Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Planning considerations for preservation and use of the national seashores

This article reviews the historical development of the category of national park known as “national seashore,”; and traces the evolution of basic planning and management policies that are applied to such areas. Also reviewed is the current sequential approach applied by the National Park Service in all its planning efforts, including a discussion of the impact of the National Environmental Policy Act on park planning. Several examples are provided to illustrate a variety of planning problems common to the sashore environment. Finally, comments are provided on the future promise of seashore and barrier island conservation and development, as well as the future role of other agencies and private organizations.     

Leadership and Commitment in the Royal Navy ☆

What is proposed here is a relatively simple procedure by which a fleet manager may select the best mix of ships to provide a given transport service. The method is confined to non-liner trades and assumes the availability of more than enough suitable ships. It further assumes that unneeded ships can be placed in idle status (or returned to owner if chartered in) without penalty.

The objective of the procedure is to select the mix of available ships and sea speeds that can perform the required service at maximum profitability to the owner.     

Examining the influence of firm performance on business risk-taking and the mediation effect of scale of operations in the container terminal industry

Container shipping and its related service sectors help accelerate globalization of the world economy. This industry has been experiencing rapid growth, prompting container terminal operators to increase their handling capacity in response. Providing container terminal services requires substantial capital investment in physical assets such as cargo handling facilities and information systems. On the other hand, operating container terminals is a long-term investment that typically spans several business cycles. Hence prudent asset management using appropriate tools is critical for container terminal operators to sustain their businesses. Generally, due to risk-adverseness, investors are unwilling to take more risk in their investment unless they can reap a higher return. Contrary to this argument, this study finds no direct influence of better firm performance as a proxy of higher return on business risk-taking by container terminal operators. Instead, scale of operations is positively associated with business risk-taking, suggesting that container terminal operators with a larger scale of operations are willing to take more business risk.     

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.     

Design and evaluation of an integrated inventory and transportation system

This paper proposes the adoption of an integrated inventory and transportation system (IITS) to minimize the total costs of inventory and transportation. A non-linear programing is developed by analyzing transportation and inventory costs with one supplier and many retailers in the distribution environment. The paper compares the proposed model with the traditional approach in computing total costs with numerical data. The results indicate that the total costs can be optimized by adopting integrated programing rather than the traditional approach, along with achieving improved customer service levels. In particular, sensitivity analysis is applied to determine the performance of the IITS under various transportation costs, holding costs and shortage costs. It shows that the transportation cost per unit is most sensitive in the proposed model. In this situation, the IITS is more effective for cost saving when set-up cost, holding and shortage costs are high, but is less effective for situations involving high per-unit transportation costs.     

Evolution of coastal hazards policies in the United States

The ocean and Great Lakes coasts of the United States are experiencing widespread economic and environmental damage from coastal flooding and erosion. During this century, public response to such coastal hazards has evolved haphazardly in response to particular disasters. Over time, however, the range of response has broadened as research has helped to refine public understanding of physical coastal processes, and specific disasters have been studied before longer term forms of institutional response have been formulated. Earlier reliance on engineered shoreline protection has been supplemented by beach nourishment, flood insurance, building and land use regulations, coastal zone planning, and other approaches. This article interprets the evolution of such public policy innovations in terms of a model that depicts the interaction of spatially differentiated systems of physical, legal, and cultural phenomena in the coastal context.     

The use of demand correlation in the modeling of air carrier departure delays as first‐order autoregressive random processes

A method for modeling air carrier departure delays at commercial‐service airports as autoregressive random processes is presented. This method employs the correlation of a priori demand data to significantly reduce prediction error in the optimal least‐squares estimator for additive white noise. The reduction factor of the prediction error is demonstrated to be on the order of 10² over that of the unbiased estimator. Copyright © 2011 John Wiley & Sons, Ltd.