Space,money, life-stage,and the allocation of time

Demographic, socioeconomic, seasonal, and scheduling factors affect the allocation of time to various activities. This paper examines those variables through exploration of the 1990 Nationwide Personal Transportation Survey, which has been inverted to track activity duration. Two key issues are considered. First, how much can activity duration and frequency explain travel duration? The analysis shows activity duration has positive and significant effects on travel duration, supporting recent arguments in favor of activity based models. Second, which recent trend is the main culprit in the rise in travel: suburbanization, rising personal incomes, or female labor force participation? This paper examines the share of time within a 24-hour budget allocated to several primary activities: home, work, shop, and other. The data suggest that income and location have modest effects on time allocation compared with the loss of discretionary time due to working.

     

Assimilation of altimetric data in the mid-latitude oceans using the Singular Evolutive Extended Kalman filter with an eddy-resolving, primitive equation model

A new data assimilation scheme has been elaborated for ocean circulation models based on the concept of an evolutive, reduced-order Kalman filter. The dimension of the assimilation problem is reduced by expressing the initial error covariance matrix as a truncated series of orthogonal perturbations. This error sub-space evolves during the assimilation so as to capture the growing modes of the estimation error. The algorithm has been formulated in quite a general fashion to make it tractable with a large variety of ocean models and measurement types. In the present paper, we have examined three possible strategies to compute the evolution of the error subspace in the so-called Singular Evolutive Extended Kalman (SEEK) filter: the steady filter considers a time-independent error sub-space, the apprentice filter progressively enriches the error sub-space with the information learned from the innovation vector after each analysis step, and the dynamical filter updates the error sub-space according to the model dynamics. The SEEK filter has been implemented to assimilate synthetic observations of the surface topography in a non-linear, primitive equation model that uses density as vertical coordinate. A simplified box configuration has been adopted to simulate a Gulf Stream-like current and its associated eddies and gyres with a resolution of 20 km in the horizontal, and 4 levels in the vertical. The concept of twin experiments is used to demonstrate that the conventional SEEK filter must be complemented by a learning mechanism in order to model the misrepresented tail of the error covariance matrix. An approach based on the vertical physics of the isopycnal model, is shown particularly robust to control the velocity field in deep layers with surface observations only. The cost of the method makes it a suitable candidate for large-size assimilation problems and operational applications.     

The Mixed Logit model: The state of practice

The mixed logit model is considered to be the most promising state of the art discrete choice model currently available. Increasingly researchers and practitioners are estimating mixed logit models of various degrees of sophistication with mixtures of revealed preference and stated choice data. It is timely to review progress in model estimation since the learning curve is steep and the unwary are likely to fall into a chasm if not careful. These chasms are very deep indeed given the complexity of the mixed logit model. Although the theory is relatively clear, estimation and data issues are far from clear. Indeed there is a great deal of potential mis-inference consequent on trying to extract increased behavioural realism from data that are often not able to comply with the demands of mixed logit models. Possibly for the first time we now have an estimation method that requires extremely high quality data if the analyst wishes to take advantage of the extended behavioural capabilities of such models. This paper focuses on the new opportunities offered by mixed logit models and some issues to be aware of to avoid misuse of such advanced discrete choice methods by the practitioner.     

Safety in the road environment: a driver behavioural response perspective

The existing literature on road safety suggests that a driver's perception of safety is an important influence on their driving behaviour. A challenging research question is how to measure the perception of safety given the complex interactions among drivers, vehicles and the road setting. In this paper, we investigate a sample of driver evaluations of the perception of safety associated with a set of typical road environments. A roundabout was selected as the context for the empirical study. Data was obtained by a computerised survey using the video-captured road and traffic situations. A controlled experiment elicited driver responses when faced with a mixture of attributes that describe the roundabout environment. An ordered probit model identified the contribution of each attribute to the overall determination of the perception of safety. An indicator of perceived safety was developed for a number of typical road and traffic situations and for different driver segments.     

Risk management methods for the liner shipping industry: the case of the Bunker Adjustment Factor

Through the implementation of the Bunker Adjustment Factor (BAF), a shipowner is able to transfer the risk of fuel price fluctuation to the shipper. This and other surcharges are generally undesired by shippers who must pay this fee. The research presented provides an alternative method of risk management that is more effective than the current method, while responding to customer service demands for simplified tariffs. By strategic use of commodity futures, the shipowner is able to better reduce his/her risk exposure, transferring it to willing parties at the futures exchanges, thereby eliminating the need for the bunker surcharge.     

Impacts of Terrestrial and Shoreline Stressors on Eelgrass in Puget Sound: An Expert Elicitation

We used expert elicitation to examine potential responses of eelgrass to several restoration strategies in Puget Sound. Restoration strategies included shoreline armor removal and modification, removal and modification of overwater structures, and efforts to improve water clarity via reductions in anthropogenic nutrient and sediment loadings. Expert responses indicated a general belief that reducing stressors would increase eelgrass cover; however, responses varied greatly among stressors. Our analyses revealed that removal of overwater structures, nutrient loading and shoreline armoring will have significantly larger effects on eelgrass recovery than would removal of sediment loading, with removal of overwater structures having the largest effect. We then used a probabilistic model to estimate what actions, singularly or in combination, could yield a large increase in eelgrass cover. Reducing single stressors could, in theory, result in recovery of eelgrass in Puget Sound; however, the magnitude of actions required would be so great that it is likely not practical. In contrast, we identified combinations of smaller reductions of stressors that could achieve significant eelgrass recovery. For example, a 40% reduction in overwater structures, combined with 20% reductions in shoreline armor, and nutrient and sediment loadings, was predicted to be one of the more feasible combinations of actions for meeting the target. The importance of eelgrass to Puget requires prompt input of scientific advice, and this work fills an important knowledge gap in the face of rapidly approaching legislative deadlines. While coded expert opinion of the sort we use here is a weak substitute for data, our work clarifies the current extent of scientific uncertainty that can guide management action in the near term and scientific research in the long term.     

Integrated Environmental Modeling and Assessment of Coastal Ecosystems: Application for Aquaculture Management

This article presents an approach that couples coastal ecosystem modeling with integrated environmental assessment methodologies to support coastal management. The focus is to support the development of an ecosystem approach to aquaculture management including interactions with watershed substance loading. A Chinese bay, with intense aquaculture and multiple catchment uses, and where significant modeling efforts were undertaken is used as a case study. The ecosystem model developed for this bay is used to run scenarios that test the local management strategy for nutrient reduction. The corresponding ecological and economic impacts of the managers’ scenarios are analyzed by means of the Differential Drivers-Pressure-State-Impact-Response (ΔDPSIR) analysis. Emphasis is given to the analysis of the eutrophication process in the bay including present eutrophic condition and the expected changes due to the simulated scenarios. For this purpose, the Assessment of Estuarine Trophic Status (ASSETS) screening model is a valuable tool to interpret and classify the data and model outputs regarding eutrophication condition and to evaluate the manageable level of the nutrient loading entering in the bay.     

Aggregation of common-metric attributes in preference revelation and implications for willingness to pay

There is a growing interest in process heterogeneity in the way that individuals evaluate packages of attributes in real or hypothetical markets and make choices. We consider the role of the relative magnitude of pairs of attributes that are defined on a common metric (e.g., minutes or dollars), to look at the extent to which attributes might be added in preference revelation, in contrast to the commonly adopted single rule of compensatory behaviour. The focus is on a choice model specification that allows for different treatments of pairs of attributes across a sample, in contrast to studies that impose a single rule on all observations, and that does not require supplementary information on whether specific individuals claimed to have added up attributes; rather we structure a non-linear utility function that permits a probabilistic aggregation of each attribute. We translate this into a willingness to pay for travel time-savings for car commuters, in the context of tolling roads in Sydney, and contrast it with the results from the additive model, and a model where self-stated attribute processing information is taken into account. The empirical evidence suggests that mean willingness to pay increases when the addition rule is accounted for. This is a potentially important message for environmental applications where two or more attributes have a common metric.     

Air quality impact assessment of NOx and PM due to diesel vehicles in Delhi

The impact of diesel vehicles on NO_x and PM emissions at various locations in Delhi is assessed using two line source models; the California line source version 4 and the Indian Institute of Technology Line Source. The models offer comparable results but both under predicting the observed values with the Indian Institute of Technology model predictions being slightly better. The analysis also identifies hotspots due to concentrations of NO_x and PM and their diurnal variations is found to be greater in at night hours.