Calibration of Rail Transit Assignment Model with Automated Fare Collection Data and a Parallel Genetic Algorithm

2014 ◽  
Vol 2442 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Wei Zhu ◽  
Ruihua Xu ◽  
Yue-Ping Jiang
Keyword(s):  
Genetic Algorithm ◽  
Rail Transit ◽  
Parallel Genetic Algorithm ◽  
Transit Assignment ◽  
Assignment Model ◽  
Collection Data

Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data

2015 ◽  
Vol 2526 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Wei Zhu ◽  
Feng Zhou ◽  
Jiajun Huang ◽  
Ruihua Xu
Keyword(s):  
Cluster Analysis ◽  
Large Scale ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Assignment ◽  
Collection Data ◽  
The Difference ◽  
Cluster Analysis Technique ◽  
Assignment Models ◽  
Automatic Fare Collection Data

Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature
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