TRAVEL DEMAND ANALYSIS WITH LOW-COST SURVEYS

ABSTRACT Travel Demand Analysis (TDA) plays a pivotal role in urban planning and transportation management. This study investigates the application of low-cost survey techniques in TDA to provide more accessible data collection alternatives without compromising data quality. Traditional TDA relies heavily on extensive, costly data-gathering methods such as household travel surveys and large-scale field observations, which often limit feasibility in budget-constrained environments. In contrast, emerging low-cost surveys, including online surveys, mobile apps, and social media analytics, are providing viable and economical options. This research assesses the effectiveness, cost-efficiency, and accuracy of these innovative survey methods by comparing them with conventional approaches. By gathering travel data from diverse socio-economic backgrounds, the study examines the potential for low-cost surveys to estimate travel patterns, vehicle kilometers traveled (VKT), and CO2 emissions accurately. Methodologically, this study designs a cost-effective survey tool, evaluates sample sizes and response rates, and employs various analytical techniques, such as regression analysis and mode choice modeling, to derive insights from the data. Findings reveal that low-cost surveys can provide reliable TDA data and that technology-enabled data collection can reach broader populations with minimal costs. However, challenges such as data quality, sample representativeness, and privacy concerns are prevalent. Comparative analysis across urban zones highlights variations in traffic patterns and emission levels, identifying hotspots like the Bolori Store Junction and Mala Roundabout for emissions reduction interventions. The study concludes by proposing targeted measures to enhance urban transport sustainability, emphasizing expanded public transit, car-sharing, and active commuting as critical strategies for CO2 emissions reduction.