Earticle

A correct understanding of hospital parking characters and parking behavioral regularities is very important to hospital parking planning, making parking management measures and improving the utilization of parking lot. The existing researches in parking behavior concentrate on parking behavior in central area, and the researches on hospital parking behavior are still not deep. Besides, the research methods mainly adopt disaggregate model which only considers driver’s observable manifest variables, neglecting the influence of mental latent variables on the results of parking choice, so the model’s explanatory ability is weakened. Aiming at the hot issue - hospital parking problem, based on the analysis of hospital parking character, this paper puts forward the latent variables of hospital parking mode choice. By improving the utility function of traditional ML disaggregate model, establishing three kinds of SEM-ML integration model which could describe car drivers’ characteristic manifest variables and subjective mental latent variables, and then applying examples to make comparison and analyze. The results show that: Compared with traditional ML model, the SEM-ML model has a better accuracy and explanatory ability. Among the three models, the SEM-ML3 integration model could describe the relationship between latent variables and manifest variables, latent variables and observable variable by structural equation, so it has a better accuracy and explanatory capability. The results validate that manifest variables (parking purpose) and mental latent variables (convenient level and feelings of parking) have effects on hospital parking mode choice. According to the analysis results, this paper proposes corresponding suggestions on planning and managing hospital parking from three aspects: convenience, comfort and safety.

본 논문에서는 철도 수요예측 문제의 유형을 목적에 따라 3가지로 분류하였고, 최근 철도자원을 재고관리 차원에서 접근하고자 하는 시각에 따라 분배모형으로써 적응필터를 사용하는 방법의 타당성에 대해 설명하였다. 또 철도 승객수요의 주요 특징을 분석하였으며, 철도 승객수요 예측의 요구사항 및 방법론을 대규모 재고관리 시스템의 일반적 요구사항에 따라 정리하였다. 영향인자에 대한 분석으로 요일별 계절변동 지수를 정량적으로 산정하였다. 적응필터를 이용한 철도 승객수요 예측의 예제를 제시하였으며, 예측에의 정확성에 대한 비교를 제시하였다.