Systems Science Dept, Institute of High Performance Computing, A*STAR, Singapore, Singapore.
Analytics, Computing, and Complex Systems Laboratory, Aboitiz School of Innovation, Technology, and Entrepreneurship, Asian Institute of Management, Makati, Phillippines.
PLoS One. 2022 Apr 28;17(4):e0267222. doi: 10.1371/journal.pone.0267222. eCollection 2022.
Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore's rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world.
量化中断对快速交通弹性的影响对于交通规划至关重要。我们提出了一个快速交通系统的综合弹性评分,由四个指标组成,这些指标衡量了弹性的不同物理方面。这些指标是使用加权网络模型计算的,该模型纳入了车站的网络结构、线路容量差异和出行需求。我们的方法提供了对网络弹性的整体评估,并允许对不同情景(包括铁路扩展和需求变化)进行直接比较。我们将该方法应用于新加坡快速交通系统的多种配置中,证明了它在捕捉未来规划线路影响方面的有效性。我们还通过模拟研究展示了当需求变化时弹性的临界点是如何出现的。此外,我们还表明,通过将通勤需求重新分配到外围地区,可能会无意中降低系统的弹性。我们的方法可以很容易地应用于世界各地的其他快速交通系统。