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This study examines the dual impact of the Fresno Area Express (FAX) Q Line, a 15.7-mile Bus Rapid Transit (BRT) system launched in 2018, on the Fresno housing market and passenger satisfaction. The Q Line, designed to modernize public transit,features eco-friendly compressed natural gas (CNG) vehicles, real-time passenger information, and improved service efficiency.Housing market analysis focused on residential properties sold between 2012 and 2024, utilizing Geographic Information System(GIS) mapping to segment properties into three regions: the Q Line corridor, an outer buffer zone, and the rest of Fresno. Results indicate no statistically significant increase in property values within the Q Line corridor, challenging assumptions that public transit improvements substantially influence housing prices. Passenger satisfaction was assessed using FAX survey data and advanced machine learning models to identify key predictors. Statistical independence tests revealed no significant differences in satisfaction based on age, gender, employment status, or education. However, larger households reported higher satisfaction, while lower-income passengers expressed dissatisfaction. Machine learning models highlighted eight key factors influencing satisfaction, including audio-visual quality, value, comfort, closeness to home, and driver helpfulness. The findings underscore the importance of inclusive, passenger-centered transit services to enhance satisfaction while addressing disparities among lower-income riders. Policy recommendations encourage continued investment in public transit improvements without expecting significant housing market impacts. By prioritizing the identified satisfaction drivers, FAX can further improve the Q Line’s service quality and better meet the community’s needs, ensuring its long-term success.
Yertai Tanai
Yertai Tanai is an Associate Professor of Decision Sciences at the Craig School of Business, California State University, Fresno. His research spans a diverse range of fields, from conceptual modeling in supply chain operations to the application of machine learning in various industries and domains. His work reflects a commitment to bridging theoretical frameworks with practical solutions, providing innovative insights into complex business challenges.
Kamil Ciftci
Kamil Ciftci is an Assistant Professor in the Department of Information Systems and Decision Sciences (ISDS) at California State University, Fresno. His research interests include supply chain, logistics and transportation management, operations management in healthcare, business analytics, applied operations research, and decision making under uncertainty.
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