A co-integration approach to forecasting freight rates in the dry bulk shipping sector

In this paper, a vector autoregressive model is developed for a sample of ocean dry bulk freight rates. Although the series of freight rates are themselves found to be non-stationary, thus precluding the use of many modelling methodologies, evidence provided by cointegration tests points to the existence of stable long-term relationships between the series. An assessment of the forecasts derived from the model suggests that the specification of these long-term relationships does not improve the accuracy of short- or long-term forecasts. These results are interpreted as a corroboration of the efficient market hypothesis.     

GIS-based identification and visualization of multimodal freight transportation catchment areas

Transportation - To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure...     

Selection of techniques for reducing shipping NOx and SOx emissions

This paper develops a subjective generic methodology for providing ship owners with a transparent evaluation tool for selecting their preferred NOx and SOx control techniques. We quantitatively analyse the merits of the control methods available in marine air pollution control practice using data collected from shipping companies, shipyards and maritime academies. We also prioritize the applicable control techniques with respect to operational shipping environments.     

Mapping and evaluating the complexity of information flows in freight transport chains

ABSTRACT

The role of information in the efficient management of freight transport systems is well acknowledged. Administrative functions, such as negotiation or payments, involve intensive communication, while the production of the transport service relies heavily on the sharing of information (e.g. track and trace). Yet, specific literature on information flows is relatively scarce. This paper sets out to contribute to filling in this gap. Firstly, it elaborates a set of maps of the information flows for four conceptualisations – direct link, corridor, hub and spoke, and connected hubs – of maritime freight transport, initially proposed by Woxenius (2007, “Generic Framework for Transport Network Designs: Applications and Treatment in Intermodal Freight Transport Literature.” Transport Reviews 27 (6): 733–749. doi:10.1080/01441640701358796). The results reveal a high degree of similarity between import and export services. Secondly, it assesses the complexity of each conceptualisation’s flow network using the concept of entropy. Flows are characterised along four variables: involved agents, timing, and contents and message type. Additionally, a new taxonomy is proposed to categorise the messages. The results evidence the heterogeneous contributions of each category to the complexity of the flows. Finally, the results also reveal that the information flows for the tasks in the pre-transport stage are not specific to the conceptualisation in question and are of comparable complexity to the flows occurring during the transport stage.     

灰色BP网络串联式组合模型在铁路客货运输量预测中的应用

针对铁路客货运输量发展趋势的研究,建立一种基于灰色理论和BP神经网络的串联式组合预测模型。该模型首先用同一组数据序列建立不同参数的灰色方程,然后用各灰色方程分别预测,最后将各灰色方程预测的结果进行BP神经网络非线性组合,形成串联式组合预测模型。对湖南省铁路客货运量进行分析预测,结果表明:该组合模型预测的准确性高于单独使用灰色模型的准确性,是一种可靠有效的预测方法。     

State-of-the art of freight forecast modeling: lessons learned and the road ahead

In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.     

The effectiveness and costs of speed reductions on emissions from international shipping

Greenhouse gas emissions from international shipping are an increasing concern. The paper evaluates whether vessel speed reduction can be a potentially cost-effective CO₂ mitigation option for ships calling on US ports. By applying a profit-maximizing equation to estimate route-specific, economically-efficient speeds, we explore policy impacts of a fuel tax and a speed reduction mandate on CO₂ emissions. The profit-maximizing function incorporates opportunity costs associated with speed reduction that go unobserved in more traditional marginal abatement cost analyses. We find that a fuel tax of about $150/ton fuel will lead to average speed-related CO₂ reductions of about 20–30%. Moreover, a speed reduction mandate targeted to achieve 20% CO₂ reduction in the container fleet costs between $30 and $200 per ton CO₂ abated, depending on how the fleet responds to a speed reduction mandate.     

Vulnerability evaluation of freight railway networks using a heuristic routing and scheduling optimization model

Transportation - Railway network is an integral part of the economy of many countries. Identifying critical network elements can help network executives to take appropriate preventive actions...     

Joint optimization of freight facility location and pavement infrastructure rehabilitation under network traffic equilibrium

Establishment of industry facilities often induces heavy vehicle traffic that exacerbates congestion and pavement deterioration in the neighboring highway network. While planning facility locations and land use developments, it is important to take into account the routing of freight vehicles, the impact on public traffic, as well as the planning of pavement rehabilitation. This paper presents an integrated facility location model that simultaneously considers traffic routing under congestion and pavement rehabilitation under deterioration. The objective is to minimize the total cost due to facility investment, transportation cost including traffic delay, and pavement life-cycle costs. Building upon analytical results on optimal pavement rehabilitation, the problem is formulated into a bi-level mixed-integer non-linear program (MINLP), with facility location, freight shipment routing and pavement rehabilitation decisions in the upper level and traffic equilibrium in the lower level. This problem is then reformulated into an equivalent single-level MINLP based on Karush–Kuhn–Tucker (KKT) conditions and approximation by piece-wise linear functions. Numerical experiments on hypothetical and empirical network examples are conducted to show performance of the proposed algorithm and to draw managerial insights.     

Assessing the energy and greenhouse gas emissions mitigation effectiveness of potential US modal freight policies

This paper estimates the total embodied energy and emissions modal freight requirements across the supply chain for each of over 400 sectors using Bureau of Transportation Statistics Commodity Flow Survey data and Bureau of Economic Analysis economic input-output tables for 2002. Across all sectors, direct domestic truck and rail transportation are similar in magnitude for embodied freight transportation of goods and services in terms of ton-km. However, the sectors differ significantly in energy consumption, greenhouse gas emissions, and costs per ton-km. Recent pressure to reduce energy consumption and emissions has motivated a search for more efficient freight mode choices. One solution would be to shift freight transportation away from modes that require more energy and emit more (e.g., truck) to modes that consume and emit less (e.g., rail and water).Our results show there are no individual sectors for which targeting changes would significantly decrease the total freight transportation energy and emissions, therefore we have also looked at the prospect of policies encouraging many sectors to shift modes. There are four scenarios analyzed: (1) shifting all truck to rail, shifting top 20% sector mode choice, (2) based on their emissions, (3) based on a multi-attribute analysis, and (4) increasing truck efficiency (e.g., mpg). Increasing truck efficiency by 10% results in similar energy and emissions reductions (approximately 7% for energy and 6% for emissions) as targeting the top 20% of sectors when selected based on emissions, whereas selecting the top 20% based on availability to shift from truck results in slightly less reductions of energy and emissions. Implementing policies to encourage higher efficiency in freight trucks may be a sufficient short term goal while efforts to reduce truck freight transportation through sectoral policies are implemented in the long term.     

道路货运成本结构与通行费下调效应分析

"超限超载"的产生与发展,蕴藏着深层的内在运作机理,包含着复杂的经济社会原因.其中道路货物运输价格体系的长期扭曲失衡与成本结构的有欠合理是一个不容忽视的根本原因.为此,公路所开展了货运市场运价与成本"治超"专项研究,先后历时一个多月,行程数千公里,深入7省市运输一线,对21家典型企业和个体业户的23辆货车、24条线路进行了跟踪调查,获取了大量的第一手资料与原始数据,并对此进行了解析.     

Rail freight transport and demand requirements: an analysis of attribute cut-offs through a stated preference experiment

This paper analyses the choice between road and rail in Spain where rail market share for freight is still residual. Discrete choice models are estimated with data obtained through a two-phase fieldwork, thus allowing us to carry out a stated preference efficient design for each interviewee. We analyse the existence of attribute cut-offs and the presence of a segment of the population with a zero value of frequency. Our results show that ignoring the existence of cut-offs and segments of the population with polarised valuations can lead to erroneous conclusions in terms of the possibilities of rail for absorbing significant quota.     

Large-scale network distribution of pooled empty freight cars over time,with limited substitution and equitable benefits

A set of models and procedures is described for finding the optimal distribution of empty freight cars owned by the railroads participating in a pooling agreement. A distinction is drawn between a system focus, in which the emphasis is on minimizing total cost, and a company focus, in which the benefits of the agreement to the individual railroads are emphasized. Limited car substitution is accounted for by combining interchange costs with distribution costs, and incorporating interchange possibilities and prohibitions into the network structure. Temporal variations in car supply and demand levels are also taken into account. A large-scale network algorithm is used in conjunction with decomposition to obtain solutions which show for a given time horizon how much equity can be achieved in the balance of savings among the railroads involved and at what cost. Results using actual operating data are reported.     

Joint modeling of evacuation departure and travel times in hurricanes

Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an inter-relationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.

     

大型沉管隧道干坞的设计与研究

文章主要从干坞的选址、方案比选、防汛体系、边坡、基底处理以及边坡稳定计算和基底变形计算等方面总结了干坞设计的一些经验.     

Freight generation and geographical effects: modelling freight needs of establishments in developing economies and analyzing their geographical disparities

Transportation - Freight generation (FG) models are important to transportation authorities and planning agencies as they can be used to forecast local/regional/state/national freight movements for...     

A sequential approach to exploiting the combined strengths of SP and RP data: Application to freight shipper choice

The possibility of and procedure for pooling RP and SP data have been discussed in recent research work. In that literature, the RP data has been viewed as the yardstick against which the SP data must be compared. In this paper we take a fresh look at the two data types. Based on the peculiar strengths and weaknesses of each we propose a new, sequential approach to exploiting the strengths and avoiding the weaknesses of each data source. This approach is based on the premise that SP data, characterized by a well-conditioned design matrix and a less constrained decision environment than the real world, is able to capture respondents' tradeoffs more robustly than is possible in RP data. (This, in turn, results in more robust estimates of share changes due to changes in independent variables.) The RP data, however, represent the current market situation better than the SP data, hence should be used to establish the aggregate equilibrium level represented by the final model. The approachfixes the RP parameters for independent variables at the estimated SP parameters but uses the RP data to establish alternative-specific constants. Simultaneously, the RP data are rescaled to correct for error-in-variables problems in the RP design matrixvis-à- vis the SP design matrix. All specifications tested are Multinomial Logit (MNL) models.The approach is tested with freight shippers' choice of carrier in three major North American cities. It is shown that the proposed sequential approach to using SP and RP data has the same or better predictive power as the model calibrated solely on the RP data (which is the best possible model for that data, in terms of goodness-of-fit figures of merit), when measured in terms of Pearson's Chi-squared ratio and the percent correctly predicted statistic. The sequential approach is also shown to produce predictions with lower error than produced by the more usual method of pooling the RP and SP data.     

Freight transport demand revisited: A microeconomic view of multimodal, multicharacteristic service uncertainty and the demand for freight transport

This paper provides a unifying framework for micro-based models of freight transport demand. An expected utility of profits model is posed that clarifies the conditions under which a system of equations (continuous) regression model should be used as opposed to the conditions that make a quantal choice model appropriate. The model also indicates the nature of subjective data that is necessary for estimating demand. In general, the conditions that give rise to the appropriate use of a quantal choice model imply that the needed subjective information is completely contained in the subjective probability distributions on the service characteristics; the utility function itself is irrelevant.     

The determinants of car ownership in England and Wales from anonymous 2001 census data

Within the transportation research literature, the attempt to understand and predict the level of car ownership is probably one of the most popular areas of study. The primary reason for this is understandable, having access to a vehicle increases an individual’s (or their household’s) travel options, leading to greater mobility. Secondary reasons for this scrutiny include the need to predict future transport investment in road infrastructure and the commercial demand for new vehicles. This paper attempts to predict the level of household car ownership as a function of the characteristics of the household and the individuals that make up the household. The primary data source for this study comes from the 2001 United Kingdom Census and the analysis methods used are from the discipline of data mining. The results of this study are in line with those from previous research but show a potential to predict the higher levels of household car ownership with greater accuracy than other similar studies.