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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

Impacts of Automated Driving and Shared Mobility – Explorative Model and Case Study of the Province of North-Holland

What is the impact of self-driving cars, and how will this affect the society and policy making? These questions are surrounded by many uncertainties. In case self-driving vehicles and their effects are perceived as positive and technology keeps developing at a rapid pace (and hence becomes more affordable), a “self-driving future” will be possible. A future in which the traffic and transport system is radically different to todays ‘, incorporating self-driving vehicles as alternatives and/or supplements for current transportation modes. Governmental interventions can also accelerate a transition to a selfdriving future, while there are also contra-productive developments that might impede this transition. Considering the aforementioned the Province of Noord-Holland and the Amsterdam Transport Region expressed interest to gain insight into the impact of self-driving vehicles on their society, and on their responsibilities. These impacts have been examined in this study based on the central question: What consequences does the advanced automation of driving tasks have on social-, economic-, spatial- and mobility development for the Province of Noord-Holland (PNH) and the Amsterdam Transport Region, and how will this impact their role as infrastructure- and public transport managers In the period November 2017 - March 2018, the combination Arcadis/ TNO carried out an impact analysis on the introduction of self-driving vehicles. This study also provides insight into interventions for the Province and the Amsterdam Transport Region from their role as an infrastructure manager and a public transport concession provider. The impact determination is performed through a scenario study, developing four scenarios which are projected on five distinct area types in the Province. These scenarios form the input for a model calculation, using the Quick Scan Tool as developed by TNO. The outcome of the model shows that the introduction of self-driving vehicles in the Province of Noord-Holland leads to an increase in vehicle kilometers for all area types and all scenario’s. This results in an increase of the road network pressure (vehicle-loss-hours) for primarily urban areas for all scenarios, while the pressure on rural areas remains similar to the reference situation 2040 (except for the most extreme scenario where it increases). The increase in vehicle kilometers is explained by a switch in mode choice among users, preferring self-driving concepts over conventional transport modes, such as public transport (bus, tram, metro and train), cycling and walking. Self-driving vehicles form attractive transport alternatives which enable a larger public to have access to mobility because of lower user-costs and ease-of-use. The aforementioned results in an improvement of social development, allowing a broader public to participate in the community. Also, a slight improvement is expected for traffic safety because of overall improvements in vehicle technology and performance, while however conflicts between fast and slow transportation modes (especially in highly dense areas) remain an attention point. A spatial change is expected because of a (minor) population exchange between urban and rural areas, and a change of the functional use of parking facilities (long term parking vs Kiss+Ride) in and around urban areas (nodes and mass attraction locations). From an economic perspective a change could occur in job markets, where some professions could become obsolete, while new markets and demands will emerge. Also, costs of asset management operations could rise because of an increase in vehicle kilometers on the road network, requiring more (frequent) maintenance. The increase in vehicle kilometers could result in an increased pressure on sustainability and livability (gas emission and sound exposure), which however could be compensated through parallel developments such as the electrification of vehicles. Subsequently interventions are identified to facilitate opportunities and/or to mitigate risks on policy goals as a result of the introduction of self-driving vehicles. A selection of these interventions is used as input for the Quick Scan Tool to showcase the impacts of the interventions. This shows that farreaching (combinations of) repressive measures are necessary to guarantee the accessibility of the traffic system and the quality of life in urban areas. This requires a multimodal and integrated network vision on the public transport system of the future. The multimodal and integrated network vision can also elaborate on opportunities for accessibility and livability improvement of less dense urban, rural, and recreational areas. This can be achieved through the deployment of new transport/mobility concepts such as Mobility as a Service for example. In the near future opportunities with these new mobility concepts can already be exploited through the deployment of pilots/experiments. This offers the chance for accessibility improvement to these areas in the short term.

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  • Supplemental Notes:
    • Abstract used by permission of Association for European Transport.
  • Corporate Authors:

    Association for European Transport (AET)

    1 Vernon Mews, Vernon Street, West Kensington
    London W14 0RL,    
  • Authors:
    • Bergveld, Hendrik Jan
    • Snelder, Maaike
    • Hoseini, Parvin
  • Conference:
  • Publication Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Pagination: 14p
  • Monograph Title: European Transport Conference 2019

Subject/Index Terms

Filing Info

  • Accession Number: 01753719
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 29 2020 11:19AM