Delivering a light rail system into service: Considerations for effective asset management

2014 ◽  
Author(s):  
A. Knott ◽  
M. Hack ◽  
A. Cope
Keyword(s):  
Asset Management ◽  
Light Rail ◽  
Rail System

Application of Artificial Neural Networks in Predicting the Degradation of Tram Tracks Using Maintenance Data

2020 ◽  
pp. 845-864
Author(s):  
Sara Moridpour ◽  
Ehsan Mazloumi ◽  
Reyhaneh Hesami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asset Management ◽  
Management Strategies ◽  
Operating Cost ◽  
Light Rail ◽  
Ann Model ◽  
Track Geometry ◽  
Artificial Neural ◽  
And Performance

The increase in number of passengers and tramcars will wear down existing rail structures faster. This is forcing the rail infrastructure asset owners to incorporate asset management strategies to reduce total operating cost of maintenance whilst improving safety and performance. Analysing track geometry defects is critical to plan a proactive maintenance strategy in short and long term. Repairing and maintaining the correctly selected tram tracks can effectively reduce the cost of maintenance operations. The main contribution of this chapter is to explore the factors influencing the degradation of tram tracks (light rail tracks) using existing geometric data, inspection data, load data and repair data. This chapter also presents an Artificial Neural Networks (ANN) model to predict the degradation of tram tracks. Predicting the degradation of tram tracks will assist in understanding the maintenance needs of tram system and reduce the operating costs of the system.

An RFID-Based Fallen Object Detection System: A Case Study of Hong Kong’s Light Rail System

2018 ◽  
Vol 2 (2) ◽  
pp. 55-67 ◽  
Author(s):  
C. H. Wu ◽  
G. T. S. Ho ◽  
K. L. Yung ◽  
W. W. Y. Tam ◽  
W. H. Ip
Keyword(s):  
Object Detection ◽  
Detection System ◽  
Light Rail ◽  
System A ◽  
Rail System

Evaluation of Substation Capacity Using Headway Shift and Perturbation Parameters

2018 ◽  
Author(s):  
Bih-Yuan Ku ◽  
Ching-Hsiang Chang
Keyword(s):  
Maximum Power ◽  
Light Rail ◽  
Case Scenario ◽  
Worst Case ◽  
Rail System ◽  
Rail Systems ◽  
Perturbation Parameters ◽  
Two Parameters ◽  
Operation Schedule ◽  
Traction Power

The variations in the operation timetable or schedule of an electrified transit rail system can lead to substantial fluctuations in power demands of its traction power network. This paper studies the correlation between the maximum power demands and timetable perturbations for electrified transit rail systems. Specifically, the operation schedule uncertainties are quantified as two parameters: headway shift and headway perturbation. A computation algorithm is introduced to illustrate how to use these two parameters to obtain the worst case scenario to obtain maximum power demand of traction power substations. Also a special type of catenary-free light rail system is used as an example to illustrate the algorithm and numerical results.

DC Frame Fault and Ground Fault Field Testing on TriMet Portland Light Rail System

2005 ◽  
Author(s):  
Kinh D. Pham ◽  
Thomas Heilig ◽  
Kai Looijenga ◽  
Xavier Ramirez
Keyword(s):  
Field Testing ◽  
Light Rail ◽  
Ground Fault ◽  
Rail System

Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City

2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Light Rail ◽  
Rail System ◽  
Total Travel Time ◽  
Vehicle Fleet ◽  
Event Based

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.

Sign in / Sign up

Export Citation Format

Share Document