8. AI in Adapting the Human Travel Behaviour

This chapter identifies some of the basic characteristics of human travel behaviour and investigates how AI can influence it towards meeting the strategic goals of sustainable and climate friendly mobility. It stresses the view that human travel behaviour and transportation system operation are two interlinked elements that interact in many and complex ways. Travel behaviour has been a major field of study in the Transportation science for many decades, and this chapter summarises many of the findings so far and explores ways in which AI can influence human behaviour to adapt and “match” the available transportation system capacity and make it more aware of the climate change and its impacts on transport and mobility. The chapter refers to the various Transportation Demand Management (TDM) policies (e.g., incentives, information dissemination campaigns, and other) and how AI can support their formulation and implementation. AI is seen as the catalyst for a whole new series of TDM models and policies so its potential application areas in this field are many and these are presented and discussed in detail. To support the view that when TDM policies are properly designed and consistently applied, they can significantly reduce vehicle travel even without the use of AI, the chapter refers to a recent report by the Australian Victoria Transport Institute where a series of data are presented showing the results of specific TDM policies that have been applied in the past in several urban areas. As TDM policies tend to have synergistic effects, their results are more pronounced if implemented as an integrated program that includes a combination of measures of different but complimentary nature. With the wider use of AI in TDM, it is reasonable to expect that the impacts will be higher, more widespread, and varied. Of particular interest is the list of application areas of AI in this field which are given in this chapter. These applications are aimed at influencing travel demand while at the same time optimising the transport system’s performance and operation.