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This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

The purpose of this paper is to examine the effects of maritime and air transportation on the environment and economy of Iran. The authors specify two dynamic models of the environmental pollution and the economic growth. Then, the authors estimate the environmental and economic elasticities of maritime and air transportation in short run and long run in Iran during 1978–2012.

The authors estimate the environmental and economic elasticities of maritime elasticities in short and long run, using simultaneous equations system.

The findings indicate that the short- and long-run environmental pollution elasticities of maritime transportation are higher than those of the air ones. In addition, the economic growth elasticities are greater in the air transportation compared to maritime one. As a result, the maritime transportation is more pollutant and less productive in Iran in comparison with the air transportation.

The policymakers are advised to improve the infrastructure of maritime transportation from both the environmental and economic point of views. Consequently, the air transportation is considered as a cleaner and more beneficial transportation mode in Iran, where geographical position limits the maritime transport as a widespread transportation mode.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

The use of air cargo by low-income countries and the effects of freight charges on their export flows are described. This is accomplished by illustrating the difference between export flows from developing countries of perishable products and high-tech goods. Descriptive statistics are used to highlight the importance of trade that travels by air from these countries to the United States and the European Union. Subsequently, costs of air freight are estimated. A gravity model of trade measures the effect of these costs on export flows. Major institutional and regulatory constraints that may be halting additional trade that relies on air transportation, and the implications for economic growth, are identified.

Companies are adopting innovative methods for responsiveness and efficiency in the public transport sector. The implementation of air-taxi services (ATS) in the transport sector is a move in this direction. Air taxis have a two-pronged advantage as they can reduce travel times by avoiding traffic congestion and have the potential to reduce carbon footprint compared to traditional modes of public transportation. Many companies worldwide are developing and testing ATS for practical applications. However, many factors may play a significant role in adopting ATS in the transport sector. This paper attempts to unearth such critical success factors (CSFs) and establish the interrelationships between these factors.

Fifteen CSFs were identified by systematically reviewing the literature and taking experts' input. An integrated multi-criteria decision-making (MCDM) technique, Decision-Making Trial and Evaluation Laboratory-Analytic Network Process (DEMATEL-ANP [DANP]) was used to envisage the causal relationships between the identified CSF.

The results reveal that Govt Regulations (GOR), Skilled Workforce (SKF) and Conductive Research Environment (CRE) are the most influential factors that impact the adoption of ATS in the transport sector.

The research implications of these findings will help practitioners and policymakers effectively implement ATS in the public transportation sector.

This is the first kind of study that identifies and explores the different CSFs for ATS implementation in public transportation. The CSFs are evaluated with the help of a framework built with inputs from logistics experts. The study recognizes the CSFs for ATS implementation and provides a foundation for future research and smooth adoption of ATS.