Successful Travel Awareness Campaigns and Mobility Management Strategies

The main outcome is a set of integrated tools designed to make it easier to implement high quality MM measures:

1. MaxExplorer. A decision support guide for MM measures. This user-friendly tool supports decision makers and MM practitioners (in particular less experienced users) to select suitable MM measures according to specific target group characteristics and project location. More specifically this tool helps users to:

• select suitable measures for their organisation and situation;

• further develop projects and schemes at the local level with reference to the characteristics of the target group;

• benefit from the experience of experts in choosing appropriate measures;

• discover Mobility Management through a general overview of a wide range of measures;

• compare the effects of MM measures by providing the opportunity to look at the relevance and the possible efficiency of different types of measures in different situations, through a multi-criteria assessment;

• understand the variety of different MM measures, as MaxExplorer covers many common situations, from Company Mobility Plans to Walking Buses for Schools or Travel Awareness Campaigns.

• provision of a
  

  Policy implications

  The main recommendations relate to the development of a future fully-functional European Prospective Assessment Tool (PAT). This PAT should be developed as a combination of three primary elements that serve as the 'heart' of the predictive functioning of the model:

  • A set of demand elasticities for price- and service-based measures (including public transport fare discounts, public transport service expansion or new services, alternative mode incentives, etc.), referred to as 'harder' MM measures because they involve pricing or service provision.
  • A set of experiential relationships based on the benchmarking function of MaxEva or other comparative evaluation studies and their findings. These are referred to as 'softer' measures because they involve promotion and information. Comparative cross-sectional analysis from MaxEva or another dataset would provide a relationship in the form of average mode shift impacts from a given MM measure when applied to a certain target group.
  • Inclusion of a behavioural change module from MaxSem that provides results that are not strictly related to mode shift impacts. Rather this 'stage diagnostic' module would provide estimates of the proportion of the target population what would move between stages as a result of the offer of a given measure. As such, the PAT would estimate shifts to more sustainable modes (mode shift) and precursor behavioural shifts (stage shifts).

  The main features of this PAT should be:

  • a user-friendly interface and instructions, requiring no knowledge of 'transport modelling';
  • minimal user inputs and reliance on existing data (no new surveys needed);
  • easy navigation in model and simple means to save and report impact forecasts;
  • able to predict impacts of packages of measures by assessing interactive effects.

  The inputs of this PAT should include:

  • number of travellers and the type of the target group (company, target market, students, etc.);
  • average trip characteristics (travel time, trip distance, vehicle occupancy);
  • type of application setting (city centre, employment centre, suburban area, well/poor accessibility by foot, bike, public transport, etc.).

  The outputs should include:

  • baseline and projected mode shares and proportional changes;
  • baseline and projected vehicle trips and proportional changes;
  • baseline and projected vehicle kilometres of travel and prop