Optimization of Train-Speed Trajectory of Mass Rapid Transit Systems

2007 ◽  
Author(s):  
Bwo-Ren Ke ◽  
Nanming Chen
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Rapid Transit ◽  
Signaling System ◽  
Ant System ◽  
Transit System ◽  
Transit Systems ◽  
Train Speed ◽  
Ant Colony Optimization Algorithms ◽  
Mass Rapid Transit

This paper presents a method for optimizing train-speed trajectory between two neighboring stations of a mass rapid transit system. This research uses the MAX-MIN ant system of the ant colony optimization algorithms to minimize energy consumption based on the framework of a moving-block signaling system of the mass rapid transit system. The alignment gradient is considered for energy saving. Several constrains, for example acceleration/deceleration and jerk of the train, as well as the average train speed, are also addressed according to the requirements of practical systems. In conclusion, this method will be used to design an operational system.

Commuter-Centric Resilience Index for Rapid Transit System Networks: Case Study of the Singapore Mass Rapid Transit Network

2017 ◽  
Vol 2648 (1) ◽  
pp. 111-116
Author(s):  
Jian Sheng Yeung ◽  
Jason B. P. Lee ◽  
Yun Han Wee ◽  
Keng Seng Mak
Keyword(s):  
Rapid Transit ◽  
Transit System ◽  
Transit Systems ◽  
Transit Network ◽  
The Public ◽  
Commute Time ◽  
Resilience Index ◽  
Rail Line ◽  
Mass Rapid Transit

Rapid transit systems (RTSs) will increasingly play an important role in the daily commute. However, RTSs are complex systems and are susceptible to degradation over time, and recurring RTS service disruptions are inevitable. Therefore, resilience should be considered in the design of an RTS network, to provide commuters alternative paths that enable them to work around service disruptions. This paper proposes a commuter-centric resilience index for RTS networks that is based on the concept of an acceptable commute time. The proposed index was applied to the Singapore Mass Rapid Transit network, and the findings revealed that the introduction of each new rail line increased the resilience of the RTS network. Ring lines or orbital lines appeared to be most effective in improving network resilience. The resilience index can also be determined for individual stations to help planners identify gaps in the RTS network and to provide useful insight for land use and transport planning. The proposed index would be applicable to RTS networks in other cities or regions, but while information on an RTS network can be sourced from the public domain, computation of the index requires the corresponding commuter trip data.

scholarly journals Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rong Hu ◽  
Yi-Chang Chiu ◽  
Chih-Wei Hsieh ◽  
Tang-Hsien Chang ◽  
Xingsi Xue ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Training Dataset ◽  
Traffic Prediction ◽  
Rapid Transit ◽  
Transit System ◽  
Transit Systems ◽  
Passenger Traffic ◽  
Mass Rapid Transit

In this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classification task. However, in this process, the training dataset usually ended up being imbalanced. To address this dataset imbalance, our research proposes re-sample Recurrent Neural Network. A case study of the California Mass Rapid Transit System revealed that the model introduced in this work could timely and effectively predict passenger traffic of MRT. The measurements of passenger traffic themselves were also studied and showed that the new method provided a good understanding of the level of passenger traffic and was able to achieve prediction accuracy upwards of 90% higher than standard tests. The development of this model adds value to the methodology of traffic applications by employing these Recurrent Neural Networks.

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