System for estimating seaport planning year traffic generative amount based on inverse calculation of goods series

Abstract

The invention discloses a traffic generation amount prediction system in a seaport planning year based on cargo back stepping, the technical scheme comprises the following steps: 1, a relational graph of a traffic generation amount and various traffic characteristic parameters and a traffic amount flow chart which is led by various cargo throughput are built, and current situation traffic characteristic parameters which are needed are collected. 2, the current situation traffic generation amount of a container truck, a common freight car and a bus is predicted. 3, the predicted current situation traffic generation amount is compared with the traffic generation amount which is obtained based on a current situation traffic investigation. 4, above related parameters are calibrated to obtain a group of best parameter values through a genetic algorithm. 5, prospect factors are comprehensively considered, the current situation traffic characteristic parameters of various cargo are amended, thereby obtaining the traffic characteristic parameters in a planning year. 6, various cargo public road transportation volume, the traffic characteristic parameters in the planning year and the current situation in the planning year of a building area are considered, and the prediction of the traffic generation amount in the planning year is completed based on cargo back stepping traffic amount.

Description

technical field [0001] The invention relates to a method for forecasting seaport road traffic demand by using information technology. Background technique [0002] With the development of the foreign economy, more and more attention has been paid to the planning and construction of seaports. To build a first-class international port, the traffic planning work in the port area must be solved. Traffic forecasting is the key to traffic planning. Is traffic forecasting scientific and accurate? Conformity to reality will directly affect the benefits of the traffic planning system. At present, the most widely used road traffic demand forecasting system is the four-stage traffic demand forecasting system. The input of the "four phases" is external factors such as urban land use, economic and social demographic data, and its prediction is essentially to divide the traffic demand prediction tasks that need to be completed in urban road traffic planning into four sub-tasks to complet...

AI Technical Summary

Problems solved by technology

The above-mentioned traditional "four-stage" road traffic forecasting system cannot be simply applied to the forecasting of road traffic demand inside the seaport. Therefore, a road traffic demand forecasting system must be established for the characteristics of road traffic in the port area.

[0003] In the existing technology, the traditional traffic forecasting models include the growth rate method, the gravity model method, the opportunity model method, etc. Although these methods have relatively high accuracy, they require a large amount of data, and the modeling and calibration process is also very complicated.

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