Life cycle CO₂ footprint reduction comparison of hybrid and electric buses for bus transit networks

2022 ◽  
Vol 308 ◽  
pp. 118354
Author(s):  
Antonio García ◽  
Javier Monsalve-Serrano ◽  
Rafael Lago Sari ◽  
Shashwat Tripathi
2015 ◽  
Vol 06 (11) ◽  
pp. 1197-1218 ◽  
Author(s):  
Márcio de Almeida D’Agosto ◽  
Cintia Machado de Oliveira ◽  
Fabiana do Couto Assumpção ◽  
Ana Carolina Peixoto Deveza

2016 ◽  
Vol 27 (06) ◽  
pp. 1650064 ◽  
Author(s):  
Ailing Huang ◽  
Jie Xiong ◽  
Jinsheng Shen ◽  
Wei Guan

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.


2021 ◽  
Vol 81 (ET.2021) ◽  
pp. 1-14
Author(s):  
Fabio Porcu

Although public transport buses may be considered a safe transportation mode, bus safety is a crucial issue from the perspectives of operators, passengers and local authorities owing to the relevant implications it generates. Therefore, assessing the risk of crashes on bus routes may help improve the safety performance of transit operators. Much research has identified patterns of bus crashes to understand the effects of many factors on the frequency and the severity of them. Conversely, to the best of our knowledge, the research measuring the risk of crashes in bus transit networks is seldom faced. This paper adjusts existing methods to assess the safety on bus transit networks by the integration of safety factors, prediction models and risk methods. More precisely, first, the methodology identifies several safety factors as well as the exposure risk factors. Second, this methodology specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes and models their relationships in a risk function. Third, this methodology computes the risk of crashes for each route and provides a ranking of safety performance. A real case study demonstrates the feasibility of this methodology using 3,457 bus crashes provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology could be implemented in a road traffic safety management system in order to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms.


Author(s):  
Mao-Chang Shih ◽  
Hani S. Mahmassani ◽  
M. Hadi Baaj

A heuristic model is presented for the design of bus transit networks with coordinated operations. Different from past solution methodologies focusing on conventional uncoordinated transit systems, this model addresses the design of transit networks with coordinated operations, using a transit center concept and incorporating a trip assignment model explicitly developed for coordinated (timed-transfer) systems. In addition, this model determines the appropriate vehicle size for each bus route and incorporates demand-responsive capabilities to meet demand that cannot be served effectely by fixed-route, fixed-schedule services. This model is composed of four major procedures: ( a) a route generation procedure (RGP), which constructs the transit network around the transit center concept; ( b) a network analysis procedure, which incorporates a trip assignment model (for both coordinated and uncoordinated operations) and a frequency-setting and vehicle-sizing procedure; ( c) a transit center selection procedure, which identifies the suitable transit centers for route coordination; and ( d) a network improvement procedure, which improves on the set of routes generated by the RGP. The model is demonstrated via a case-study application to data generated from the existing transit system in Austin, Texas.


Sign in / Sign up

Export Citation Format

Share Document