Study On Inhabitant Trip Distribution Model Based On Maximum Information Entropy

The dissertation relies on National Natural Science Fund Supported Project "Research on Complexity and Models of Inhabitants Reorganization and Trip Distribution", Jilin Province Preeminence Youth Fund Sustentation Project "Research in Macro-Traffic Flow Theory" and Supported by Foundation for University Key Teacher by the Ministry of Education Project "Research on Theory and Models of Inhabitants Reorganization and Trip Distribution". The paper mainly studies the models based on the maximum information entropy theory of inhabitant trip distribution, calibrating the parameters and validating the models by the data. The full paper is composed of five chapters. The studied emphases are Chapter Two, Three and Four. And Chapter Four is kernel part in the paper.It is well known that our city changes planned economy system into market economy gradually since the innovation and open up. Our economy system, labor distribution system, land use system and housing system all takes biggish change in the change. The innovation of housing system releases employee dorm housing from its former restriction, therefore the layout of housing form a uniform commercial house system. The change has an impact on a part of inhabitants who inhabited in employee dorm housing formerly, as a result, they rechoose residences which results in inhabitant reorganization and trip distribution in the city. Since our city develop and change quickly now, traffic planning can't deviate from economy movement in the city, especially realty trade which is regarded as economy increase dot by the government. Because trip generation, trip distribution, modal split and traffic assignment will all change as the innovation of housing system changes, scientific theoretical system is need to be basis during traffic planning. Forecasting the former changes can let planning need traffic trip demand in the future. Reasonably and effectively forecasting the principle of inhabitant trip distribution have important meaning and referred merit for land use planning, traffic planning and global layout planning in the city. The models which are adopted at large in order to forecast inhabitant trip distribution at home and overseas presently mainly include growth factor methods, gravity models, intervening-opportunities model etc. The theoretical basis of growth factor methods is that the full or part observed trip matrix all has uniform growth rate, trip pattern almost have no change at present and in the future. The methods are simple to understand, need whole base-year trip matrices and are heavily dependent on the accuracy of the base-year matrix. The most serious limitation is that the methods do not take into account changes in transport costs due to improvement in the network. Therefore, these methods only suit to short planning in which traffic crisis changes very small or cursorily forecasting trip distribution. The gravity models originally generated from an analogy with Newton's gravitational law and start from assumptions about group trip-making behavior and the way this is influenced by external factors. The models mainly are used to study future-year trip forecast when the biggish changes occurs in the network. Its advantages lie in the introduced deterrence function. The considered factors are more all-around than the growth factor methods. The model is very sensitive to traffic deterrence parameters and precise to forecast trip distribution. In addition, the model can be used when actual survey materials aren't complete. The disadvantage of the gravity model lies in that the amount of trip distribution may inclines infinite when traffic deterrence approaches 0. Therefore, the model is unsuitable to compute trip distribution of short distance. The intervening opportunities model is stronger than the gravity model for the basis of building model. It is interesting because it starts from different first principles in its derivation: it uses distance as an ordinal variable instead of a continuous cardinal one as in the gravity model. It explicit...