교통수요 예측을 위한 활동기반 접근 방법: 경향과 적용현황 고찰

Four-step travel-demand modeling based on a trip-level has been widely used over many decades. However, there has been a wide variance between forecasted- and real-travel demands, which leads less reliable on the model implications. A primary reason is that person’s real travel behavior is not properly captured throughout the model developments. An activity-based modeling (ABM)approach was proposed and developed toward increasing the accuracy and reality of person’s travel behavior in the U.S. since 1990’,and stands as a good alternative to replace the existing trip-based approach. The paper contributes to the understanding of how the ABM approaches are dissimilar to the trip-based modeling approach in terms of estimation units, estimation process, their pros and cons and etc. We examined three activity-based travel demand model systems (DaySim, CT-Ramp, and CEMDAP) that are most commonly applied by many MPOs (Metropolitan Planning Organization). We found that the ABM approach can effectively explain multi-dimensional travel decision-makings and be expected to increase the predictive accuracy. Overall, the ABM approach can be a good substitute for the existing travel-demand methods having unreliable forecasts.