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After many years of data scarcity in transportation-related sciences, we have now entered the era of big data. Large amounts of data are available from GPS devices, mobile phone traces, payment transactions, social media, and other sources. The opportunities that this new availability presents are enormous. High-quality data is available at very low or negligible cost. These data can be used to develop new tools, to explore and understand travel behavior and to formulate new policies. However, the challenges are also big: the access to the data is not guaranteed, confidentiality has to be considered, the capacity of processing and enriching these databases has to be developed, and only then will they become really useful for decision-making and for the definition of public policies. This chapter presents an overview of the current state of play, and discusses the future perspectives, focusing on the challenges of building new predictive models.

An airport’s capacity is in general indivisible and hence cannot be adjusted continuously, while the demand for airport services tends to increase over time as with the growing economy. Consequently, when new runways are completed, they are likely under-utilized, but as traffic increases over time, congestion occurs. It may therefore be optimal to vary airport charges over time by keeping charges low at the early stage of the infrastructure life cycle and raising charges towards the end of its life cycle. This chapter discusses the benefits of time-dependent charges by considering the airport’s economic impacts as well as the changing economic environment, such as the unemployment level. The analysis is based on a case study of Hong Kong International Airport. The authors attempt to demonstrate quantitatively the above airport pricing strategy, and to further show how to conduct a real-case cost-benefit analysis of airport pricing for policy application.

The Spatial Computable General Equilibrium (CGE) model is one of the powerful tools to understand the regional economic effects on transport development. In this model, Armington elasticities are known to be important for the properties of model behavior but are seldom estimated empirically in the domestic area. Armington elasticities in multi-regional trade in Japan are estimated in this chapter. The estimated elasticities are intended for use in the Spatial CGE model for transport policy. The authors suggest that estimation is possible for Japan, for which economic data are generally considered poor, provided appropriate account is taken of transport cost.

Transportation investment policy faces an unprecedented critical moment especially when traditional public funding sources shrink. How to allocate the limited funding source among different modes of transportation infrastructure to maintain their quality and function is not only a challenging decision, it also requires the knowledge and understanding of the relationships between transportation infrastructure and regional economic development. This study conducts a comparative assessment investigating this relationship with a focus on a multimodal perspective. Through the comparison of the authors’ previous evaluation studies conducted at different geographic scales in the US and using different data and methodologies, the regional economic impacts of different modes of transportation infrastructures are contrasted and discussed. Although transportation infrastructure is found to have a consistent positive impact on regional development across different studies, the effects vary considerably among different modes of transportation. Issues of impact assessment and policy implications are raised and discussed.