The Staten Island Rapid Transit

2013 ◽  
pp. 223-248
Author(s):  
Roger P. Roess ◽  
Gene Sansone
Keyword(s):  
Rapid Transit ◽  
Staten Island

Handling Passengers on a Rapid Transit Railroad—II

1911 ◽  
Vol 71 (1839supp) ◽  
pp. 194-195
Author(s):  
J. Vipond Davies
Keyword(s):  
Rapid Transit

A Staten Island Cottage

1892 ◽  
Vol 14 (2build) ◽  
pp. 17-17
Keyword(s):  
Staten Island

Rapid Transit in the Future

1909 ◽  
Vol 68 (1753supp) ◽  
pp. 89-90
Keyword(s):  
Rapid Transit ◽  
The Future

A Staten Island Cottage

1891 ◽  
Vol 12 (5build) ◽  
pp. 66-66
Keyword(s):  
Staten Island

Game Model of the Route Selection for Bus Rapid Transit

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Hua-Wei Gong ◽  
Wen-Zhou Jin ◽  
Xiao-Dong Zang ◽  
Qiang Luo
Keyword(s):  
Bus Rapid Transit ◽  
Game Model ◽  
Route Selection ◽  
Rapid Transit ◽  
Selection For

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

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