Measuring political commitment in statistical models for evidence-based agenda setting in nonmotorized traffic.

Abstract

When investigating national and international transport policies of the last decade, an ever increasing emphasis on promoting non-motorized transport modes such as walking or cycling can be identified, aiming at reaching multiple political targets (eg. reducing pollution, increasing health or lowering land consumption). However, despite substantial financial efforts being put into infrastructural or awarenessraising activities, achieving the desired modal shift towards active mobility remains a challenge. This is frequently due to unclear cause and effect patterns between active mode shares and their determinants, which in turn leads to uncoordinated or highly fragmented initiatives that impede target-oriented planning. An internationally adopted approach to overcome this problem is applying aggregated statistical models that explain modal choice involving multiple regression techniques and hypothetical covariates. Still, general critique against these models points out that important intangible soft factors such as attitudinal characteristics of the local population or mind-sets and political commitment of decision makers are not duly reflected. Also, for Austria there is currently no systematic holistic approach to explain spatial variance in active travel shares on the scale of municipalities. Hence the main objective of our research is to design a comprehensive macroscopic model-based approach for the quantitative explanation of modal split shares in active travel modes in Austria. In our approach we attach great importance to the inclusion of soft factors in order to contribute novel findings on the dynamics behind active travel. The research outcomes will aid decision makers and planners in their question where and more specifically, how to effectively invest into active mobility by revealing key soft factors and intangible determinants of active travel mode shares alongside a broad range of more known, traditional factors. Based on this evidence-based decision support approach it is possible to simulate impacts of actions when aiming at locally promoting active travel modes.