Search

This paper studies strategic level train planning for high performance passenger and freight train operations on shared-use corridors in the US. We develop a hypergraph-based, two-level approach to sequentially minimize passenger and freight costs while scheduling train services. Passenger schedule delay and freight lost demand are explicitly modeled. We explore different solution strategies and conclude that a problem-tailored linearized reformulation yields superior computational performance. Using realistic parameter values, our numerical experiments show that passenger cost due to schedule delay is comparable to in-vehicle travel time cost and rail fare. In most cases, marginal freight cost increase from scheduling more passenger trains is higher than marginal reduction in passenger schedule delay cost. The heterogeneity of train speed reduces the number of freight trains that can run on a corridor. Greater tolerance for delays could reduce lost demand and overall cost on the freight side. The approach developed in the paper could be applied to other scenarios with different parameter values. [ABSTRACT FROM AUTHOR]

Freight transportation by railroads is an integral part of the U.S. economy. Identifying critical rail infrastructures can help stakeholders prioritize protection initiatives or add necessary redundancy to maximize rail network resiliency. The criticality of an infrastructure element, link or yard, is based on the increased cost (delay) incurred when that element is disrupted. An event of disruption can cause heavy congestion so that the capacity at links and yards should be considered when freight is re-routed. This paper proposes an optimization model for making-up and routing of trains in a disruptive situation to minimize the system-wide total cost, including classification time at yards and travel time along links. Train design optimization seeks to determine the optimal number of trains, their routes, and associated blocks, subject to various capacity and operational constraints at rail links and yards. An iterative heuristic algorithm is proposed to attack the computational burden for real-world networks. The solution algorithm considers the impact of volume on travel time in a congested or near-congested network. The proposed heuristics provide quality solutions with high speed, demonstrated by numerical experiments for small instances. A case study is conducted for the network of a major U.S. Class-I railroad based on publicly available data. The paper provides maps showing the criticality of infrastructure in the study area from the viewpoint of strategic planning. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes the welfare effects of slot controls on major US airports. We consider the fundamental tradeoff between benefits from queuing delay reduction and costs due to simultaneous schedule delay increase to passengers while imposing slot limits at airports. A set of quantitative models and simulation procedures are developed to explore the possible airline scheduling responses through reallocating and trimming flights. We find that, of the 35 major US airports, a more widespread use of slot controls would improve travelers’ welfare. The results from our analyses suggest that slot caps at the four airports that currently have slot controls (Washington Reagan, Newark, New York LaGuardia, New York John F. Kennedy) are set too high. Further slot reduction by removing some of the flights at these airports could generate additional benefits to passengers. Slot controls can potentially reduce two thirds of the total system delays caused by congestion. A number of implementation and design issues related to the use of slot controls are also discussed in the paper. [Copyright &y& Elsevier]

Abstract: This paper formally derives the class of multiple discrete-continuous generalized extreme value (MDCGEV) models, a general class of multiple discrete-continuous choice models based on generalized extreme value (GEV) error specifications. Specifically, the paper proves the existence of, and derives the general form of, closed-form consumption probability expressions for multiple discrete-continuous choice models with GEV-based error structures. In addition to deriving the general form, the paper derives a compact and readily usable form of consumption probability expressions that can be used to estimate multiple discrete-continuous choice models with general cross-nested error structures. The cross-nested version of the MDCGEV model is applied to analyze household annual expenditure patterns in various transportation-related expenses using data from a Consumer Expenditure Survey in the United States. Model estimation results and predictive log-likelihood based validation tests indicate the superiority of the cross-nested model over the mutually exclusively nested and non-nested model specifications. Further, the cross-nested model was amenable to the accommodation of socio-demographic heterogeneity in inter-alternative covariance across decision-makers through a parameterization of the allocation parameters. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to examine whether the manipulative or strategic behaviors of slot-holding carriers have resulted in restricted market entry and service expansion by other carriers, especially rival carriers, at the four US airports that have secondary slot markets. Airport congestion and flight delays at many major airports have become a serious problem. It has been suggested that a secondary slot market is one of the most practical options for addressing airport congestion, which would increase the possibility of competitive entry and efficient use of scarce resources. A secondary slot market would work in the same manner as a congestion toll system or an auction system, provided that carriers’ manipulative or strategic behaviors do not have serious effects on slot trading. However, the empirical effects of slot markets have not been investigated systematically. This paper examines empirically whether carriers’ manipulative or strategic behaviors have impeded effective functioning of slot markets. Slot transfer data from four US airports between 1994 and 1999 were examined using regression analysis. Results of the analysis are mixed, suggesting that the most effective way of congestion management depends on conditions unique to each airport, and that subsequent interventions should reflect those conditions. [Copyright &y& Elsevier]

Abstract: The increasing diversity of vehicle type holdings and the growing usage of vehicles by households have serious policy implications for traffic congestion and air pollution. Consequently, it is important to accurately predict the vehicle holdings of households as well as the vehicle miles of travel by vehicle type to project future traffic congestion and mobile source emission levels. In this paper, we apply a multiple discrete-continuous extreme value model to analyze the holdings and use of multiple vehicle types by households. Data for the analysis is drawn from a 2000 San Francisco Bay Area survey. The model results indicate the important effects of household demographics, residence location variables and vehicle attributes on vehicle type holdings and use. The model developed in the paper can be applied to predict the impact of demographic, land use, and operating cost changes on vehicle type holdings and usage. Such predictions are important at a time when the household demographic characteristics are changing rapidly in the United States. The predictions can also inform the design of proactive land-use, economic, and transportation policies to influence household vehicle holdings and usage in a way that reduces traffic congestion and air quality problems. [Copyright &y& Elsevier]

Airport choice is an important air travel-related decision in multiple airport regions. This paper proposes the use of a probabilistic choice set multinomial logit (PCMNL) model for airport choice that generalizes the multinomial logit model used in all earlier airport choice studies. The paper discusses the properties of the PCMNL model, and applies it to examine airport choice of business travelers residing in the San Francisco Bay Area. Substantive policy implications of the results are discussed. Overall, the results indicate that it is important to analyze the choice (consideration) set formation of travelers. Failure to recognize consideration effects of air travelers can lead to biased model parameters, misleading evaluation of the effects of policy action, and a diminished data fit. [Copyright &y& Elsevier]

Abstract: US railroad companies spend billions of dollars every year on track maintenance in order to ensure safety and operational efficiency. Optimizing the production team (i.e., large maintenance team) schedule is a very complex problem with major cost implications. In current practice, the decision making process for production team scheduling is largely manual and primarily relies on the knowledge and judgment of experts. This paper addressed the production team scheduling problem by formulating it as a time–space network model with many types of challenging side constraints. Some of these constraints are identified from industry practice and formulated for the first time. Multiple neighborhood search and other enhancement algorithms were proposed to solve the model. The proposed modeling approach has been tested through numerical experiments and also applied to large-scale real-world problem instances, and superior computational performances were found. The proposed approach has been adopted by a Class I railroad to help make annual network maintenance scheduling decisions. [Copyright &y& Elsevier]

Abstract: Multiple-discrete continuous choice models formulated and applied in recent years consider a single linear resource constraint, which, when combined with consumer preferences, determines the optimal consumption point. However, in reality, consumers face multiple resource constraints such as those associated with time, money, and capacity. Ignoring such multiple constraints and instead using a single constraint can, and in general will, lead to poor data fit and inconsistent preference estimation, which can then have a serious negative downstream effect on forecasting and welfare/policy analysis. In this paper, we extend the multiple-discrete continuous extreme value (MDCEV) model to accommodate multiple constraints. The formulation uses a flexible and general utility function form, and is applicable to the case of complete demand systems as well as incomplete demand systems. The proposed MC-MDCEV model is applied to time-use decisions, where individuals are assumed to maximize their utility from time-use in one or more activities subject to monetary and time availability constraints. The sample for the empirical exercise is generated by combining time-use information from the 2008 American Time Use Survey and expenditure records from the 2008 US Consumer Expenditure Survey. The estimation results show that preferences can get severely mis-estimated, and the data fit can degrade substantially, when only a subset of active resource constraints is used. [Copyright &y& Elsevier]

Abstract: This paper analyzes aggregate personal motor-vehicle travel within a simultaneous model of aggregate vehicle travel, fleet size, fuel efficiency, and congestion formation. We measure the impacts of driving costs on congestion, and two other well-known feedback effects affecting motor-vehicle travel: its responses to aggregate road capacity (“induced demand”) and to driving costs including those caused by fuel-economy improvements (“rebound effect”). We measure these effects using cross-sectional time series data at the level of US states for 1966 through 2004. Results show that congestion affects the demand for driving negatively, as expected, and more strongly when incomes are higher. We decompose induced demand into effects from increasing overall accessibility of destinations and those from increasing urban capacity, finding the two elasticities close in magnitude and totaling about 0.16, somewhat smaller than most previous estimates. We confirm previous findings that the magnitude of the rebound effect decreases with income and increases with fuel cost, and find also that it increases with the level of congestion. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a stochastic integer programming model for managing air traffic inbound to an airport when both the airport itself and its approach routes are subject to adverse weather. In the model, ground delay decisions are static, while those on rerouting are dynamic. The decision variables in the model are aggregate number of flights planned to arrive at various capacity constrained resources. The model does not directly assign arrival times to individual flights. Therefore, in context of Collaborative Decision Making, which is the governing philosophy of the air traffic management system of the United States, the solutions from the dynamic rerouting model can be directly fed to some resource allocation algorithm that assigns routes and release times to individual flights or to the airlines who operate them. When adverse weather blocks or severely limits capacity of terminal approach routes, rerouting flights onto other approaches yields substantial benefits by alleviating high ground delays. Our experimental results indicate that making rerouting decisions dynamically results in 10–15% delay cost reduction compared to static rerouting, and about 50% less delay cost compared to a “pure” ground holding strategy (i.e., no rerouting). In contrast to static rerouting, the dynamic rerouting capability results in making rerouting decisions that are better matched to realized weather conditions. Lower total expected delay cost is achieved by delaying the rerouting decisions for flights until they reach the divergence point between alternative routes, and hence exploiting updated information on weather while making those decisions. In cases where the airport is the main, but not the only, bottleneck, the dynamic rerouting model may assign higher ground delays so that the rerouting decisions can be deferred until more information on en route weather becomes available. [Copyright &y& Elsevier]

Abstract: This paper presents a new model and solution methodology for the arrival slot reallocation problem faced by airlines when responding to a ground delay program (GDP). The objective is to reassign the flights in the GDP to time slots made available by the Federal Aviation Administration (FAA) such that flight delay and passenger missed connection costs are minimized. The problem is formulated as a dynamic program and solved with the help of branch and bound. Using data provided by American Airlines, initial tests showed that while the results were good for relatively small instances, as more flights were included, computation times grew exponentially. Given that the problem needs to be solved quickly in practice, the methodology was incorporated in a rolling horizon framework where larger problems are split into smaller subproblems and solved sequentially. This led to some degradation in solution quality but there was still considerable cost savings compared to the initial slot assignments proposed by the FAA. Computational experiments with both real and randomly generated data confirmed that problems of practical size could be solved within 5min. [Copyright &y& Elsevier]

In this paper we present a parallel regret insertion heuristic to solve a dial-a-ride problem with time windows. A new route initialization procedure is implemented, that keeps into account both the spatial and the temporal aspects of the problem, and a regret insertion is then performed to serve the remaining requests. The considered operating scenario is representative of a large-scale dial-a-ride program in Los Angeles County. The proposed algorithm was tested on data sets of 500 and 1000 requests built from data of paratransit service in this area. The computational results show the effectiveness of this approach in terms of trading-off solution quality and computational times. The latter measure being especially important in large-scale systems where numerous daily requests need to be processed. [Copyright &y& Elsevier]

This paper formulates and applies a unified mixed-logit framework for joint analysis of revealed and stated preference data that accommodates a flexible competition pattern across alternatives, scale difference in the revealed and stated choice contexts, heterogeneity across individuals in the intrinsic preferences for alternatives, heterogeneity across individuals in the responsiveness to level-of-service factors, state-dependence of the stated choices on the revealed choice, and heterogeneity across individuals in the state-dependence effect. The estimation of the mixed logit formulation is achieved using simulation techniques that employ quasi-random Monte Carlo draws. The formulation is applied to examine the travel behavior responses of San Francisco Bay Bridge users to changes in travel conditions. The data for the study are drawn from surveys conducted as part of the 1996 San Francisco Bay Area Travel Study. The results of the mixed logit formulation are compared with those of more restrictive structures on the basis of parameter estimates, implied trade-offs among level-of-service attributes, heterogeneity and state-dependence effects, data fit, and substantive implications of congestion pricing policy simulations. [Copyright &y& Elsevier]