Network concentration and airport congestion in a post de-regulation context: A case study of Brazil 2000–2010

This paper empirically investigates the main drivers of airline network concentration in an air transport market subject to rapid growth. We consider the Brazilian air transport industry of the 2000s, in which network concentration rapidly increased and was followed by a period of massive flight delays and cancelations, which resulted in the “big blackout” of 2006–2007. We develop an econometric model of network concentration, accounting for demand, cost and competition variables that may affect the propensity of carriers to concentrate flights and passenger connections on a few airports of a network. The main focus of the paper is on the relation between networks leading to the problems of the blackout episode. We investigate the dynamic pattern of the evolution of concentration before and after the abnormal period of operations and find that concentration began to rise at least six quarters before, and persisted at a high level until two quarters after the blackout – and then plunged steeply toward the end of the decade. We believe that our analysis contributes to an improved understanding of the behavior of air transport systems subject to network concentration and congestion. With respect to methodology, we suggest and employ the use of alternative measures of network concentration to check the robustness and validity of our results.     

Interregional trade network analysis for road freight transport in Greece

This paper suggests a flexible decision support framework for the strategic planning of a freight transport hub network in Greece. The proposed methodology treats practical aspects related to the optimal number, location and geographical covering of hubs, through the network analysis of interregional trade, based on original survey data for road freight flows during 2004–2012. The results offer insights into the hierarchical structure of the network and related investment priorities, as the hub role of a prefecture is found to be strongly influenced by high population densities and manufacturing specialization, and its location along highway corridors.     

Analysis of the Chinese Airline Network as multi-layer networks

This paper encapsulates the Chinese Airline Network (CAN) into multi-layer infrastructures via the “k-core decomposition” method. The network is divided into three layers: Core layer, containing airports of provincial capital cities, is densely connected and sustains most flight flow; Bridge layer, consisting of airports in Tier 2 and Tier 3 cities, mainly connects two other layers; and Periphery layer, comprising airports of remote areas, sustains little flight flow. Moreover, it is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundant.     

Exploring the relationship between truck load capacity and traffic accidents in the European Union

Applying econometric techniques to EU28 panel data and controlling for explanatory variables such as road types, we find that increased truck load capacity does not necessarily aggravate road traffic safety. Specifically, heavy trucks do not seem to be linked with greater numbers of traffic fatalities/accidents, medium trucks appear to be the worst performers in terms of fatalities, and light trucks seem to be the worst for accidents. In summary, our results clarify the complex relationship between truck load capacity and road safety, pointing to the existence of a negative correlation for accidents per capita and an inverse U-shaped curve for fatalities per capita.     

The effect of continuous improvement capacity on the relationship between of corporate social performance and business performance in maritime transport in Singapore

Drawing on organisational learning theory, this study argues that shipping firms with high continuous improvement capacities have better success in transforming corporate social performance (CSP) into business performance. Survey data were collected from 223 shipping firms in Singapore and analysed using multi-sampling analysis, hierarchical regression modelling, and simple slope analysis. The results support the study’s argument and show that business performance is maximised when continuous improvement programmes targeted at CSP are carried out at a gradual pace and at regular intervals. This implies that shipping firms should adopt a dynamic, value-driven approach to improving CSP.     

On the multi-dimensionality and sampling of air transport networks

Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

Mediation Between Tourism Contribution and Economic Growth in the Greater Mekong Subregion

The objective of this paper is to determine the mediation between tourism contribution and economic growth in the Greater Mekong Sub-region (GMS) (i.e. Cambodia, Laos, Myanmar, Thailand and Vietnam). The sample period is semi-annual data from 1995 to 2013 and is also estimated by bootstrap panel cointegration, Panel Fully Modified Ordinary Least Squares method and the Sobel test. The results show that the variables are cointegrated as three models; economic growth is running to tourism's total contribution to GDP and international tourism expenditure for passenger transport whereas international tourism expenditure for passenger transport is running to tourism's total contribution to GDP. Therefore, this study finds that international tourism expenditure for passenger transport is a mediator and has partial mediation. The GMS needs to develop every economic sector and improve the potential of the transport sector as a regional integration project which supports the sustainable development of the region.     

A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting

Air transport demand forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. Total passengers are used as a proxy for air transport demand. However, the air passenger time series usually has a complex behavior due to their irregularity, high volatility and seasonality. This paper proposes a new hybrid approach, combining singular spectrum analysis (SSA), adaptive-network-based fuzzy inference system (ANFIS) and improved particle swarm optimization (IPSO), for short-term air passenger traffic prediction. The SSA is used for identifying and extracting the trend and seasonality of air transport demand and the artificial intelligence technologies, including ANFIS and IPSO, are utilized to deal with the irregularity and volatility of the demand. The HK air passenger data are collected to establish and validate the forecasting model. Empirical results clearly points to the enormous potential that the proposed approach possesses in air transport demand forecasting and can be considered as a viable alternative.     

大秦铁路榆次站枢纽现状分析及扩能探讨

铁路枢纽运转效率的高低直接影响着铁路运输企业的竞争力。随着石太、侯月、丰沙大线三线增量及车流的调整，榆次站枢纽的地位显得尤为重要。调研榆次站枢纽现状，分析车流现状、各场能力及运用情况，针对既有运输组织方式下局部运转不畅的问题，提出优化作业组织、人员配备、编组计划等提升榆次站枢纽能力的扩能建议。