Establish a Time-Dependent Mass Rapid Transit Timetable: A Case Study for the Kaohsiung Mass Rapid Transit System

2012 ◽  
Author(s):  
Shou-Ren Hu ◽  
Chao-Tang Liu
Keyword(s):  
Operating Cost ◽  
Temporal Distribution ◽  
Time Dependent ◽  
Total System ◽  
Rapid Transit ◽  
Transit System ◽  
Solution Algorithms ◽  
The Government ◽  
Mass Rapid Transit

The operation of a mass rapid transit (MRT) should consider the balance between total system costs and service level. For a MRT system, the main service is to provide passengers with the cost-affordable mobility, under the regulation of MRT service indicators; the government sets the minimum standards to ensure a certain level of MTR services. Thereby, how to establish an optimal operating timetable is one of the important operational issues for a MRT system. In the past research concerning MRT operations, most researches focused on the issues of train delay, energy saving, route design or overall system operating regards, and placed less focus on the optimal timetable problem. In the Kaohsiung Mass Rapid Transit (KMRT) system, the total ridership has not reached a predicted level, but the service provided in terms of the Train Service Plan (TSP) is more than needed. Therefore, a time-dependent time table for the KMRT system is crucial to minimize total system cost while maintaining the certain level of train service. In this research, we aim to develop a time-dependent timetable model which is able to dynamically adjust train schedule depending on the passenger spatio-temporal distribution demands during daily operation. The developed model will be solved by minimizing both the operating cost and passenger waiting cost. Finally, numerical case study and sensitivity analysis will be conducted to demonstrate the feasibility and effectiveness of the developed models and solution algorithms.

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.

Deadhead Minimization with a Flexible Facility Locator Tool

2021 ◽  
pp. 036119812110444
Author(s):  
Kara Todd ◽  
Freyja Brandel-Tanis ◽  
Daniel Arias ◽  
Kari Edison Watkins
Keyword(s):  
Open Source Software ◽  
Operating Cost ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Flexible Tool ◽  
The Subject ◽  
Transit Agencies ◽  
A Site

As transit agencies expand, they may outgrow their existing bus storage and service facilities. When selecting a site for an additional facility, an important consideration is the change in bus deadhead time, which affects the agency’s operating costs. Minimizing bus deadhead time is the subject of many studies, though agencies may lack the necessary software or programming skill to implement those methods. This study presents a flexible tool for determination of bus facility location. Using the R dodgr package, it evaluates each candidate site based on a given bus network and existing depots and calculates the network minimum deadhead time for each potential set of facilities. Importantly, the tool could be used by any transit agency, no matter its resources. It runs on open-source software and uses only General Transit Feed Specification (GTFS) and data inputs readily available to transit agencies in the U.S.A., filling the accessibility gap identified in the literature. The tool is demonstrated through a case study with the Metropolitan Atlanta Rapid Transit Authority (MARTA), which is considering a new bus depot as it builds its bus rapid transit network. The case study used current MARTA bus GTFS data, existing depot locations, and vacant properties from Fulton County, Georgia. The tool evaluated 17 candidate sites and found that the winning site would save 29.7 deadhead hours on a typical weekday, which translates to more than $12,000 daily based on operating cost assumptions. The output provides important guidance to transit agencies evaluating sites for a new bus depot.

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