Metro systems : Construction, operation and impacts

Peer reviewedPublisher PD

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Saving Time and Making Cents: A Blueprint for Building Transit Better

Cities, states, and metropolitan areas across the United States are looking to invest in a range of public transit projects in order to connect people to jobs and economic opportunity, reduce greenhouse gas emissions from vehicles, and shape development patterns.  According to one estimate, the United States invested about $50 billion in new transit projects in just the last decade.1 These include underground subways in Los Angeles, commuter rail lines along the Front Range near Denver, a streetcar in downtown Atlanta, light rail lines in suburban Phoenix, and bus rapid transit in Richmond, Virginia, among many others.While these projects are as diverse as the country itself, they all have one thing in common: increased scrutiny over their costs and timelines to build. A few very visible projects have reinforced the narrative that rail transit investments have systemic issues that are endemic to the United States.This all begs the questions: Is this true? If so, why? And what should we do about it?These are precisely the questions Eno set out to answer through this research, policy, and communications project to analyze current and historical trends in public transit project delivery. We convened a set of advisors and conducted in-depth interviews with key stakeholders to understand the drivers behind mass transit construction, cost, and delivery in the United States. A comprehensive database of rail transit projects was created and curated to compare costs and timelines among U.S. cities and peer metropolitan areas in Western Europe and Canada. Through this quantitative and qualitative approach, we developed actionable recommendations for policy changes at all levels of government as well as best practices for the public and private sectors

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Methods for risk and resilience evaluation in interdependent infrastructure networks

Urban infrastructure plays a key role in the structure and dynamics of every city. Besides ensuring the sustainability of communities and businesses, high-quality infrastructure services are crucial for generating jobs and attracting capital investments. Modern infrastructure systems are highly interconnected to enhance efficiency and safety of operations; however, the interconnections increase the risks of cascading failures during extreme events, such as natural disasters, acts of terrorism, and pandemics. Not only are the normal operations interrupted during such events, but prolonged operational disruptions in infrastructure services also have debilitating effects on emergency response and economic recovery in affected regions. With the emergence of new threats and intensifying climate change, the resilience of infrastructure systems has become a necessity rather than a choice for our cities. As with any resource allocation problem, potential resilience investments require identifying priorities and evaluating project alternatives. Appropriate resilience indicators can be used to rank and prioritize infrastructure components and systems as well as to evaluate the efficacy of resilience interventions. The dissertation proposes five indicator-based methodological frameworks to assist decision-makers in analyzing the intrinsic risks and resilience in large-scale interdependent infrastructure networks. For generic interdependent networks, an agent-based simulation approach is adopted. In this approach, the interdependent network is modeled as a weighted bi-directed network where nodes represent infrastructure components and links denote the interconnections. For evaluating the risks of cascading failures and the network's resilience, a hybrid risk measure based on the well-known Inoperability Input-Output Model (IIM) using expert judgments is developed. In the process, to handle the issue of epistemic uncertainty associated with subjective infrastructure dependency data, a method based on possibility theory is also proposed. Later, the hybrid risk measure is extended to develop two resilience indexes for quantifying the criticality and susceptibility of infrastructure components and ranking algorithms are presented. In addition, the hybrid risk measure is combined with socio-economic characteristics obtained from census data to develop a priority index to quantify the risks of cascading failures in various urban communities. With regard to infrastructure-specific networks, the dissertation developed infrastructure ranking and prioritization methods for two distinct transportation systems, specifically road networks, and marine port systems, based on empirical disaster data. For characterizing the resilience of road networks, the dissertation proposed three indicators based on the concepts of resilience triangle and extreme travel time observations. The dissertation combined time series decomposition techniques with anomaly detection algorithms to segregate disaster effects from normal traffic patterns. For characterizing the risks of natural hazards to port systems, the dissertation employed disaster impact data along with international trade data and identified the ports with the highest risks.Civil, Architectural, and Environmental Engineerin

人・ユーザー中心の移動サービスと群集マネジメントのためのモデリング，シミュレーションと最適化

Tohoku University博士（情報科学）thesi

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Proceedings of the 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction

Proceedings of The 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction. The conference was held at Queensland University of Technology (QUT), Brisbane, Australia from 8-10 July 2013. The event title for the 9th Annual Conference was: Risk-informed Disaster Management: Planning for Response, Recovery and Resilience. All papers were double blind peer reviewed and the Proceedings were published online in March 2015