Font Size: a A A

Study On Multi-Objective Distribution Of Urban Rail Transit Stations

Posted on:2016-02-16Degree:MasterType:Thesis
Country:ChinaCandidate:S HeFull Text:PDF
GTID:2272330461972155Subject:Transportation planning and management
Abstract/Summary:PDF Full Text Request
In recent years, China’s prosperous economy has provided great vitality for transportation.20 cities, such as Beijing, Shanghai and Guangzhou, have built subways by December 31,2014. There are 81 subway lines with 1732 stations, and the total length is about 2624km. In spite of fruitful achievements of China’s urban rail transit construction, many problems arise. For example, qualitative analysis weighs more than quantitative analysis in area of stop selection; factors are unclear when locating stops; when determining station coverage and station spacing, lack of theoretical support. Hence, it’s of great importance to come up with a reasonable location idea of urban rail transit station.According to the domestic and foreign research, current situation, development, trends and problems of China’s urban rail transit are reviewed in this paper. Based on this, this paper proposed research methods and technical route. Urban rail transit station classification and distribution factors were analyzed. Reasonable rail station spacing range is determined after reviewing the domestic and foreign research of station spacing.This paper studies how to distribute stations on an established line with determined original and final station. To solve this problem, three phases of station distribution method is firstly proposed, then reasonable station spacing is set as constraints, and a bi-objective optimization model is built with maximal station coverage and net income.E-Constraint Method is chosen based on comparative analysis of traditional classical multi-objective optimization algorithm. The multi-objective optimization problem is switched into a single-objective optimization withε-Constraint Method. The simulated annealing algorithm is applied to solve the model, and it is coded on Matlab. This method has been successfully applied to a specific example to prove the feasibility of the model and algorithm design. It has a certain reference value for the distribution of urban rail transit station.
Keywords/Search Tags:Urban rail transit, Stations distribution, Multi-objective optimization model, Simulated annealing algorithm
PDF Full Text Request
Related items