Relevant bibliographies by topics / Transportation System Uncertainty

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Transportation System Uncertainty'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Transportation System Uncertainty"

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Krygina, I. E. "ORGANIZATION OF MULTIMODAL TRANSPORTATION UNDER UNCERTAINTY AND RISK." Business Strategies, no. 11 (November 23, 2019): 08–11. http://dx.doi.org/10.17747/2311-7184-2019-11-08-11.

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The article provides a terminological interpretation of the concept of multimodal transportation and the signs that determine it. We consider the features of the transport and logistics system of our country, dictating specific requirements for logistics schemes for delivering goods. Also considered is the concept of risk, its types and factors as applied to multimodal transport. In conclusion, a unified procedure for risk management in logistics systems is formulated.
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Tao, Xianding, and Paul Schonfeld. "Lagrangian Relaxation Heuristic for Selecting Interdependent Transportation Projects under Cost Uncertainty." Transportation Research Record: Journal of the Transportation Research Board 1931, no. 1 (January 2005): 74–80. http://dx.doi.org/10.1177/0361198105193100110.

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The capital budgeting process for a transportation system is usually complicated by the interdependencies of projects and the uncertainty of project costs. The existence of project interdependence in transportation systems makes it difficult to evaluate the project effects with analytical methods. Furthermore, when the construction costs of candidate projects are uncertain, the budget constraints that bind project selection become chance constraints, and this may render most existing approaches inapplicable. This paper formulates the project selection problem as a nonlinear integer optimization problem whose objective function is implicit but can be evaluated with network simulation. The Lagrangian method is applied to relax the complex project constraints that are nonlinear under cost uncertainty. An efficient genetic algorithm is developed to solve the Lagrangian subproblems. This paper applies an equilibrium traffic assignment model to evaluate the project impacts and the objective values of the Lagrangian subproblems. Experiments are designed to test the performance of the developed approach on a fairly generic highway system. The experiment results show that the developed approach can effectively solve the problem of selecting interdependent projects under cost uncertainty.
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Maksakova, Darya. "Gas transportation system development in Mongolia under gas prices uncertainty." E3S Web of Conferences 289 (2021): 04006. http://dx.doi.org/10.1051/e3sconf/202128904006.

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The paper analyses the stability of a solution to a problem of gas transportation system development in terms of gas import prices. The object of the study is a future gas transportation system in Mongolia. The employed tools are based on an original optimization problem, which is aimed to support decision-making process when choosing capacity, location, and time for investments in gas infrastructure. Different scenarios of gas import prices are considered for Mongolia. A stable solution is defined as the solution that is included in the optimal plans for every scenario. A multi-criteria approach is proposed to expanding the area of stable solutions. In conclusion, the priority areas of gas transportation system development in Mongolia are highlighted.
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Sevcikova, Hana, and Brice Nichols. "Land use uncertainty in transportation forecast." Journal of Transport and Land Use 14, no. 1 (July 26, 2021): 805–20. http://dx.doi.org/10.5198/jtlu.2021.1853.

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Using an integrated land use and travel model system implemented for the Puget Sound region in Washington state, a Bayesian Melding technique is applied to represent variations in land use outcomes, and is propagated into travel choices across a multi-year agent-based simulation. A scenario is considered where zoned capacity is increased around light rail stations. Samples are drawn from the posterior distribution of households to generate travel model inputs. They allow for propagation of land use uncertainty into travel choices, which are themselves assessed for uncertainty by comparing against observed data. Resulting travel measures of zonal vehicle miles traveled (VMT) per capita and light rail station boardings indicate the importance of comparing distributions rather than point forecasts. Results suggest decreased VMT per capita in zones near light rail stations and increased boardings at certain stations with existing development, and less significant impacts around stations with lower initial development capacity. In many cases, individual point level comparisons of scenarios would lead to very different conclusions. Altogether, this finding adds to a line of work demonstrating the policy value of incorporating uncertainty in integrated models and provides a method for assessing these variations in a systematic way.
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Zhao, Weixin, Hongyu Jiang, Kai Tang, Wenqi Pei, Yadong Wu, and Abdul Qayoom. "Knotted-line: A Visual explorer for uncertainty in transportation system." Journal of Computer Languages 53 (August 2019): 1–8. http://dx.doi.org/10.1016/j.cola.2019.01.001.

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Shrivastava, Himanshu, Andreas T. Ernst, and Mohan Krishnamoorthy. "Distribution and Inventory Planning in a Supply Chain Under Transportation Route Disruptions and Uncertain Demands." International Journal of Information Systems and Supply Chain Management 12, no. 3 (July 2019): 47–71. http://dx.doi.org/10.4018/ijisscm.2019070103.

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This article considers transportation disruptions and its detrimental impact on the quality of the enroute shipment. The authors consider a supply chain system of a short life cycle product that has a capacitated supplier, a retailer and multiple routes of transportation under different disruption risks, uncertain cost of transportation, and uncertain demands. The authors investigate a hybrid problem in which the firm needs to develop a suitable distribution strategy under disruption risks along with an optimal checking policy when faced with the supply of varying quantities of damaged items. The authors formulate a non-linear mathematical model in which the overall objective is to maximise the expected profit and to help the firm in decision making under uncertain environments. Lastly, a statistical study is carried out to perform uncertainty analysis.
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Raskin, Lev, Oksana Sira, and Viacheslav Karpenko. "TRANSPORTATION MANAGEMENT IN A DISTRIBUTED LOGISTIC CONSUMPTION SYSTEM UNDER UNCERTAINTY CONDITIONS." EUREKA: Physics and Engineering 4 (July 31, 2019): 82–90. http://dx.doi.org/10.21303/2461-4262.2019.00936.

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The problem of supply management in the supplier-to-consumer logistics transport system has been formed and solved. The novelty of the formulation of the problem consists in the integrated accounting of costs in the logistic system, which takes into account at the same time the cost of transporting products from suppliers to consumers, as well as the costs for each of the consumers to store the unsold product and losses due to possible shortages. The resulting optimization problem is no longer a standard linear programming problem. In addition, the work assumes that the solution of the problem should be sought taking into account the fact that the initial data of the problem are not deterministic. The analysis of traditional methods of describing the uncertainty of the source data. It is concluded that, given the rapidly changing conditions for the implementation of the delivery process in a distributed supplier-to-consumer system, it is advisable to move from a theoretical probability representation of the source data to their description in terms of fuzzy mathematics. At the same time, in particular, the fuzzy values of the demand for the delivered product for each consumer are determined by their membership functions. Distribution of supplies in the system is described by solving a mathematical programming problem with a nonlinear objective function and a set of linear constraints of the transport type. In forming the criterion, a technology is used to transform the membership functions of fuzzy parameters of the problem to its theoretical probabilistic counterparts – density distribution of demand values. The task is reduced to finding for each consumer the value of the ordered product, minimizing the average total cost of storing the unrealized product and losses from the deficit. The initial problem is reduced to solving a set of integral equations solved, in general, numerically. It is shown that in particular, important for practice, particular cases, this solution is achieved analytically. The paper states the insufficient adequacy of the traditionally used mathematical models for describing fuzzy parameters of the problem, in particular, the demand. Statistical processing of real data on demand shows that the parameters of the membership functions of the corresponding fuzzy numbers are themselves fuzzy numbers. Acceptable mathematical models of the corresponding fuzzy numbers are formulated in terms of bifuzzy mathematics. The relations describing the membership functions of the bifuzzy numbers are given. A formula is obtained for calculating the total losses to storage and from the deficit, taking into account the bifuzzy of demand. In this case, the initial task is reduced to finding the distribution of supplies, at which the maximum value of the total losses does not exceed the permissible value.
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Hong, Sungchul, and Alan P. Vonderohe. "Uncertainty Issues in Integrating Geographic Information Systems and the Global Positioning System for Transportation." Transportation Research Record: Journal of the Transportation Research Board 2215, no. 1 (January 2011): 50–58. http://dx.doi.org/10.3141/2215-05.

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Liu, Dong Jie, and Zhen Lin Wei. "Safety Assessment of Railway Dangerous Goods Transportation Based on Set Pair Analysis." Advanced Materials Research 869-870 (December 2013): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.288.

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In order to overcome the drawbacks that occur when traditional safety assessment methods do not consider the certain and uncertain characteristics of information and in order to enhance the calculation efficiency, a new Identical Discrepancy Contrary (IDC) system is safety comprehensive assessment model based on Set Pair Analysis is established. Incorporating the Analytic Hierarchy Process (AHP) to obtain the index weight, this model analyses the uncertainties in the degree of connection acquired by assessment, and then makes the identical discrepancy contrary system research on the certainty and uncertainty of information in the engineering system.
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Ngossaha, J. Moskolaï, R. Houé Ngouna, B. Archimède, and J. M. Nlong. "Sustainability assessment of a transportation system under uncertainty: an integrated multicriteria approach." IFAC-PapersOnLine 50, no. 1 (July 2017): 7481–86. http://dx.doi.org/10.1016/j.ifacol.2017.08.1064.

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Dissertations / Theses on the topic "Transportation System Uncertainty"

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Zhang, Xu. "INCORPORATING TRAVEL TIME RELIABILITY INTO TRANSPORTATION NETWORK MODELING." UKnowledge, 2017. http://uknowledge.uky.edu/ce_etds/54.

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Travel time reliability is deemed as one of the most important factors affecting travelers’ route choice decisions. However, existing practices mostly consider average travel time only. This dissertation establishes a methodology framework to overcome such limitation. Semi-standard deviation is first proposed as the measure of reliability to quantify the risk under uncertain conditions on the network. This measure only accounts for travel times that exceed certain pre-specified benchmark, which offers a better behavioral interpretation and theoretical foundation than some currently used measures such as standard deviation and the probability of on-time arrival. Two path finding models are then developed by integrating both average travel time and semi-standard deviation. The single objective model tries to minimize the weighted sum of average travel time and semi-standard deviation, while the multi-objective model treats them as separate objectives and seeks to minimize them simultaneously. The multi-objective formulation is preferred to the single objective model, because it eliminates the need for prior knowledge of reliability ratios. It offers an additional benefit of providing multiple attractive paths for traveler’s further decision making. The sampling based approach using archived travel time data is applied to derive the path semi-standard deviation. The approach provides a nice workaround to the problem that there is no exact solution to analytically derive the measure. Through this process, the correlation structure can be implicitly accounted for while simultaneously avoiding the complicated link travel time distribution fitting and convolution process. Furthermore, the metaheuristic algorithm and stochastic dominance based approach are adapted to solve the proposed models. Both approaches address the issue where classical shortest path algorithms are not applicable due to non-additive semi-standard deviation. However, the stochastic dominance based approach is preferred because it is more computationally efficient and can always find the true optimal paths. In addition to semi-standard deviation, on-time arrival probability and scheduling delay measures are also investigated. Although these three measures share similar mathematical structures, they exhibit different behaviors in response to large deviations from the pre-specified travel time benchmark. Theoretical connections between these measures and the first three stochastic dominance rules are also established. This enables us to incorporate on-time arrival probability and scheduling delay measures into the methodology framework as well.
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Nisar, Hiral (Hiral Dhiraj), and Joshua Rosenzweig. "Real-time order acceptance in transportation under uncertainty." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92119.

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Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 56).
Without using any order acceptance criteria, retail companies distributing products with private transportation fleets are not able to maximize their profits because they are not adequately utilizing their capacity. The objective of this paper was to create and validate a model to determine if historical demand data can be used by retail firms operating private fleets to make effective real-time order acceptance/rejection decisions with the purpose of eliminating unprofitable orders in a short-haul transportation setting. A Java tool was generated to instantaneously decide whether or not to accept an order depending on the order location and time of receipt. The model was tested against optimal decisions using total demand knowledge and several alternative real-time decision-making strategies. The model was found to significantly outperform the alternative real-time decision making strategies and provided profits approximately eight percent lower than the optimal decisions. We conclude that using historical demand probabilities is useful in informing the decisions of retail firms seeking to utilize private fleets efficiently and increase profitability through cost reduction.
by Hiral Nisar and Joshua Rosenzweig.
M. Eng. in Logistics
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Pálsson, Sigurjón. "Creating value from uncertainty : a study of ocean transportation contracting." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33339.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.
Includes bibliographical references (leaves 80-83).
How can financial tools like real options and hedging mitigate and create value from uncertainty in transportation? This paper describes these concepts and identifies research on them that has relevance to transportation. It then gives historical background of the containerized ocean transportation industry, uses Porter's five forces to explain its dynamics, and explains how contracts are set up and managed. It identifies areas within containerized ocean transportation that could benefit from real options and hedging, claiming that recent deregulation is creating opportunities for innovative thought. It gives examples of how real options are already being used in the industry to create flexibility without having any price attached to them and then comes up with new ideas of using them. It concludes by, first, stating that both shippers and carriers can benefit from managing uncertainty together and, second, suggesting future areas of research.
by Sigurjón Pálsson.
M.Eng.in Logistics
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Lee, Renée. "Uncertainty and correlation in seismic risk assessment of transportation systems /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Törnquist, Johanna. "Computer-based decision support for handling uncertainty in railway traffic and transportation." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00273.

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Railway transportation has great potential, but interdependencies in the railway traffic make trains very sensitive to disturbances, which can bedifficult to handle. Using railway in an intermodal transport chain may complicate the interconnections with other modes if there is large uncertainty in the performance of the railway. A study based on interviews with several customers of the Swedish National Railway Administration shows that the customers lack information regarding occurrences of disturbances and the consequences for their trains, i.e. the new estimated time of arrival. This information is necessary when taking actions within the customers’ organisation to minimise the negative impacts. Predicting the consequences of a disturbance and the effects of counter measures taken by the Swedish train traffic managers is today an overwhelming task considering there is no computational decision support available. However, provided that the traffic and decision-making could be simulated, the effects from disturbances and actions taken could be computed. Requirements regarding computational time for a simulation are obviously significant and affect the usefulness in a real-time environment. For strategic purposes, a simulator could also serve as an analytical tool for evaluating strategies for handling certain types of disturbances. An approach to model the different layers of such a simulator, taken into account the infrastructure, the traffic flow and the influence of the traffic management decisions and transport operators, is presented. The part that covers the interaction between train traffic and infrastructure has been implemented as a small-scale discrete-event simulator. The simulator is extended by an optimisation approach, attempting to act as decision support for the train traffic managers when handling disturbances by generating effective counter measures. It is composed by a linear model specifying the timetable of a sub-network and solved by optimisation software. Iteratively, a heuristic is applied to modify the timetable by changing meets and overtakes and generate a new updated linear model to be solved. In addition to consider the train traffic system as an explicit part of a transport chain, it can be seen as a black box where some relations between input and output are known. We have investigated how uncertainty in the reliability related to one or several transport links can be handled when combining several links into a transport chain. A simulation-based approach is proposed.
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Kronprasert, Nopadon. "Reasoning for Public Transportation Systems Planning: Use of Dempster-Shafer Theory of Evidence." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/26538.

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Policy-makers of todayâ s public transportation investment projects engage in debates in which the reasonableness and clarity of their judgment are tested many times. How to recommend the transportation system that achieves projectâ s goals and different stakeholdersâ needs in a most logical and justifiable manner is the main question of this dissertation. This study develops a new decision-making approach, Belief Reasoning method, for evaluating public transportation systems in the planning process. The proposed approach applies a reasoning map to model how experts perceive and reason transportation alternatives to lead to the projectâ s goals. It applies the belief measures in the Dempster-Shafer theory of evidence as the mathematical mechanism to represent knowledge under uncertainty and ambiguity and to analyze the degree of achievement of stated goals. Three phases are involved in implementing the Belief Reasoning method. First, a set of goals, a set of characteristics of the alternatives, a set of performances and impacts are identified and the reasoning map, which connects the alternatives to the goals through a series of causal relations, is constructed. Second, a knowledge base is developed through interviewing the experts their degree of belief associated with individual premises and relations, and then aggregating the expert opinions. Third, the model is executed and the results are evaluated in three ways: (i) the transportation alternatives are evaluated based on the degree of belief for achieving individual goals; (ii) the integrity of the reasoning process is evaluated based on the measures of uncertainty associated with information used; and (iii) the critical reasoning chains that significantly influence the outcome are determined based on the sensitivity analysis. The Belief Reasoning method is compared with the Bayesian reasoning, which uses the probability measures as the measure of uncertainty. Also it is compared with the Analytical Hierarchy Process method, which uses a hierarchical tree structure and a weighting scheme. The numerical examples in transit planning are developed for comparison. The proposed Belief Reasoning method has advantages over these traditional evaluation and reasoning methods in several ways. â ¢ Use of a reasoning map structure together with an inference process, instead of a tree structure together with a weighting scheme, allows modeling interdependency, redundancy and interactions among variables, usually found in transportation systems. â ¢ Use of belief measures in Dempster-Shafer theory can preserve non-deterministic nature of inputs and performances as well as handle incomplete or partial knowledge of experts or citizens, i.e. â I donâ t knowâ type opinion. The â degrees of beliefâ measures allow experts to express their strength of opinions in the conservative and optimistic terms. Such operation is not possible by the probability-based approach. â ¢ Dempster-Shafer theory can avoid the scalability issue encountered in Bayesian reasoning. It can also measure uncertainty in the reasoning chains, and identify information needed for improving the reasoning process. â ¢ Use of Dempsterâ s rule of combination, instead of the average operator in probability theory, to merge expert opinions about inputs or relations is a better way for combining conflicting and incomplete opinions. In the dissertation, the Belief Reasoning method is applied in real-world Alternatives Analysis of a transit investment project. The results show its potential to analyze and evaluate the alternatives and to provide reasons for recommending a preferred alternative and to measure the uncertainty in the reasoning process. In spite of some shortcomings, discussed in the dissertation, the Belief Reasoning method is an effective method for transportation planning compared with the existing methods. It provides means for the planners and citizens to present their own reasons and allows review and analysis of reasoning and judgments of all participating stakeholders. The proposed method can promote focused discourse among different groups of stakeholders, and enriches the quality of the planning process.
Ph. D.
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Andersson, David, and Simon Robertsson. "Intelligent Transportation Systems : Capturing the socio-economic value of uncertain and flexible investments." Thesis, Uppsala universitet, Industriell teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-322388.

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The aim of this study is to evaluate an alternative socio-economical valuation method (i.e., Hybrid Real Options, HRO) to the traditional benefit cost method (CBA) for the evaluation of investments within Intelligent Transportation Systems (ITS). The proposed alternative method will be evaluated by the use of a case study where it is applied and compared to the results of the traditional method. The case study evaluates the socio-economical effects of an investment in Variable Speed Limits along a section of the motorway E18. The results of the study shows that the choice of evaluation methods affects both the investment strategy and the estimated socio-economical benefits of the investment. Using the HRO method yields twice as high socio-economical benefits compared to the CBA method. The main reason for this being that HRO account for risk and uncertainties wheras CBA only accounts for the most probable outcome of the investment. The choice of method is a complex task that involves many stakeholders however a more critical approach to the choice of socio-economical evaluation method is advocated based on the results of this study.
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Mangalpally, Sharat C. "Assessment of integrity of reasoning in large-scale decision systems application to public transit investment project evaluation /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 1.07Mb, 127 p, 2005. http://wwwlib.umi.com/dissertations/fullcit/1428262.

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Haj, Kazem Kashani Hamed. "A real options model for the financial valuation of infrastructure systems under uncertainty." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43630.

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Build-Operate-Transfer (BOT) is a form of Public-Private Partnerships that is commonly used to close the growing gap between the cost of developing and modernizing transportation infrastructure systems and the financial resources available to governments. When assessing the feasibility of a BOT project, private investors consider revenue risk - which is stemmed from the uncertainty about future traffic demand - as a critical factor. A potential approach to mitigating the revenue risk is the offering of revenue risk sharing mechanisms such as Minimum Revenue Guarantee options by the government. In addition to Minimum Revenue Guarantee options, a mechanism known as Traffic Revenue Cap options may also be negotiated, which makes the government entitled to a share of revenue when it grows beyond a specified threshold. Financial valuation of investments in BOT projects should take into account uncertainty about future traffic demand, as well as Minimum Revenue Guarantee and Traffic Revenue Cap options. The conventional valuation methods including Net Present Value (NPV) analysis are not capable of integrating the uncertainty about future traffic demand in the valuation of BOT projects and properly pricing Minimum Revenue Guarantee and Traffic Revenue Cap options. Real options analysis can be used as an alternative approach to valuation of investments in transportation projects under uncertainties. However, the appropriate application of real options analysis to valuation of investments in transportation projects is conditioned upon overcoming specific theoretical challenges. Current real options models do not provide a systematic method for estimating the project volatility, which measures the variability of investment value. Existing models do not provide a method for calculating the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options. Also, current models are not able to characterize the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on private investors' financial risk profile. The overarching objective of this research is to apply the real options theory in order to price Minimum Revenue Guarantee and Traffic Revenue Cap options under the uncertainty about future traffic demand. To achieve this objective, a real options model is created that characterizes the long-term traffic demand uncertainty in BOT projects and determines investors' financial risk profile under uncertainty about future traffic demand. This model presents a novel method for estimating the project volatility for real options analysis. This model devises a market-based option pricing approach to determine the correct value of Minimum Revenue Guarantee and Traffic Revenue Cap options. An appropriate procedure is created for characterizing the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on the investors' financial risk profile. The proposed real options model is applied to a BOT project to illustrate the valuation process. The limitations of the proposed real options model, as well as the barriers to its implementation, are identified and recommendations for future research are offered. This research contributes to the state of knowledge by presenting a new method for estimating the project volatility, which is required for the real options analysis of transportation investments. It also introduces a risk-neutral valuation method for pricing the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options in BOT projects. The research also contributes to the state of practice by introducing a novel class of assessment tools for decision makers that characterize the investors' financial risk profile under uncertainty about future traffic demand. Proper methods for pricing of Minimum Revenue Guarantee and Traffic Revenue Cap options are useful to public and private investors, in order to avoid wasting capital in transportation projects.
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O'Har, John Patrick. "Transportation asset management systems: a risk-oriented decision making approach to bridge investment." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41231.

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Transportation Asset Management (TAM) systems are in use at a significant number of transportation agencies. These systems can be used to effectively allocate resources and continuously inventory and monitor the condition of transportation infrastructure assets. Risk-oriented decision making is becoming an increasingly important component of the management process at many organizations, including transportation agencies. TAM systems can be used to incorporate risk assessment and risk management techniques at transportation agencies. To demonstrate the value of incorporating risk in TAM systems, an examination of the literature was performed, and a case study was conducted. This case study incorporated risk in bridge project prioritization through the utilization of data from the National Bridge Inventory (NBI), and application of Multi Attribute Decision Making (MADM) concepts to address uncertainty and prioritize selected bridges in the state of Georgia. The case study examines the impacts of data aggregation and disaggregation, and the incorporation of uncertainty on bridge project prioritization. Results of this analysis show that when available, disaggregate data on bridge condition should be used. In addition, uncertainty, in terms of performance risk, should be incorporated when past bridge condition data is available. Furthermore, decision-maker input is an important component of the Multi Attribute Utility Theory (MAUT) prioritization methodology used in this analysis. Decision-makers determine the relative importance of certain attributes, which is one of the strengths of this type of prioritization effort.
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Books on the topic "Transportation System Uncertainty"

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Office, General Accounting. Air traffic control: Impact of revised personnel relocation policies is uncertain : report to the Chairman, Subcommittee on Aviation, Committee on Transportation and Infrastructure, House of Representatives. Washington, D.C: The Office, 2002.

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Office, General Accounting. Alternative-fueled vehicles: Progress made in accelerating federal purchases, but benefits and costs remain uncertain : report to Congressional requesters. Washington, D.C: The Office, 1994.

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Office, General Accounting. Alternative-fueled vehicles: Progress made in accelerating federal purchases, but benefits and costs remain uncertain : report to Congressional requesters. Washington, D.C: The Office, 1994.

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Oliveira, Rui Carvalho, and Joao de Abreu e Silva. Risk, Uncertainty and Statistical Modelling for Transportation Systems. Taylor & Francis Group, 2018.

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Alternative-fueled vehicles: Progress made in accelerating federal purchases, but benefits and costs remain uncertain : report to congressional requesters. Washington, D.C: The Office, 1994.

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Alternative-fueled vehicles: Progress made in accelerating federal purchases, but benefits and costs remain uncertain : report to Congressional requesters. Washington, D.C: The Office, 1994.

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Alternative-fueled vehicles: Progress made in accelerating federal purchases, but benefits and costs remain uncertain : report to Congressional requesters. Washington, D.C: The Office, 1994.

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Aviation safety: Slow progress, uncertain future threaten FAA program to measure safety : statement of JayEtta Z. Hecker, Director, Resources, Community, and Economic Development Information Systems, Information Management and Technology Division, before the Subcommittee on Government Activities and Transportation, Committee on Government Operations, House of Representatives. [Washington, D.C.]: The Office, 1992.

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Book chapters on the topic "Transportation System Uncertainty"

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Feng, Cuiying, Chi Ouyang, and Xiaoling Song. "Multiple Objective Optimization for Multistage Transportation System Under Uncertainty." In Lecture Notes in Electrical Engineering, 567–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40078-0_49.

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Ignaciuk, Przemysław, and Adam Dziomdziora. "Intelligent Planning of Logistic Networks to Counteract Uncertainty Propagation." In Computational Science – ICCS 2021, 351–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77970-2_27.

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AbstractA major obstacle to stable and cost-efficient management of goods distribution systems is the bullwhip effect – reinforced demand uncertainty propagating among system nodes. In this work, by solving a formally established optimization problem, it is shown how one can mitigate the bullwhip effect, at the same minimizing transportation costs, in modern logistic networks with complex topologies. The flow of resources in the analyzed network is governed by the popular order-up-to inventory policy, which thrives to maintain sufficient stock at the nodes to answer a priori unknown, uncertain demand. The optimization objective is to decide how intensive a given transport channel should be used so that unnecessary goods relocation and the bullwhip effect are avoided while being able to fulfill demand requests. The computationally challenging optimization task is solved using a population-based evolutionary technique – Biogeography-Based Optimization. The results are verified in extensive simulations of a real-world transportation network.
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Dam, B. K., and A. K. Chakraborty. "Crucial Role of Transportation System in the Socioeconomic Development of the Nation in the Context of Safety Assessment and Management." In Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM - 2012), 1299–313. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0757-3_91.

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Caggiani, Leonardo, and Michele Ottomanelli. "Design of Priority Transportation Corridor Under Uncertainty." In Advances in Intelligent Systems and Computing, 291–303. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00930-8_26.

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Liu, Qianqian, and Qun Wang. "A Dynamic Model of Post-disaster Search and Rescue Considering Information Uncertainty." In Green Intelligent Transportation Systems, 597–606. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3551-7_47.

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Ma, Yongfeng, and Rangang Yu. "Uncertainty Analysis of Rock Strength Based on Mohr-Coulomb Criterion." In Green, Smart and Connected Transportation Systems, 1423–30. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0644-4_107.

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Yang, Ye, and Fu-tian Wang. "Uncertainty in Lanzhou-Xinjiang Railway Track Longitudinal Level Irregularity Degradation." In Green, Smart and Connected Transportation Systems, 249–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0644-4_19.

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Zhou, Guiliang, Tianwen Bao, Lina Mao, Lijun Huang, and Dingxin Wu. "Coordinated Carpool Route Selection and Optimization for Dynamic Uncertain Demand Based on Connected Vehicles." In Green Intelligent Transportation Systems, 513–26. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3551-7_40.

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Wang, Y., S. Fu, E. Zio, D. Zhang, and X. Yan. "Modeling epistemic uncertainty in resilience assessment for water transportation systems." In Risk, Reliability and Safety: Innovating Theory and Practice, 2223–30. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-335.

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Borodin, Valeria, Jean Bourtembourg, Faicel Hnaien, and Nacima Labadie. "An Interval Programming Approach for an Operational Transportation Planning Problem." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 117–26. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08795-5_13.

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Conference papers on the topic "Transportation System Uncertainty"

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Li, Bing, Jin-Xin Cao, and Yuan-Yuan Li. "Research on a Public Bicycle Pricing System under Uncertainty." In 16th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479896.192.

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Yan, Yuexia, and Junqi Wang. "Urban Traffic System Evaluation Based on Uncertainty Multi-Attribute Method." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)102.

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He, Guoguang, Shoufeng Ma, and Guizhu Wang. "A Microscopic Simulation System for Traffic Flow with Uncertainty." In Second International Conference on Transportation and Traffic Studies (ICTTS ). Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40503(277)98.

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Leuveano, Achmad Chairdino, Fairul Azni Bin Jafar, and Mohd Razali Bin Muhamad. "Development of genetic algorithm on multi-vendor integrated procurement-production system under shared transportation and just-in-time delivery system." In 2012 2nd International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE). IEEE, 2012. http://dx.doi.org/10.1109/urke.2012.6319589.

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Yuchao Wang, Lifei Bai, and Sheng Liu. "Robust H2/H8 control of nonlinear system with differential uncertainty." In 2014 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific). IEEE, 2014. http://dx.doi.org/10.1109/itec-ap.2014.6940618.

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Chee, Matthew, and Cameron J. Turner. "Localized Decision-Making for Materials Transportation Systems Subject to Stochastic Uncertainty." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38274.

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Programming and installation of Materials Transportation Systems (MTS) in a manufacturing setting represent a significant portion of the investment in these systems. The costs are often so high that this alone presents a barrier to adoption amongst midsize manufacturers. Furthermore, the resulting systems are just as costly to reconfigure, limiting the flexibility of the resulting manufacturing facility to product changes. This research examines a localized decision-making scheme, in which individual manufacturing components are enhanced with an amount of intelligence and autonomy to enable the system to automatically self-program. Through an example, we study this paradigm versus fixed programming alternatives as the stochastic variability of the manufacturing setting increases. Localized Decision-Making enables a plug-n-play autonomy that can readily adapt to changes and deal with uncertainties in manufacturing.
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Chen, Lu, Xiaodan Wu, Jian Wu, and Jinbo Jiao. "Risk Assessment Method for Combined Air Storage System Based on Wind Power Uncertainty." In 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2020. http://dx.doi.org/10.1109/icitbs49701.2020.00099.

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Bigoni, D., A. P. Engsig-Karup, and H. True. "Modern Uncertainty Quantification Methods in Railroad Vehicle Dynamics." In ASME 2013 Rail Transportation Division Fall Technical Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/rtdf2013-4713.

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This paper describes the results of the application of Uncertainty Quantification methods to a simple railroad vehicle dynamical example. Uncertainty Quantification methods take the probability distribution of the system parameters that stems from the parameter tolerances into account in the result. In this paper the methods are applied to a low-dimensional vehicle dynamical model composed by a two-axle truck that is connected to a car body by a lateral spring, a lateral damper and a torsional spring, all with linear characteristics. Their characteristics are not deterministically defined, but they are defined by probability distributions. The model — but with deterministically defined parameters — was studied in [1] and [2], and this article will focus on the calculation of the critical speed of the model, when the distribution of the parameters is taken into account. Results of the application of the traditional Monte Carlo sampling method will be compared with the results of the application of advanced Uncertainty Quantification methods [3]. The computational performance and fast convergence that result from the application of advanced Uncertainty Quantification methods is highlighted. Generalized Polynomial Chaos will be presented in the Collocation form with emphasis on the pros and cons of each of those approaches.
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Wu, Jiaxin, and Pingfeng Wang. "Risk-Averse Optimization for Resilience Enhancement Under Uncertainty." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22226.

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Abstract With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
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Kim, Kihyung, Meng Wang, Michael R. von Spakovsky, and Douglas J. Nelson. "Dynamic Synthesis/Design and Operation/Control Optimization Under Uncertainty of a PEMFC System." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68070.

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Proton exchange membrane fuel cells (PEMFCs) are one of the leading candidates in alternative energy conversion devices for transportation, stationary, and portable power generation applications. Such systems with their own fuel conversion unit typically consist of several subsystems: a fuel processing subsystem, a fuel cell stack subsystem, a work recovery-air supply subsystem, and a power electronics subsystem. Since these subsystems have different physical characteristics, their integration into a single system/subsystem level unit make the problems of optimal dynamic system synthesis/design and operation/control highly complex. Thus, dynamic system/subsystem/component modeling and highly effective optimization strategies are required. Furthermore, uncertainties in the results of system synthesis/design and operation/control optimization can be affected by any number of sources of uncertainty such as the load profiles and cost models. These uncertainties can be taken into account by treating the problem probabilistically. The difficulty with doing this, particularly when large-scale dynamic optimization with a large number of degrees of freedom is being used to determine the optimal synthesis/design and operation/control of the system, is that the traditional probabilistic approaches (e.g., Monte Carlo Method) are so computationally intensive that combined with large-scale optimization it renders the problem computationally intractable. This difficulty can be overcome by the use of approximate approaches such as the response sensitivity analysis (RSA) method based on Taylor series expansion. Thus, in this paper, a stochastic modeling and uncertainty analysis methodology for energy system synthesis/design and operation/control which uses the RSA method is proposed and employed for calculating the uncertainties on the system outputs. Their effects on the synthesis/design and operation/control optimization of a 5kWe PEMFC system are assessed by taking the uncertainties into account in the objectives and constraints. It is shown that these uncertainties significantly affect the reliability of being able to meet certain constraints (e.g., that on the CO concentration) during the synthesis/design and operation/control optimization process. These and other results are presented.
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Reports on the topic "Transportation System Uncertainty"

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Fu, Gongkang. Evaluation of Illinois Bridge Deterioration Models. Illinois Center for Transportation, September 2021. http://dx.doi.org/10.36501/0197-9191/21-029.

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The National Bridge Inventory bridge inspection system ranks the condition of bridge components on a scale of zero to nine. The resulting condition ratings represent an important element considered in deciding measures for bridge maintenance, repair, and rehabilitation. Thus, forecasting future condition ratings well is critical to reliable planning for these activities and estimating the costs. The Illinois Department of Transportation currently has deterministic models for this purpose. This study’s objective is to review the current models using condition rating histories gathered from 1980 to 2020 in Illinois for the following bridge components: deck, superstructure, substructure, culvert, and deck beam. The results show the current Illinois Department of Transportation models are inadequate in forecasting condition ratings, producing overestimates of the transition times between two condition rating levels for these components / systems, except for the deck beam, which is underestimated. It is recommended that the mean transition times found in this study from condition rating histories are used to replace the current models as a short-term solution. Further research is recommended to develop probabilistic models as a long-term solution to address observed significant variation or uncertainty in condition rating and transition times between condition rating levels.
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Lempert, Robert J., Michelle Miro, and Diogo Prosdocimi. A DMDU Guidebook for Transportation Planning Under a Changing Climate. Edited by Benoit Lefevre and Ernesto Monter Flores. Inter-American Development Bank, February 2021. http://dx.doi.org/10.18235/0003042.

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The effects of climate-related natural hazards pose a significant threat to sustainable development in Latin America and the Caribbean (LAC) region and in particular its transportation sector. Risk Management provides an appropriate framework for assessing and mitigating the impacts of climate change and other climate-related natural hazards on transportation systems and choosing actions to enhance their resilience. However, analysts and policymakers involved in transportation planning, policy, and investment face significant challenges in managing the risks triggered by the effects of climate change. Climate change impacts the lifespan of roads, airports, and railroads as they have time horizons that surpass 40 years, thus making it harder (if not impossible) to forecast with confidence all relevant future events that will affect such infrastructure. In addition, the climate has already changed, so the return frequency of storms, for example, and other extreme events may now be different than suggested by the historical record in ways that are not always currently well understood. Implementing Risk Management under conditions of such uncertainty can prove difficult. Decision Making Under Deep Uncertainty (DMDU) enables Risk Management under conditions of Deep Uncertainty, that is when risks cannot confidently be quantified. This guidebook is aligned with the Disaster and Climate Change Risk Assessment Methodology for IDB projects (IDB 2018) and introduces and provides guidance on applying methods for Decision Making Under Deep Uncertainty (DMDU) to transportation planning. It presents the methodological steps that are necessary for the implementation of DMDU methodologies and reviews several such methods, including scenario planning, Adaptive Pathways, and robust decision making (RDM). This review is geared towards supporting the incorporation of DMDU methods into IDBs transportation sector funding and planning processes.
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