results (as the South Perth experiences have tended to be some of the most successful individualised marketing projects in Australia and Europe) so two sets were removed from the calculation of averages.
From the summary statistics, significance tests showed that the mean car as driver diversion rate is not significantly less than zero than at the 5% level. This result is affected by the very small sample size of 19. This further supported fewer rather than more sets of default diversion rates. As discussed under work travel plans, other modes not being significantly different from zero is not cause for concern. The plots of initial mode share versus percentage point change revealed no relationship between the two for all modes.
The two sets of default rates were estimated in a similar method to Work Travel Plans. The data for each mode was sorted into ascending order for the car as driver series. The standard set of diversion rates used the average for the whole sample, while the low set used the average value for the lower half of results (by number of observations). It was also necessary to adjust these values to meet the constraint of mode share summing to 100% (using the same process as for Work Travel Plans).
The two sets of default diversion rates that are recommended for Household based programs are shown in Table 12 below.
Table 12: Household programs — default diversion rates
| Car as driver | Car as passenger | Public transport | Walking | Cycling | |
|---|---|---|---|---|---|
| Low | -1.0% | -0.2% | 0.5% | 0.4% | 0.3% |
| Standard | -3.1% | -0.5% | 1.4% | 1.3% | 0.9% |
As discussed earlier it is recommended that the standard diversion rate profile based on the average of all case studies is used for most household TBhC projects. The low set of default rates, based on the average of the bottom half of diversion rates achieved, should be used for any projects that may not implement the full range of initiatives that have become standard in Household based programs such as TravelSMART, or where publictransport services or cycle/walk facilities are poor, with the decision to use the low set at the discretion of analysts.
4.4.3 School travel plans
The results of School Travel Plans were generally from projects implemented in the UK. Results were reported for car as passenger, walking, park and walk, cycling, public transport and car sharer. Park and walk is significantly different to walking, as it generally involves only walking a short distance to school and driving the majority of the distance. Park and walk is not as popular in New Zealand school travel plans. However, walking school buses are popular. A walking school bus walker is essentially the same as an independent walker, except that there is a coordinated walking approach that results in groups of students walking together in a supervised environment. For evaluation purposes it is considered that there is no difference in the benefits for independent walkers and a walking school bus walker.
The summary statistics for school travel plans showed that car as passenger was significantly different from zero at the 5% level (2 tailed p value of 1.73%) and that walking was not significantly different from zero. Public transport, cycling and car sharing had less than 10 observations each, too small for meaningful statistical analysis. The plots of initial mode share against percentage point change showed no relationship between the two.
Estimation of different diversion rates for primary and secondary schools could not be determined from the literature, with only a small number of observations for secondary schools. The modes considered important to New Zealand travel plans are car as passenger, walking (which incorporates both walking and walking school buses), public transport and cycling. Generally, a travel plan focuses on only one of these walking measures.
The mean percentage point change in car as passenger is used as the default ‘from’ diversion rate for both primary and secondary schools.
In the case of primary schools, the ‘to’ diversion rates are considered to be walking initiatives and cycling. Evidence suggests that public transport is not an important mode for primary schools, possibly due to the relatively short journey distances and parental concern for safety. The default value for Walking is estimated at 80% of the diversion rate of car as passenger, and cycling at 20%. The reason for the split is that the literature suggests there is a single mode that attracts the majority of the change, and that this is generally associated with walking initiatives.
Default diversion rates for secondary schools are estimated for walking initiatives, public transport and cycling. There is limited evidence on proportions of ‘to’ mode shares. Based on experience and judgement public transport is considered to account for most diversion and cycling is considered to achieve the least change. Results from future projects will provide evidence on which these assumptions may be refined.
The default diversion rate values for primary and secondary schools are shown in Table 13 below. The secondary school diversion rate profile is also considered appropriate for intermediate schools.
Table 13: School travel plans — default diversion rates
| Car as passenger | Public transport | Walking | Cycling | |
|---|---|---|---|---|
| Primary school | -9.0% | 0.0% | 7.5% | 1.5% |
| Secondary school | -9.0% | 5.0% | 3.5% | 0.5% |
Page created: 28 October 2008