Bus rapid transit service quality analysis : a case study of Hefei

2014 ◽  
Author(s):  
Xiyuan Liu
Author(s):  
Kara Todd ◽  
Freyja Brandel-Tanis ◽  
Daniel Arias ◽  
Kari Edison Watkins

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.


Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 625
Author(s):  
Cheng ◽  
Zhao ◽  
Zhang

The purpose of this study is to create a bi-level programming model for the optimal bus stop spacing of a bus rapid transit (BRT) system, to ensure simultaneous coordination and consider the interests of bus companies and passengers. The top-level model attempts to optimize and determine optimal bus stop spacing to minimize the equivalent costs, including wait, in-vehicle, walk, and operator costs, while the bottom-level model reveals the relation between the locations of stops and spatial service coverage to attract an increasing number of passengers. A case study of Chengdu, by making use of a genetic algorithm, is presented to highlight the validity and practicability of the proposed model and analyze the sensitivity of the coverage coefficient, headway, and speed with different spacing between bus stops.


2015 ◽  
Vol 50 (4) ◽  
pp. 473-488
Author(s):  
Tadeas Umlauf ◽  
Luis David Galicia ◽  
Ruey Long Cheu ◽  
Tomas Horak

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