Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks

A method for determining optimal risk-based maintenance and rehabilitation (M&R) policies for transportation infrastructure is presented. The proposed policies guarantee a certain performance level across the network under a predefined level of risk. The long-term model is formulated in the Markov Decision Process framework with risk-averse actions and transitional probabilities describing the uncertainty in the deterioration process. The well known Conditional Value at Risk (CVaR) is used as the measure of risk. The steady-state risk-averse M&R policies are modeled assuming no budget restriction. To address the short-term resource allocation problem, two linear programming models are presented to generate network-level polices with different objectives. While the proposed methodology is general and can be used with any performance indicator, pavement roughness is used for numerical studies and an analytical expression for computing CVaR is derived.     

A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach

The objective of this paper is to introduce a multi-year pavement maintenance programming methodology that can explicitly account for uncertainty in pavement deterioration. This is accomplished with the development of a simulation-based genetic algorithm (GA) approach that is capable of planning the maintenance activities over a multi-year planning period. A stochastic simulation is used to simulate the uncertainty of future pavement conditions based on the calibrated deterioration model while GA is used to handle the combinatorial nature of the network-level pavement maintenance programming. The effects of the uncertainty of pavement deterioration on the maintenance program are investigated using a case study. The results show that programming the maintenance activities using only the expected pavement conditions is likely to underestimate the required maintenance budget and overestimate the performance of pavement network.     

Incorporating inspection decisions in pavement management

Pavement management systems need to address not only maintenance and rehabilitation (M&R) decisions, but also facility inspection decisions. The state of the art in pavement management is lacking of any consistent methodology for making such decisions on a cost-effectiveness basis. Such a methodology must recognize the presence of interactions between M&R and inspection decisions. These interactions argue for a joint decision-making approach, where the sum of inspection and M&R costs is minimized. This paper reviews different possible mathematical formulations to such a joint decision-making model, having various levels of restriction and computational complexity. These formulations are then compared and the effect of the forecast uncertainty on the minimum expected costs produced by each of them is investigated empirically. It is concluded that optimizing inspection decisions jointly with M&R decisions can lead to substantial cost savings, especially for high precisions of forecasting.     

Model uncertainty and the management of a system of infrastructure facilities

The network-level infrastructure management problem involves selecting and scheduling maintenance, repair, and rehabilitation (MR&R) activities on networks of infrastructure facilities so as to maintain the level of service provided by the network in a cost-effective manner. This problem is frequently formulated as a Markov decision problem (MDP) solved via linear programming (LP). The conditions of facilities are represented by elements of discrete condition rating sets, and transition probabilities are employed to describe deterioration processes. Epistemic and parametric uncertainties not considered within the standard MDP/LP framework are associated with the transition probabilities used in infrastructure management optimization routines. This paper contrasts the expected costs incurred when model uncertainty is ignored with those incurred when this uncertainty is explicitly considered using robust optimization. A case study involving a network-level pavement management MDP/LP problem demonstrates how explicitly considering uncertainty may limit worst-case MR&R expenditures. The methods and results can also be used to identify the costs of uncertainty in transition probability matrices used in infrastructure management systems.     

A multiobjective chance constrained programming model for supplier selection under uncertainty

Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.     

Path finding under uncertainty

Path finding problems have many real‐world applications in various fields, such as operations research, computer science, telecommunication, transportation, etc. In this paper, we examine three definitions of optimality for finding the optimal path under an uncertain environment. These three stochastic path finding models are formulated as the expected value model, dependent‐chance model, and chance‐constrained model using different criteria to hedge against the travel time uncertainty. A simulation‐based genetic algorithm procedure is developed to solve these path finding models under uncertainties. Numerical results are also presented to demonstrate the features of these stochastic path finding models.     

Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

With a particular emphasis on the end-to-end travel time prediction problem, this paper proposes an information-theoretic sensor location model that aims to minimize total travel time uncertainties from a set of point, point-to-point and probe sensors in a traffic network. Based on a Kalman filtering structure, the proposed measurement and uncertainty quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors. By considering only critical paths and limited time intervals, this paper selects a path travel time uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework with a unified modeling of both recurring and non-recurring traffic conditions. An analytical determinant maximization model and heuristic beam-search algorithm are used to find an effective lower bound and solve the combinatorial sensor selection problem. A number of illustrative examples and one case study are used to demonstrate the effectiveness of the proposed methodology.     

Short-term shift setting and manpower supplying under stochastic demands for air cargo terminals

A good air cargo terminal manpower supply plan helps terminals deal efficiently with their cargos and reduces their operating costs. To design a good air cargo terminal manpower supply plan, a terminal has to consider not only its operating costs, but also the uncertainty of the manpower demand in actual operations. However, most air cargo terminals in Taiwan currently depend on staff experience with a fixed demand when establishing the manpower supply plan, which is neither effective nor efficient. We have developed two stochastic-demand manpower supply plan models for air cargo terminals that can resolve stochastic demands occurring in practice. The objectives of both models are to minimize the total man-hour cost, subject to the related operating constraints. The models are formulated as integer/mixed integer linear programs. To evaluate the two stochastic-demand models under stochastic demands, we have also developed two deterministic-demand manpower supply plan models, by suitably modifying two stochastic-demand models, respectively, and an evaluation method. Here, we perform a case study using real operating data from a Taiwan air cargo terminal. The preliminary results are good, showing that the models could be useful for planning air cargo terminal manpower supply.

Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand

The problem of optimal container vessels deployment is one of great significance for the liner shipping industry. Although the pioneering work on this problem dates back to the early 1990s, only until recently have researchers started to acknowledge and account for the significant amount of uncertainty present in shipping demand in real world container shipping. In this paper, new analytical results are presented to further relax the input requirements for this problem. Specifically, only the mean and variance of the maximum shipping demand are required to be known. An optional symmetry assumption is shown to further reduce the feasible region and deployment cost for typical confidence levels. Moreover, unlike previous work that tends to ignore stochastic dependencies between the shipping demands on the various routes (that are known to exist in the real world), our models account for such dependencies in the most general setting to date. A salient feature of our modeling approach is that the exact dependence structure does not need to be specified, something that is hard, if not simply impossible, to determine in practice. A numerical case study is provided to illustrate the proposed models.     

A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction

We present a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. The proposed bottom-up approach adopts an augmented condition state to account for the history-dependent properties of pavement deterioration, and solves for steady-state policies for an infinite horizon. Genetic algorithms (GAs) are implemented in the system-level optimization based on segment-specific optimization results. The complexity of the proposed algorithm is polynomial in the size of the system and the policy-related parameters. We provide graphical presentations of the optimal solutions for various budget situations. As a case study, a subset of California’s highway system is analyzed. The case study results demonstrate the economic benefit of a combined rehabilitation and reconstruction budget compared to separate budgets.     

Quantifying errors in micro-scale emissions models using a case-study approach

To quantify the level of uncertainty attached to forecasts of CO₂ emissions, an analysis of errors is undertaken; looking at both errors inherent in the model structure and the uncertainties in the input data. Both error types are treated in relation to CO₂ emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of traffic conditions using Monte Carlo simulation is used. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO₂ prediction, the interaction between the two components is taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a compromise solution is found. The results suggest that the CO₂ model used in the analysis produces low overall uncertainty under free flow traffic conditions. When average traffic speeds approach congested conditions, however, there are significant errors associated with emissions estimates.     

A hyperpath-based network generalized extreme-value model for route choice under uncertainties

Previous route choice studies treated uncertainties as randomness; however, it is argued that other uncertainties exist beyond random effects. As a general modeling framework for route choice under uncertainties, this paper presents a model of route choice that incorporates hyperpath and network generalized extreme-value-based link choice models. Accounting for the travel time uncertainty, numerical studies of specified models within the proposed framework are conducted. The modeling framework may be helpful in various research contexts dealing with both randomness and other non-probabilistic uncertainties that cannot be exactly perceived.     

Stochastic optimization models for ground delay program planning with equity–efficiency tradeoffs

Bad weather is a major contributor to air traffic delays. The ration-by-distance algorithm was proposed to increase the efficiency of ground delay program (GDP) planning in situations where there is uncertainty regarding the duration of the weather conditions that motivated the GDP. This algorithm, however, can treat shorter flights unfairly. We describe a two-stage stochastic, multi-objective integer program for GDP planning. This multi-objective approach allows one to balance equity and efficiency. We provide experimental evidence showing the effectiveness of the solutions generated.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity?

A sensitivity analysis of plausible errors in population, employment, fuel price, and income projections is conducted using the travel demand and emissions models of the Sacramento, CA, USA, region for their transportation plan. The results of the analyses indicate that plausible error ranges for household income and fuel prices are not a significant source of uncertainty with respect to the region's travel demand and emissions projections. However, plausible errors in population and employment projections (within approximately one standard deviation) may result in the region's transportation plan not meeting the conformity test for nitrogens of oxides (NO_x) in the year 2005 (i.e., an approximately 16% probability). This outcome is also possible in the year 2015 but less likely (within approximately two standard deviations or a 2.5% probability). Errors in socioeconomic projections are only one of many sources of error in travel demand and emissions models. These results have several policy implications. First, regions like Sacramento that meet their conformity tests by a very small margin should rethink new highway investment and consider contingency transportation plans that incorporate more aggressive emissions reduction policies. Second, regional transportation planning agencies should conduct sensitivity analyses as part of their conformity analysis to make explicit significant uncertainties in the methods and to identify the probability of their transportation plan not conforming. Third, the US Environmental Protection Agency (EPA) should clarify the interpretation of “demonstrate” conformity of transportation plans; that is, specify the level of certainty that it considers a sufficient demonstration of conformity.     

Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty

Experiments studying the behavior of agent-based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. In this paper we apply two structurally distinct solution approaches, an on-line optimization and an agent-based approach, to a drayage problem with time windows under two types of uncertainty. Both solution approaches are able to respond to dynamic events. The on-line optimization approach utilizes a mixed integer program to obtain a feasible route at 30-s intervals. The second solution approach deploys agents that engage in auctions to satisfy their own objectives based on the information they perceive and maintain locally. Our results reveal that the agent-based system can outperform the on-line optimization when service time duration is highly uncertain. The on-line optimization approach, on the other hand, performs competitively with the agent-based system under conditions of job-arrival uncertainty. When both moderate service time and job-arrival uncertainties are combined, the agent system outperforms the on-line optimization; however, in the case of extremely high combined uncertainty, the on-line optimization outperforms the agent-based approach.     

Sensitivity-based uncertainty analysis of a combined travel demand model

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.     

A comparative study of the emissions by road maintenance works and the disrupted traffic using life cycle assessment and micro-simulation

Life cycle assessment is being accepted by the road industry to measure such key environmental impacts as the energy consumption and carbon footprint of its materials and laying processes. Previous life cycle studies have indicated that the traffic vehicles account for the majority of fuel consumption and emissions from a road. Contractors and road agencies are looking for road maintenance works that have the least overall environmental impact considering both the roadwork itself and the disrupted traffic. We review life cycle assessment studies and describe the development of a model for pavement construction and maintenance, detailing the methodology and data sources. The model is applied to an asphalt pavement rehabilitation project in the UK, and the micro-simulation program VISSIM is used to model the traffic on that road section. The simulation results are fed into a traffic emissions model and emissions from the roadwork and the traffic are compared. The additional fuel consumption and emissions by the traffic during the roadwork are significant. This indicates that traffic management at road maintenance projects should be included in the life cycle assessment analysis of such work.     

A model of an urban transportation system formulated as a multiobjective mathematical programming problem under uncertainty

An urban transportation system formulated in terms of a multiobjective mixed integer linear fractional programming (MOMILFP) problem under uncertainty is considered. The system is based on two means of public transportation i.e., trams and buses. One takes into account the factors of both passengers' and operator's concern, whose objectives are, generally, in conflict. The real uncertainty and imprecision of data is modeled by L-R type fuzzy numbers. To solve the fuzzy MOMILFP problem an interactive method is utilized. As an illustration of that approach an application to Poznan's urban transportation system is presented.     

Optimal pricing for priority service and space allocation in container ports

This paper focuses on the determination of optimal space allocation and optimal pricing for priority systems in container ports. The problem is formulated taking into account the intrinsic and logistic cargo value, and a capacity constraint that considers the various physical requirements of the containers. Prices and space allocations are found for various cases, showing explicitly the role of each element. The resulting models extend classical price differentiation theory, i.e. the inverse elasticity rule, in various directions. Finally, the implications of these results and the corresponding information requirements are clearly established.