Abstract

Life-cycle management of urban road networks as a part of an urban system is a very complex process from the management standpoint of social, technical and economic aspects. The complexity and multidisciplinarity of such a problem suggest the need for using soft computing tools as well as multicriteria analysis and group decision-making. Recently, there is a significant increase in using various soft computing tools, especially neural networks, for different prediction purposes in the field of road construction planning and management. Along with known advantages of such a prediction method, yet some applications showed the shortcomings. In that sense, the focus of this research is on possible applications of neural networks related to the life-cycle phases during the management of urban road projects. This is done in both horizontal (projects‘ life-cycle phases) and vertical (hierarchical decisionmaking levels) approach. The final aim of the research is to compare and highlight the possible applications of neural networks as a prediction tool and support for decision-making in urban road management.

Abstract

Increasing transport activities in cities are a substantial driver for congestion and pollution, influencing urban populations’ health and quality of life. These effects are consequences of ongoing urbanization in combination with rising individual demand for mobility, goods, and services. With the goal of increased environmental sustainability in urban areas, city authorities and politics aim for reduced traffic and minimized transport emissions. To support more efficient and sustainable urban transport, this cumulative dissertation focuses on alternative transport concepts. For this purpose, scientific methods and models of the interdisciplinary information systems domain combined with elements of operations research, transportation, and logistics are developed and investigated in multiple research contributions. Different transport concepts are examined in terms of optimization and acceptance to provide decision support for relevant stakeholders. In more detail, the overarching topic of urban transport in this dissertation is divided into the complexes urban mobility (part A) in terms of passenger transport and urban logistics (part B) with a focus on the delivery of goods and services. Within part A, approaches to carsharing optimization are presented at various planning levels. Furthermore, the user acceptance of ridepooling is investigated. Part B outlines several optimization models for alternative urban parcel and e-grocery delivery concepts by proposing different network structures and transport vehicles. Conducted surveys on intentional use of urban logistics concepts give valuable hints to providers and decision makers. The introduced approaches with their corresponding results provide target-oriented support to facilitate decision making based on quantitative data. Due to the continuous growth of urban transport, the relevance of decision support in this regard, but also the understanding of the key drivers for people to use certain services will further increase in the future. By providing decision support for urban mobility as well as urban logistics concepts, this dissertation contributes to enhanced economic, social, and environmental sustainability in urban areas.

Abstract

Due to the geographical distance between producers and consumers, the impact of our current transport system on the environment is considerable. In an ideal, more sustainable transport system, several complementary transport modes are available, so that the most suitable route can always be selected. Because multiple transport modes and operators are involved, decision processes become more complex. That is why this research focuses on the development of innovative transport systems with realistic characteristics and planning limitations, in order to stimulate intermodal transport. Two planning tools have been developed to support decisions made by intermodal transport planners. It concerns on the one hand the integration of intermodal short and long distance routing and on the other hand the loading planning of trains. Both tools consist of fast planning algorithms that can handle realistic problem properties and can be integrated into transport management systems. The objective is to minimize transport costs and to make maximum use of available transport capacity.

Abstract

The on-going H2020 project INFRALERT aims to increase rail and road infrastructure capacity in the current"br" framework of increased transportation demand by developing and deploying solutions to optimise maintenance"br" interventions planning. INFRALERT develops an ICT platform - the expert-based Infrastructure Management"br" System eIMS - which follows a modular approach including several expert-based toolkits. This paper presents"br" the architecture of the eIMS as well as the functionalities, methodologies and exemplary results of the toolkits"br" for i) nowcasting and forecasting of asset condition, ii) alert generation, iii) RAMS & LCC analysis and iv)"br" decision support. The applicability and effectiveness of the eIMS and its toolkits will be demonstrated in two"br" real-world pilot scenarios, which are described in the paper: a meshed road network in Portugal under the"br" jurisdiction of Infraestruturas de Portugal (IP) and a freight railway line in Northern Europe managed by"br" Trafikverket.

Abstract

The EU co-funded R&D project RAGTIME “Risk based approaches for Asset inteGrity multimodal Transport Infrastructure” aims at setting the basis for an efficient and innovative asset management (AM) across Evaluation/Planning, Procurement/Decision, Design/Project, Construction, Operation & Maintenance phases of Transport Infrastructure (TI). This paper discusses the RAGTIME approach to TIAM, which aims to establish a common framework for governance, management and finance in order to ensure the best possible return from limited investment funds. In line with this main goal, RAGTIME will lead to lay out a whole system planning software platform, based on standard multiscale data models, able to facilitate a holistic management throughout the entire lifecycle of the infrastructure, providing an integrated view of risk based approach, implementing risk based concepts, models and operative methods in order to optimize Return Of Investments (ROI), and guarantying at the same time the same or higher Level Of Service (LOS) through the whole lifecycle of the TI.

Abstract

This work conceptualizes an integrated real-time management and dynamic optimization system for earthwork fleets, based on recent technological developments with potential to track and improve productivity in transportation infrastructure construction processes, namely real-time monitoring and modern optimization technologies. The combination of monitoring and dynamic optimization technologies features the potential to tackle the elevated uncertainty and volatility inherent to the earthworks environment, not only by providing the necessary flexibility and adaptability to an optimization system, but also by integrating equipment, processes and design in a real-time decision support context. Ultimately, this combination can comprise a foundation for development towards the automated, interconnected cyber-physical systems of Industry 4.0.

Abstract

"jats:p"The decision support system is one of the instruments for choosing the most effective decision for cargo owner in constant fluctuated business environment. The objective of this Paper is to suggest the multiple-criteria approach for evaluation and choice the alternatives of cargo transportation in the large scale transportation transit system for the decision makers - cargo owners. The large scale transportation transit system is presented by directed finite graph. Each of 57 alternatives is represented by the set of key performance indicators Kvi and set of parameters Paj. There has been developed a two-level hierarchy system of criteria with ranging expert evaluations based on Analytic Hierarchy Process Method. The best alternatives were suggested according to this method. Document type: Article