Integrating Climate and Ocean Change Vulnerability into Conservation Planning

Tropical coastal and marine ecosystems are particularly vulnerable to ocean warming, ocean acidification, and sea-level rise. Yet these projected climate and ocean change impacts are rarely considered in conservation planning due to the lack of guidance on how existing climate and ocean change models, tools, and data can be applied. Here, we address this gap by describing how conservation planning can use available tools and data for assessing the vulnerability of tropical marine ecosystems to key climate threats. Additionally, we identify limitations of existing tools and provide recommendations for future research to improve integration of climate and ocean change information and conservation planning. Such information is critical for developing a conservation response that adequately protects these ecosystems and dependent coastal communities in the face of climate and ocean change.     

The impact of the Panama Canal expansion on Panama’s maritime cluster

The Panama Canal is currently in the process of a major expansion effort. After the new set of locks is opened in 2016, significantly larger ships can traverse the Canal. The current lock system has been in place for over 100 years, even though the size of ocean-going vessels has expanded considerably. One impact is on Panama’s maritime cluster. It is expected that the expansion will result in greater demand for many of the goods and services provided by the cluster. This article examines the economic impact of the Canal expansion on Panama’s maritime cluster. Clusters of economic activity can result in economies of agglomeration and supply chain network effects. Without these economies and network effects, clusters would not have a competitive advantage over businesses that are not in a cluster. It is expected that with a larger cluster, both agglomerative economies and network effects will increase. But, which cluster components will grow and which will not be affected to a great extent? To what extent will bottlenecks appear? These are some of the questions that this article addresses.     

A game-theoretical approach for modeling competitions in a maritime supply chain

ABSTRACT

To accommodate the structural changes in logistics, competitions along a maritime supply chain are more intensive than before. This paper adopts a two-stage noncooperative game-theoretical approach to model the horizontal and vertical interactions among liners and ports. In the first stage, Bertrand game is applied to model the interactions between two liners. When the shipping line decides which port to call, a multinomial Logit model is applied. In the second stage, Bertrand game is applied to model the competitions between two ports. Nash equilibrium is derived by solving the Bertrand games. A numerical example is provided as a case study.     

Using kinematic driving data to detect sleep apnea treatment adherence

People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea (OSA). We collected driving data and medication adherence from a sample of 75 OSA patients over 3.5 months. We converted speed and acceleration behaviors to symbols using symbolic aggregate approximation and converted these symbols to pattern frequencies using a sliding window. The resulting frequency data was matched with treatment adherence information. A random forest model was trained on the data and evaluated using a held-aside test dataset. The random forest model detects lapses in treatment adherence. An assessment of variable importance suggests that the important patterns of driving in classification correspond to route decisions and patterns that may be associated with drowsy driving. The success of this approach suggests driving data may be valuable for evaluating new treatments, analyzing side effects of medications, and that the approach may benefit other drowsiness detection algorithms.     

Comparative analysis of three user equilibrium models under stochastic demand

Recent empirical studies on the value of time and reliability reveal that travel time variability plays an important role on travelers' route choice decision process. It can be considered as a risk to travelers making a trip. Therefore, travelers are not only interested in saving their travel time but also in reducing their risk. Typically, risk can be represented by two different aspects: acceptable risk and unacceptable risk. Acceptable risk refers to the reliability aspect of acceptable travel time, which is defined as the average travel time plus the acceptable additional time (or buffer time) needed to ensure more frequent on‐time arrivals, while unacceptable risk refers to the unreliability aspect of unacceptable late arrivals (though infrequent) that have a travel time excessively higher than the acceptable travel time. Most research in the network equilibrium based approach to modeling travel time variability ignores the unreliability aspect of unacceptable late arrivals. This paper examines the effects of both reliability and unreliability aspects in a network equilibrium framework. Specifically, the traditional user equilibrium model, the demand driven travel time reliability‐based user equilibrium model, and the α‐reliable mean‐excess travel time user equilibrium model are considered in the investigation under an uncertain environment due to stochastic travel demand. Numerical results are presented to examine how these models handle risk under travel time variability.     

Deregulation of inter‐city coach services in Britain

Public transport in Britain has traditionally operated under conditions of regulation. The 1930 Road Traffic Act imposed controls upon the quantity and quality of transport regulations, with an abolition of the quantity controls associated with inter‐city coach services. The principal objectives of the Act were to eliminate restriction upon entry to the market and to promote competition between coach operators.

This paper is based on research which was undertaken to establish the impact of the 1980 Transport Act upon express (inter‐city) coach services in Britain. The effects of the legislation are examined in relation to the experience of various coach operators, British Rail, and the inter‐city traveller. This provides evidence of the way in which the supply of, and demand for, public transport behaves in a competitive, deregulated environment. It is shown that at least in net terms, the consumer has enjoyed short‐term benefits. Greater uncertainty surrounds the long‐term situation. The main competition has been confined to the principal trunk routes and has taken place between the state‐owned undertaking of National Express and British Rail. The outcome of deregulation for the private sector has been disappointing.

The paper identified similarities which exist between inter‐city and local public transport services. In this context, the lessons derived from express deregulation are employed to suggest likely developments in the deregulation of stage carriage (local) services in Britain.     

Traffic forecasts under uncertainty and capacity constraints

Traffic forecasts provide essential input for the appraisal of transport investment projects. However, according to recent empirical evidence, long-term predictions are subject to high levels of uncertainty. This article quantifies uncertainty in traffic forecasts for the tolled motorway network in Spain. Uncertainty is quantified in the form of a confidence interval for the traffic forecast that includes both model uncertainty and input uncertainty. We apply a stochastic simulation process based on bootstrapping techniques. Furthermore, the article proposes a new methodology to account for capacity constraints in long-term traffic forecasts. Specifically, we suggest a dynamic model in which the speed of adjustment is related to the ratio between the actual traffic flow and the maximum capacity of the motorway. As an illustrative example, this methodology is applied to a specific public policy that consists of suppressing the toll on a certain motorway section before the concession expires.     

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.     

A reliability‐based network design problem

This paper presents a reliability‐based network design problem. A network reliability concept is embedded into the continuous network design problem in which travelers' route choice behavior follows the stochastic user equilibrium assumption. A new capacity‐reliability index is introduced to measure the probability that all of the network links are operated below their capacities when serving different traffic patterns deviating from the average condition. The reliability‐based network design problem is formulated as a bi‐level program in which the lower level sub‐program is the probit‐based stochastic user equilibrium problem and the upper level sub‐program is the maximization of the new capacity reliability index. The lower level sub‐program is solved by a variant of the method of successive averages using the exponential average to represent the learning process of network users on a daily basis that results in the daily variation of traffic‐flow pattern, and Monte Carlo stochastic loading. The upper level sub‐program is tackled by means of genetic algorithms. A numerical example is used to demonstrate the concept of the proposed framework.     

An actuary method for the estimation and comparison of urban bus transit cost of services

This paper presents a new cost allocation method developed for estimating the fully-allocated costs of the excess peak hour bus transit service provided by two public transit systems. The estimates were produced for the explicit purpose of carrying out a realistic comparison of these costs and the costs of the potential provision of the same service by private operators. The method utilizes the same service data for the analysis. The pragmatic estimation of the fully allocated costs of service by the public and private sectors enables a more accurate estimation of potential cost savings. Sensitivity analysis was also performed using the same costing procedure to determine the range of cost savings that are feasible in a competitive contract arrangement of private sector providers. The new method separates costs of the direct provision of service from other indirect costs and thus facilitates the identification of each cost item and its significance in comparative cost estimates.