Research shows that induced demand—ridership generated by improved level-of-service—can vary broadly, from near zero to 80% of the total ridership across high-speed rail projects. Such a wide range can yield very different economic figures which consequently have impacts on investment decisions. Because of data collection challenges and limited modeling techniques, however, this important aspect of intercity passenger transportation is not sufficiently understood. For example, many feasibility studies estimate induced demand by simply applying a fixed multiplier on top of demand diverted from other modes. This rough estimate is unreliable when the new service significantly improves the level-of-service. The most common alternative to the fixed-multiplier approach is to conduct a full travel demand model, which integrates data from multiple regions with multiple surveys. This approach is costly and time consuming, however, and as such is generally only practicable in the final stages of project evaluation. This paper aims to bridge the gap between these approaches to modeling induced demand by proposing a modeling framework which analyzes intercity induced demand effectively and efficiently, capturing most influential factors without requiring integration of incompatible regional datasets. The proposed framework consists of one single online survey which targets all residents in the study area and a behaviorally-consistent econometric model to derive induced intercity passenger demand. Derived induced demand results from the case study can enrich the literature on an innovative transportation mode which features ultra-speed.