Analysis of Urban Rail Transit Seamless Transfer Standard
نویسندگان
چکیده
منابع مشابه
Developing Seamless Connections in the Urban Transit Network: A Look toward High-Speed Rail Interconnectivity
.................................................................................................................................... iii Acknowledgement ..................................................................................................................... v Table of
متن کاملThe Optimization Model and Algorithm for Train Connection at Transfer Stations in Urban Rail Transit Network
In urban rail transit network, the passenger transfer time depends on the train connection states in transfer stations, so the optimization of the connection relations of arrival and departure time among trains is significant to improve the level of transfer service. Here, with the psychology of waiting passengers taken into consideration, the cost function of transfer waiting times has been es...
متن کاملCoherent Network Optimizing of Rail-Based Urban Mass Transit
An efficient public transport is more than ever a crucial factor when it comes to the quality of life and competitiveness of many cities and regions in Asia. In recent years, the rail-based urban mass transit has been regarded as one of the key means to overcoming the great challenges in Chinese megacities. The purpose of this study is going to develop a coherent network optimizing for railbase...
متن کاملUrban Rail Transit System Operation Optimization A Game Theoretical Methodology
The Urban Rail Transit (URT) has been one of the major trip modes in cities worldwide. As the passengers arrive at variable rates in different time slots, e.g., rush and non-rush hours, the departure frequency at a site directly relates to perceived service quality of passengers; the high departure frequency, however, incurs more operation cost to URT. Therefore, a tradeoff between the interest...
متن کاملPassenger Flow Forecast Algorithm for Urban Rail Transit
To exactly forecast the urban rail transit passenger flow, a multi-level model combining neural network and Kalman filter was proposed. Firstly, ELAN neural network model was introduced to implement a preliminary forecast of the passenger flow. Then the Kalman filter was used to correct the preliminary forecast results, so as to further improve the accuracy. Finally, in order to validate the pr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: MATEC Web of Conferences
سال: 2016
ISSN: 2261-236X
DOI: 10.1051/matecconf/20168103002