The rapid development of autonomous vehicles (AV) has prompted considerable speculation on how these vehicles will ‘revolutionise’ the future of cities’ transport systems. It has been suggested that a large-scale adoption of AV would lead to safer roads, congestion-free cities and more public spaces as vehicles can be shared, and hence fewer parking spaces are needed. However, it is far from clear if these visions are likely to be realised and what this might imply for the future transport networks and policy agendas. Recent comments by the NSW Minister of Transport who stated “we have got on-demand movies, we want on-demand transport” 1 and “if I look at the ways in which we’re going to have to change our transport planning, our road networks, the way in which insurance is going to change, all of this is going to happen incredibly quickly and I want us to be front and centre” 2 underline how extensive the transport systems will change and how critical it is to plan for these changes. If these visions turn out to be the case, questions for which clarity is needed include:
Existing planning and operational management tools are largely unsuited to answering these questions because they rely on several assumptions about how investments in mobility through car ownership and licence holding influence the way people travel, how much people are willing to pay to save wasteful travel time, how the public transport network is structured and operated, and how traffic congestion control is currently implemented in cities. All of these assumptions may no longer hold in the driverless era, when one does not need to own a car or hold a driving licence to be able to use a car, when one can work or do worthwhile activities while travelling and hence travel time is no longer that wasteful, and when public transport can be ‘ordered’ in advance. In addition, the transitional mixture of human-driven cars and AV further exacerbates the complexity and uncertainties of transport systems that entails devising innovative and holistic planning and operational models.
Furthermore, the increasing deployment trend of AV and connected Vehicle-Infrastructure technologies provides potentials to develop analytically tractable and in-depth understanding of mobility patterns in multi-modal transport networks. This enables us to establish holistic and quantitative management schemes to efficiently monitor and control traffic congestion within real-time optimisation frameworks with more realistic and multi-modal representation of traffic dynamics.
Also of significant interest to future transport and infrastructure planning and investment is how these changes will alter the nature of road requirements and the transport network, and how this will interact with the push for Mobility-as-a-Service (MaaS). MaaS is a demand-responsive resolution for a user-oriented and tailor-made transport service that provides flexible and personalized mobility services to passengers. MaaS platform leverages dynamic car- and ride-sharing opportunities within an electrified (and possibly automated) fleet along with a dynamic faring policy to optimize network-wide travel costs. Specifically, the width of traffic lanes that is mainly driven by safety considerations may be narrowed and the design of intersections may be changed, both increasing the road capacity. In addition, AV may have almost perfect information and the ability to communicate with other AV to optimally spread themselves across the network, thereby increasing the road capacity further. Finally, with the rollout of autonomous vehicles, MaaS providers will be able to bring all modes of transport into a single mobility package and allow all people to subscribe (including those who do not hold a valid driving licence). This coupled with no parking worries will open a much bigger market for MaaS which will significantly change the ways people travel. Incorporating these changes into planning and optimization processes and tools will require a revision of different assumptions, and the development of new models to forecast travel demand and manage traffic across the network.