Analysis of Travel Plan Survey Methodology and Data Collected 56

All travel plans should include a set of SMART targets or objectives (Department for Transport, 2008, p. 10). A target or objective is SMART if it is Specific, Measurable, Attainable, Realistic and Time-bound. In a university travel plan a SMART target will refer to the level of travel behaviour change expected to be seen by a particular type of user (staff, student, visitor, supplier) for a certain type of trip (commuting,

business, delivery) over a specific time period.

When setting the targets for behaviour change the national guidance suggests that they should be expressed as, for example: “the number of commuter cars arriving per 100 employees” (Department for Transport, 2008, p. 19) since “this allows you to judge progress over time”.

This guidance focuses targets for workplace trips towards changes in the relative percentages of trips taken by each mode. So a reduction in car trips of 5% will be traded with an increase in trips by other modes that sum to a total of 5%.

Measurement of progress towards the plan’s targets should be performed by conducting travel surveys which examine travel behaviour characteristics of the organisational population at the start and the end of the time period (Forum for the Future, 2003, p. 35). The travel survey will aim to capture sufficient information about individual travel behaviour to make measurement/calculation of total trip levels possible. A sample travel survey included in the national guidance suggests that the following data items are obtained for employee commuting trips

(Department for Transport, 2008, p. 62): home post code, normal place of work, normal travel to work distance, normal arrival and departure time, working days/week, travel mode for each trip taken over last 7 days, alternative travel mode(s) used if main mode not available. The sample survey then continues with further questions related to the willingness to use more sustainable modes, the

barriers preventing the uptake of these modes and a set of questions related to business travel behaviour.

The main items collected through this survey pro-forma define all the attributes of a typical commuting trip: origin, destination, length, frequency, mode and timing.

Using this data the overall modal trip percentages required for assessing progress towards the targets specified in the travel plan can be calculated relatively easily.

The university travel plans which included data on travel behaviour expressed this as the percentage of trips by each mode, matching the national guidance. Most travel plans recognised the need to stratify trips by user type, and displayed a different set of modal split percentages for staff and student trips. Similarly when targets were specified for travel behaviour change these were generally specified as decreases (or increases) in the percentages of trips being undertaken by specific mode, and again these were stratified by user type.

The modal split percentage for the least sustainable trip mode, single occupancy vehicle (SOV) trips, for both staff and students was found in 46 of the travel plans³. Analysis shows that the staff SOV mode share varies widely by institution from a low of 18% (Bristol) to a high of 74% (East Anglia) and similarly for students from a low of 4% (York) to a high of 44% (Derby). This confirms the analysis of Enoch (Enoch, 2012, p. 2 and chapter 3) who proposes that each organisation will have a unique demand profile that depends on spatial conditions, local land use policy and transport system supply combined with organisational, locational and user travel behaviour characteristics.

The range of values in this data-set also suggests that mode share percentage comparisons across universities are meaningless since the figures represent

responses to the unique conditions existing at each institution, and that mode share percentages only have validity for measuring travel behaviour change over time within an institution.

3 The HESA environmental dataset contains figures for institutional staff and student SOV mode share. However, these appears to be of a poor quality with many obviously incorrect data points which differ from the figures given in the travel plans. Therefore the SOV mode share figures contained in the travel plans were felt to be more accurate.

What the figures do show, however, is that within the plans reviewed SOV use by staff always exceeds the

equivalent SOV mode share figure for students attending the same

institution, Graph 1. This is not surprising as students are less likely to have access to a vehicle, whilst the measures identified in the plans typically

discriminate against students and in favour of staff car users, discourage student car use through parking restrictions and the

denial of student parking permits for campus and halls of residence.

Of the 75 university travel plans that were analysed, 58 (77%) were found to include results from a travel survey. Three of these institutions based their analysis on the hosting local authority’s workplace travel survey data (Huddersfield, Leeds

Metropolitan, and Cambridge), whilst another three conducted a parking survey (Surrey, East Anglia and UCL). Three institutions only surveyed staff (City

University, Roehampton, Southampton Solent), although all three indicated that a student survey would be conducted in the future. In 11 cases (15%) no evidence of a survey was found even though a travel plan or transport policy measures were defined (Anglia Ruskin, Birmingham, Essex, Greenwich, Hull, Lincoln, Liverpool, LSE, Manchester Metropolitan, Oxford, West of Scotland). Of the remaining travel plans, 22 of them either included a pro-forma of their survey, or contained a sufficiently detailed analysis of the survey responses to allow the content of the survey to be inferred. Table 2 lists the institutions which specified their travel survey instrument together with the data items relevant to commuting trips to campus that were collected in each surveys.

The data collected through these surveys matches that specified in the DfT guidance, and whilst all surveys capture the main trip mode, most also ask about either the most frequently used alternative mode, the split of modes used across each day of the week, or request details of all legs in a multi-modal commuting trip chain. All but one of the surveys requested details of the commuting trip origin and trip distance, whilst 14 (66%) also included a question about trip duration. Trip

Graph 1 – UTR: Staff/Student SOV Modal Splits By Institution

frequency information was captured by 18 (81%) of the surveys in one of two ways.

Participants are asked either to specify the number of days on which the commuting trip is made, or to specify which days of the week the trip is made. 12 (54%) of the surveys ask the respondent to provide information on arrival and departure times. In 13 (60%) of the surveys the same set of questions related to the commuting trip are asked of both students and staff, whilst only 3 surveys asked students about their academic timetable and its relationship to the trips they made. None of the travel surveys analysed attempted to capture information on the level of student leave-and-return trips although two travel plans suggested that this behaviour might exist (Manchester, Warwick).

Table 2 – UTR: Student Travel Surveys, Data Items Captured

4.4 Discussion

This section identifies a number of themes that arise out of the analysis of the travel plan data described in section 4.3.