Willingness to Pay for Active Mobility Infrastructure in a Thai University

This research examines road users' willingness to pay for enhanced active mobility infrastructure at King Mongkut's Institute of Technology Ladkrabang (KMITL), a suburban university campus in Bangkok, Thailand. The study addresses the need for sustainable transportation solutions in middle-income urban environments by analyzing factors that influence walking and cycling adoption among university community members. The research employed a comprehensive mixed-methods framework combining qualitative SWOT analysis, a stated preference survey of 400 participants, and binary logistic regression modeling. The analysis revealed that specific infrastructure improvements significantly increase the likelihood of active mobility adoption. Rest areas demonstrated the strongest positive association (OR=1.820, p=0.034), followed by CCTV security systems (OR=1.726, p=0.060), protective barriers separating pedestrians and cyclists from motorcycles (OR=1.608, p=0.086), and improved public transport connectivity (OR=2.192, p=0.005). Demographic analysis uncovered notable resistance patterns, with male participants (OR=0.512, p=0.096) and higher-income individuals (OR=0.114, p=0.004) showing reduced willingness to transition from motorized transportation. These findings reflect broader cultural preferences and socioeconomic factors that influence mobility choices in the Thai context. Using the Contingent Valuation Method, the study quantified potential behavioral changes, projecting an 8-16 minute daily increase in active mobility engagement. This enhancement would generate measurable health benefits for individuals and environmental improvements for the broader community. The research contributes valuable insights to the limited body of active mobility literature from Southeast Asian suburban contexts, where car and motorcycle dependency remains dominant. The findings emphasize that safety infrastructure and seamless connectivity are fundamental prerequisites for successful active mobility programs. The study's methodological approach, combining economic valuation through contingent valuation with statistical modeling via logistic regression, provides a replicable framework for similar investigations.