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

Optimization of the Suburban Railway Train Operation Plan Based on the Zonal Mode

Traditional all-stop train operation mode cannot meet the demand of long travel distance and centralized travel of commuters very well. To meet this special travel demand, a zonal train operation mode based on “many-to-many” train stops is proposed. The coefficient of passenger exchange is used to locate suburban areas by depicting travel characteristics of commuters. Operational separating points within the suburban area are used as decision variables to analyze the combined cost components of this model, including passenger travel costs and railway operating costs. An integer programming model with the lowest overall cost is established, and the genetic algorithm is employed to solve it. The results proved good relative benefits in operation costs and travel time. And the sensitivity analysis of both coefficient of passenger exchange and passenger intensity has shown that the zonal operation mode is suitable for suburban railways with centralized travelers. However, the research also shows that when the passenger volume rose to a very high level, the number of zones would be limited by the maximized capacity of railway lines, which may cause the decline of the relative operational efficiency

Chicago Recovery Partnership Evaluation of the American Recovery and Reinvestment Act

From 2009-2011, the City of Chicago and Cook County received a total of $2.35 billion in funding provided by the American Recovery and Reinvestment Act [ARRA]. The stimulus money was allocated to seven areas: education, basic needs, transportation and infrastructure, housing and energy, public safety, broadband and workforce development. The Chicago Recovery Partnership Evaluation of ARRA analyzes the impact of the stimulus spending using a costbenefit analysis framework. This report evaluated$1.09 billion of total spending in Chicago and Cook County, resulting in net benefits ranging from -$173.9 to$2,740.2 million. The wide range in net benefits is attributed largely to education, which received over half of ARRA funding

Carbon Free Boston: Transportation Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: • Shift trips out of automobiles to transit, biking, and walking;1 • Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; • Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

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

Stop Plan of Express and Local Train for Regional Rail Transit Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible. Document type: Articl

Full Issue 14(4)

il saggio esamina compiutamente la disciplina dell'art. 19 d.lgs. n. 5/200