Promoting active mobility: Evidence-based decision-making using statistical models

Shifting traffic to active transport modes (eg. walking/cycling) poses one of the most promising ways of tackling the persisting challenges that arise from motorized traffic. However, planning and policy making in walking and cycling domains is frequently impeded by a small-scaled and heterogeneous...

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Bibliographic Details
Published in:Journal of transport geography 2019-10, Vol.80, p.102541, Article 102541
Main Authors: Hackl, Roland, Raffler, Clemens, Friesenecker, Michael, Kramar, Hans, Kalasek, Robert, Soteropoulos, Aggelos, Wolf-Eberl, Susanne, Posch, Patrick, Tomschy, Rupert
Format: Article
Language:English
Subjects:
Active mobility
Active transport
Computer simulation
Decision making
Demographics
Demography
Domains
Evidence-based planning
GIS
Infrastructure
Mathematical models
Mobility
Political commitment to set actions
Regional policies
Settlement structure
Spatial data
Statistical analysis
Statistical modeling
Statistical models
Target recognition
Traffic models
Traffic planning
Transport planning
Transport policy
Walking/cycling determinants
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Summary:Shifting traffic to active transport modes (eg. walking/cycling) poses one of the most promising ways of tackling the persisting challenges that arise from motorized traffic. However, planning and policy making in walking and cycling domains is frequently impeded by a small-scaled and heterogeneous political landscape that rarely acts based on evidence thus limiting cost-effectiveness and target achievement. This paper proposes a largely data-driven planning approach that builds upon aggregated statistical models explaining walking and cycling modal shares. In addition to investigating a comprehensive set of influencing factors in relevant fields such as environment, climate, infrastructure or demographics, we bring attention to the role of political and administrative commitment in aggregated modal share modeling. Results suggest that our holistic approach is feasible both methodologically and in terms of its applicability in planning practice. As a first step towards evidence-based decision making the incremental effects of individual planning measures can be simulated and thus be used to rank options according to their effectiveness. Another outcome lies in the data-driven identification of spatial target areas for specific agenda setting in terms of awareness, mobility behavior, infrastructure, settlement structure and other planning-relevant domains.
ISSN:0966-6923
1873-1236
DOI:10.1016/j.jtrangeo.2019.102541