A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Planning policies for the driverless city using backcasting and the participatory Q-Methodology
Abstract Autonomous vehicles (AVs) can potentially bring about major changes in cities. Anticipatory planning approaches may provide valuable opportunities for fostering desirable transitions and pre-empting undesirable impacts. This research employs a combination of two methods to define the key policies to support a transition to the desirable driverless urban futures: the backcasting approach and the participatory Q-method. The combination of these techniques aims to identify different viewpoints about policies with the purpose of determining more effective and more acceptable options. The article analyses viewpoints from 20 citizens and 10 experts. The results point to the existence of two main viewpoints about the most and least desirable policies. The first viewpoint centres around increasing pedestrian mobility and promoting a more compact city. The second viewpoint centres around expanding transit-oriented development (TOD) and new networks of green spaces. Meanwhile, support for regulation-oriented policies to discourage the use of private motorised vehicles was relatively low. This research not only sheds light on the different viewpoints on the policies to achieve more desirable urban visions, it also illustrates the tensions and disagreements that may arise in the process of policy-making.
Highlights Autonomous vehicles (AVs) can bring about major changes in cities. A combination of backcasting and Q-method is employed to define key policy measures. Two main viewpoints about most and least desirable policy measures were detected. Pedestrian mobility, compact cities, TOD and green spaces focus the main viewpoints. Support for regulation-oriented policies on privately owned vehicles was fairly low.
Planning policies for the driverless city using backcasting and the participatory Q-Methodology
Abstract Autonomous vehicles (AVs) can potentially bring about major changes in cities. Anticipatory planning approaches may provide valuable opportunities for fostering desirable transitions and pre-empting undesirable impacts. This research employs a combination of two methods to define the key policies to support a transition to the desirable driverless urban futures: the backcasting approach and the participatory Q-method. The combination of these techniques aims to identify different viewpoints about policies with the purpose of determining more effective and more acceptable options. The article analyses viewpoints from 20 citizens and 10 experts. The results point to the existence of two main viewpoints about the most and least desirable policies. The first viewpoint centres around increasing pedestrian mobility and promoting a more compact city. The second viewpoint centres around expanding transit-oriented development (TOD) and new networks of green spaces. Meanwhile, support for regulation-oriented policies to discourage the use of private motorised vehicles was relatively low. This research not only sheds light on the different viewpoints on the policies to achieve more desirable urban visions, it also illustrates the tensions and disagreements that may arise in the process of policy-making.
Highlights Autonomous vehicles (AVs) can bring about major changes in cities. A combination of backcasting and Q-method is employed to define key policy measures. Two main viewpoints about most and least desirable policy measures were detected. Pedestrian mobility, compact cities, TOD and green spaces focus the main viewpoints. Support for regulation-oriented policies on privately owned vehicles was fairly low.
Planning policies for the driverless city using backcasting and the participatory Q-Methodology
Nogués, Soledad (author) / González-González, Esther (author) / Stead, Dominic (author) / Cordera, Rubén (author)
Cities ; 142
2023-08-18
Article (Journal)
Electronic Resource
English
Envisioning the driverless city using backcasting and Q-methodology
| Elsevier | 2022
| Taylor & Francis Verlag | 2019