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This study aims to explore the propensity of university students to use different sustainable transport modes, taking into account individual and specific trip characteristics, as well as students’ psychological traits (i.e. attitudes).

Using the transport mode preferences of 827 students who responded to a travel survey, a two-step analysis is conducted. The first step examines the effects of individual characteristics, travel experience and origin or destination features on students’ stated preferences (i.e. self-selected values assigned to personal attitudes). The second step analyses students’ travel mode choices, given their intrinsic mobility attitudes.

The results suggest that informing students about environmental issues increases their propensity to use sustainable mobility, leading to an average decrease in private transport usage of 5.8 per cent. Interestingly, improving the public transport service and promoting sustainable transport mobility have different impacts on individual campus areas. For campuses located in the city centre and in the historical hamlet, improvements in public transport are found to decrease solo driving by 3.3 per cent and 5.3 per cent, respectively. In suburban areas, this value increases to 9.5 per cent.

This work makes two contributions to the literature. First, it focuses on an unexplored setting, namely, that of a multi-campus university, with districts located in three different areas. This is used to explain how students are influenced by their travel experience and the cultural framework in which they are embedded. Second, the two-step analysis leads to a deeper understanding of the differences between attitudes and “intrinsic attitudes”, and their relative influence on the preferred alternative.

Air transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain.

The data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth.

According to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables.

The factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results.

Previous studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

In the last decades, low-cost carriers (LCCs) have generated several changes in the air market for both passengers and airports. Mainly for regional airports, LCCs have represented an important opportunity to improve their connectivity levels and passenger traffic. Furthermore, many regional airports have become key factors to regenerate the local economy by improving accessibility and stimulating several markets, such as tourism. However, the relationship between LCCs and airports is rather complex and the outcomes not always predictable. In order to analyze and understand better such relationship and its outcomes, this chapter discusses the main underlying factors identified in: relation with the regional air market (secondary/primary airports), balance of power (dominated/non-dominated airports), and industrial organization (bases/non-bases). Starting from the proposed Relative Closeness Index, which combines yearly airport passengers and distance between airport pairs, a large sample of European airports is analyzed. Then, a smaller sub-sample – which includes selected, significant case studies referring to mid-sized airports – is discussed in detail. Among the main findings, airports sharing their catchment area with others are in a very risky position, due to the potential mobility of LCCs, while geographically isolated airports in good catchment areas can better counterbalance the power of carriers.