Sensitivity Analysis for the Optimal Automated Demand Responsive Feeder Transit System

Author(s):  
Amirreza Nickkar ◽  
Young-Jae Lee ◽  
Mana Meskar
2012 ◽  
Vol 46 (1) ◽  
pp. 131-139 ◽  
Author(s):  
Karthikgeyan Sivakumaran ◽  
Yuwei Li ◽  
Michael J. Cassidy ◽  
Samer Madanat

2021 ◽  
Vol 81 (ET.2021) ◽  
pp. 1-15
Author(s):  
Chaitanya Jasti Pradeep

Estimating ‘CO2 emission savings’ of a mass rapid transit system (MRTS) project shall give an opportunity to earn carbon credits under clean development mechanism (CDM) of Kyoto protocol for ‘Non-Annex I’ countries like India. In this study, two methodologies for estimating ‘CO2 emission savings’ were demonstrated with a case study of Mumbai ‘metro line 1’. One considers actual reduction in vehicular traffic and the other considers the commuter shift to metro from other modes estimating the savings as 38.02 t/day and 27.63 t/day, respectively. Subsequently, sensitivity analysis was conducted to identify the optimal scenario for ‘CO2 emission savings’ supported by both the methods. Further, a breakeven scenario for ‘annual net CO2 emission savings’ after considering the indirect emissions due to electricity consumption by the metro system was also analysed as 1,008 t/year by the end 2019. These savings are analysed to further reach 32,537 t/year by 2025.


2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhengwu Wang ◽  
Jie Yu ◽  
Wei Hao ◽  
Tao Chen ◽  
Yi Wang

The last mile travelling problem is the most challenging part when using public transit. This study designs a high-freedom responsive feeder transit (HFRFT) system to serve at the transfer station, given vehicle routes, departure time, and service area based on demand. The proposed feeder transit system employs a travelling mode with multitype vehicles. In order to improve the operation of the HFRFT system, the optimization design methods are suggested for vehicle routes, scheduling, and service area. A mixed integer programming model and its hybrid of a metaheuristic algorithm are proposed to efficiently and integrally solve the vehicle routes and scheduling parameters according to the reservation requirements. A heuristic method is proposed to optimize the service area based on the equilibrium of system supply and demand. Case studies show that the mixed running mode of multiple models can significantly improve the seat utilization, which can also significantly reduce the number of departures and the average travel distance per passenger. The proposed service area optimization method is proved to be feasible to improve the last mile travel.


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