Thesis ; Online: Demand-responsive Disruption Management in Mass Transit Systems
An Agent-Based Simulation Model to Assess Disruptions and Resilience-Enabling Rescheduling Measures
2021
Abstract: Transit systems are indispensable to the growing urban population. However, severe unforeseen and unprecedented disruptions place planners and operators in a position where typical risk mitigation and reliability improvements cease to provide manageable ... ...
| Abstract | Transit systems are indispensable to the growing urban population. However, severe unforeseen and unprecedented disruptions place planners and operators in a position where typical risk mitigation and reliability improvements cease to provide manageable solutions. Instead, resilience assessment may lead the way towards an alternative perspective on handling disruptions. Firstly, it does not necessarily predispose that incidents are known or predictable. Moreover, it conceptualises that a system will undergo a number of phases during the draw-down and draw-up cycle following an adverse event, that each can be assessed and exploited for system improvements. This resilience perspective has been the premise of this thesis and drove development of computational models for the assessment of possible disruption management and decision-making strategies that remedy the impact of adverse events in transit systems. By taking resilience assessment as a key requirement, computational models need to capture the complete and dynamic draw-down and draw-up cycle during a disruption. At the same time, this thesis emphasizes that system performance ought to be, at least partly, expressed in terms of the inherent level-of-service that a transit system provides to passengers. Any evaluation or optimisation of system planning and operational rescheduling measures thus requires an explicit treatment of passengers and their interaction with the transit system supply. In addition, uncertainties related to variability of demand and supply as well as limited or lack of knowledge about current and future system conditions are prevalent. On the one hand, this means that models need to be able to capture these uncertainties. On the other hand, planning strategies and operational measures need to account and exploit these uncertainties to better cope whenever disruptions impinge on the network. In order to tackle the aforementioned challenges, this thesis starts by analyzing and reproducing the user demand patterns via the development of a ... |
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| Keywords | Transport Simulation ; Agent-based modelling ; Disruption modeling ; Resilience assessment ; info:eu-repo/classification/ddc/620 ; info:eu-repo/classification/ddc/004 ; info:eu-repo/classification/ddc/600 ; Engineering & allied operations ; Data processing ; computer science ; Technology (applied sciences) |
| Subject code | 380 |
| Language | English |
| Publisher | ETH Zurich |
| Publishing country | ch |
| Document type | Thesis ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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