Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Hospital Parking Character and the Latent Variables of Parking Choice
  2.1. The Analysis of Hospital Parking Character
  2.2. The Influence Factors of Parking Mode Choice
 3. The Establishment of SEM-ML Integration Model
  3.1. Model Assumption
  3.2. Establishing SEM-ML Integration Model
  3.3. Data Collection and Model Solution
 4. Case Analysis
  4.1. Effectiveness Test of Latent Variables and Observed Variables
  4.2. Comparative Evaluations of Effect between Traditional ML Model and SEM –ML Integration Model
  4.3. Analyzing the Structural Pattern and Measurement Pattern of SEM-ML Integration Model
 5. Concluding Remarks
 Acknowledgments
 References

키워드

저자

• Zeng Chao [ School of Civil Engineering & Architecture, Chongqing Jiaotong University, Chongqing, China ]
• Tang Boming [ School of Civil Engineering & Architecture, Chongqing Jiaotong University, Chongqing, China ]
• Xu Zhixiang [ School of Civil Engineering & Architecture, Chongqing Jiaotong University, Chongqing, China ]
• Liu Tangzhi [ School of Civil Engineering & Architecture, Chongqing Jiaotong University, Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605