Transit access and neighborhood segregation. Evidence from the Dallas light rail system

2018 ◽  
Vol 73 ◽  
pp. 237-250 ◽  
Author(s):  
Kilian Heilmann
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

Author(s):  
Bih-Yuan Ku ◽  
Ching-Hsiang Chang

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.


2005 ◽  
Author(s):  
Kinh D. Pham ◽  
Thomas Heilig ◽  
Kai Looijenga ◽  
Xavier Ramirez

2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow

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