Strategic fleet size planning for maritime refrigerated containers

In the present economic climate, it is often the case that profits can only be improved, or for that matter maintained, by improving efficiency and cutting costs. This is particularly notorious in the shipping business, where it has been seen that the competition is getting tougher among carriers, thus alliances and partnerships are resulting for cost effective services in recent years. In this scenario, effective planning methods are important not only for strategic but also operating tasks, covering their entire transportation systems. Container fleet size planning is an important part of the strategy of any shipping line. This paper addresses the problem of fleet size planning for refrigerated containers, to achieve cost-effective services in a competitive maritime shipping market. An analytical model is first discussed to determine the optimal size of an own dry container fleet. Then, this is extended for an own refrigerated container fleet, which is the case when an extremely unbalanced trade represents one of the major investment decisions to be taken by liner operators. Next, a simulation model is developed for fleet sizing in a more practical situation and, by using this, various scenarios are analysed to determine the most convenient composition of refrigerated fleet between own and leased containers for the transpacific cargo trade.     

The Arctic Shipping and Environmental Management Agreement: A Regime for Marine Pollution

Climate change is predicted to have particularly challenging impacts throughout the Arctic. For instance, there is a great probability of sea ice melting, leading to increased vessel traffic and oil pollution. Eight major nation-states have vested concerns in the potential opening of Arctic sea routes. They each have pledged to take protective action through the Arctic Council. However, there is still a need to develop an international institution to simultaneously address Arctic marine pollution and protect the needs of these states. This article seeks to design a legally binding regime for oil pollution control in the Arctic. In creating this new regime, we shall utilize several shared design elements of historically effective international regimes and take advantage of the legal innovations of these agreements. Our new regime, entitled the Arctic Shipping and Environmental Management Agreement (ASEMA), will take into account both the economic and environmental interests of the parties involved.     

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

The precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.     

Generating twins to build weekly time use data from multiple single day OD surveys

Origin-Destination surveys, which are regularly conducted in many cities (to calibrate transport models), contain indirect information on individual time use that can be recovered through the declared trip purpose. Although this data source is very rich, it has two limitations for the calibration of time use models: the level of disaggregation regarding time use is constrained by the definition of trip purposes, and the information gathered on different time periods is usually obtained from different individuals. In this paper we propose a new method to overcome the second limitation, transforming the original daily observations into individual-weeks. For every working day observation we build Saturday and Sunday “twins” as a convex combination of observed weekend individuals such that the distance between the attributes of the working day individual and the synthetic twin is minimized. We applied this procedure to the Santiago OD survey, and generated a database of weekly observations particularly rich for model calibration and segmentation.     

Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate ‘practical and sensible’ groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.