Freight Mode Choice Modelling with Aggregate RP Data and Disaggregate SP Data

과거 정부는 철도의 화물수송 분담률을 2020년까지 20% 수준으로 높인다는 목표를 제시하였지만, 국가교통DB에 따르면 화 물수송의 수단분담률(톤 기준)은 공로 80.8%, 해운 14.6%, 철도 4.6% 수준으로 화물차 수송 중심의 체계를 가지고 있으며, 이 러한 상태는 앞으로 더욱 심화될 것으로 보인다. 이에 따라 정부는 화물의 철도수송 분담률을 높이기 위해 ‘철도물류산업 육성 및 지원에 관한 법률’을 제정하고, 철도 수송증대를 위한 철도물류 인프라 종합투자계획 수립 등 다양한 정책을 마련하고 있다. 철도수송증대를 위한 다양한 정책들을 추진함에 있어서 우선 철도화물수요를 정확히 예측하고 이러한 예측결과를 통해 합리적 Abstract For accurate demand forecasting of railway logistics, we estimated intercity freight mode choice models based on the binary logit model and using production-consumption data from the Korea Transport Database. We estimated two types of models and compared the results by major item of railway logistics, such as container, cement, and steel: 1) The aggregate freight mode choice models are based on the revealed preference (RP) data and 2) The disaggregate models are based on the stated preference (SP) data. With respect to the container, the travel time variable was found to be statistically significant; however, the travel cost variable was not statistically significant in the RP model, while the travel cost variable was statistically significant in the SP model. For cement and steel, the travel cost variables were statistically significant but the travel time variables were not statistically significant in either the RP or the SP models. These results are inconsistent with results from previous studies based on SP data, which showed that the travel time variables were significant. Consequently, it can be concluded that the travel time factor should be considered in container transport, but that this factor is negligible for cement and steel transport.