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

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Investigating the performance of transport infrastructure using real-time data and a scalable multi-modal agent based model

The idea that including more information in more dynamic and iterative ways is central to the promise of the big data paradigm. The hope is that via new data sources, such as remote sensors and mobile phones, the reliance on heavily simplified generalised functions for model inputs will be erased. This trade between idealised and actual empirical data will be matched with dynamic models which consider complexity at a fundamental level, inherently mirroring the systems they are attempting to replicate. Cloud computing brings the possibility of doing all of this, in less time than the simplified macro models of the past, thus enabling better answers and at the time of critical decision making junctures. This research was task driven - the question of high speed rail versus aviation led to an investigation into the simplifications and assumptions that back up many of the commonly held beliefs on the sustainability of different modes of transport. The literature ultimately highlighted the need for context specific information; actual load factors, actual journey times considering traffic/engineering works and so on. Thus, rather than being explicitly an exercise in answering a specific question, a specific question was used to drive the development of a tool which may hold promise for answering a range of transportation related questions. The original contributions of this work are, firstly the use of real-time data sources to quantify temporally and spatially dynamic network performance metrics (eg. journey times on different transport models) and secondly to organise these data sources in a framework which can handle the volume and type of the data and organise the data in a way so that it is useful for the dynamic agent based modelling of future scenarios.EPSRC I Case Studentship with Ove Arup & Partner

Promoting Intermodal Connectivity at California’s High Speed Rail Stations

High-speed rail (HSR) has emerged as one of the most revolutionary and transformative transportation technologies, having a profound impact on urban-regional accessibility and inter-city travel across Europe, Japan, and more recently China and other Asian countries. One of HSR’s biggest advantages over air travel is that it offers passengers a one-seat ride into the center of major cities, eliminating time-consuming airport transfers and wait times, and providing ample opportunities for intermodal transfers at these locales. Thus, HSR passengers are typically able to arrive at stations that are only a short walk away from central business districts and major tourist attractions, without experiencing any of the stress that car drivers often experience in negotiating such highly congested environments. Such an approach requires a high level of coordination and planning of the infrastructural and spatial aspects of the HSR service, and a high degree of intermodal connectivity. But what key elements can help the US high-speed rail system blend successfully with other existing rail and transit services? That question is critically important now that high-speed rail is under construction in California. The study seeks to understand the requirements for high levels of connectivity and spatial and operational integration of HSR stations and offer recommendations for seamless, and convenient integrated service in California intercity rail/HSR stations. The study draws data from a review of the literature on the connectivity, intermodality, and spatial and operational integration of transit systems; a survey of 26 high-speed rail experts from six different European countries; and an in-depth look of the German and Spanish HSR systems and some of their stations, which are deemed as exemplary models of station connectivity. The study offers recommendations on how to enhance both the spatial and the operational connectivity of high-speed rail systems giving emphasis on four spatial zones: the station, the station neighborhood, the municipality at large, and the region

Issues on simulation of the railway rolling stock operation process – a system and literature review

Railway traffic simulation, taking into account operation and maintenance conditions, is not a new issue in the literature. External effects in such networks (eg. level crossings) were not taken into account in studies. The used models do not take into account sufficiently the process of degradation and recovery of the network. From the technical side, currently carried out simulations are made using similar approaches and techniques as in the initial stage of research. Well-established work in this area could be the basis for evaluation of new solutions. However, the progress in simulation tools during the last years, especially in performance and programming architecture, attempt to create a modern simulation tool. In the paper were presented the main assumptions for the evaluated event-based simulation method, with application to stiff-track transportation network

Edge computing and iot analytics for agile optimization in intelligent transportation systems

[EN] With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.This work was partially supported by the Spanish Ministry of Science (PID2019111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019I-ES01-KA103-062602).Peyman, M.; Copado, PJ.; Tordecilla, RD.; Do C. Martins, L.; Xhafa, F.; Juan-Pérez, ÁA. (2021). Edge computing and iot analytics for agile optimization in intelligent transportation systems. Energies. 14(19):1-26. https://doi.org/10.3390/en14196309126141

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others