An Assessment of Smart Urban Furniture Design: Istanbul Yildiz Technical University Bus Stop Case Study

Author(s):  
Gökçen Firdevs Yücel Caymaz ◽  
Kürşat Kemal Kul
2021 ◽  
Author(s):  
Sidnyaev Nikolay Ivanovich ◽  
Butenko Iuliia Ivanovna ◽  
Margaryan Tatyana Dmitrievna

2021 ◽  
Vol 5 (2) ◽  
pp. 738-753
Author(s):  
Guilherme Augusto Oliveira de Paula ◽  
Pillar Borges Rocha ◽  
Stéfhanny Maria Mendes Marinho ◽  
Bernardo Antonio Silva Ramos ◽  
Matheus Leoni Martins Nascimento
Keyword(s):  
Bus Stop ◽  

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wusheng Liu ◽  
Qian Tan ◽  
Lisheng Liu

The planning and operation of urban buses depend heavily on the time-varying origin-destination (OD) matrix for bus passengers. In most cities, however, only boarding information is recorded, while the alighting information is not available. This paper proposes a novel method to predict the destination of a single bus passenger based on bus smartcard data, metro smartcard data, and global positioning system (GPS) bus data. First, the attractiveness of each bus stop in a bus line was evaluated, considering the attractiveness of nearby metro stations. Then, the exploration and preferential return (EPR) model was employed to estimate the probability of a bus stop to be the alighting stop, i.e., the destination, of a passenger. The estimation result was obtained through a simulation based on the Monte Carlo (MC) algorithm. The effectiveness of our method was proved through a case study on the bus network in Shenzhen, China.


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.


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