The impact of reserve capacity on public transport network resilience
2013 (English)Conference paper (Refereed)
The resilience of the transport system is acknowledged as an important policy objective. Resilience refers to the extent to which a system is affected by various disturbances, and its capability to recover from such disturbances and restore its level of performance. Public transport networks (PTN) are subject to recurring service disruptions. However, most studies on transport network resilience have focused on the physical degradation of the road network. Hence, their findings have limited transferability to the PTN context. Previous studies on PTN resilience have considered vulnerability in terms of connectivity reliability. Graph theory principles were used to analyze the impact of network structure on robustness with respect to random and intentional attacks. Such analysis allows the comparison of alternative network design properties. However, it does not capture many of the PTN features that we believe are essential for analyzing its resilience.The underlying principles of PTN design and operations make it fundamentally different from road networks and potentially more vulnerable. PTN are usually less dense than the underlying road network, resulting in fewer alternative paths. Moreover, PTN operate close to capacity due to the increasing marginal operation cost during the peak period. In addition, PTN exercise discontinuity in time and space, inducing varying and stochastic waiting, walking and transfer times. Stochastictravel times arise from the inherent and interdependent underlying sources of uncertainty. Another matter thatneeds to be taken into account is that PTN are often multimodal, consisting of several independent infrastructures. As a result of these characteristics, service disruptions in the PTN have wider direct implications compared to the road network due to theescalating impacts on service availability and capacity further downstream. We develop an analysis framework for PTN resilience. The framework integrates stochastic supply and demand models, dynamic route choice and limited operational capacity. Moreover,the plausible correlation between degraded capacities among network elements is captured through the dynamic modellingof network performance. The criticality of a link is evaluated as the increase in system travel time due to a capacity reduction of the link. In general, criticality depends on the flow on the link and the availability of alternative paths in the PTN. We analyze the influence of the capacity of alternative paths on the criticality of a link. High volume to capacity ratios on neighboring links suggest that the effects of the initial disruption can cascade to the surrounding network and lead to severe impacts for many travellers. Further, we analyze the potential of increasing network resilience by increasing capacity on alternative links in response to disruptions. This implies operational strategies such as increasing the frequency on existing lines, or running replacement lines for the disrupted line. This analysis thus enables the evaluation of alternative mitigation measures designed to improve network resilience.
Place, publisher, year, edition, pages
Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-138235OAI: oai:DiVA.org:kth-138235DiVA: diva2:680637
NECTAR 2013 International Conference DYNAMICS OF GLOBAL AND LOCAL NETWORKS; São Miguel Island, Portugal, 16-18 June, 2013
TSC import 2319 2013-12-172013-12-182013-12-182014-03-19Bibliographically approved