Play&Go Corporate: An End-to-End Solution for Facilitating Urban Cyclability
Not on the map. Why?
Abstract (arXiv)
Mobility plays a fundamental role in modern cities. How citizens experience the urban environment, access city core services, and participate in city life, strongly depends on its mobility organization and efficiency. The challenges that municipalities face are very ambitious: on the one hand, administrators must guarantee their citizens the right to mobility and to easily access local services; on the other hand, they need to minimize the economic, social, and environmental costs of the mobility system. Municipalities are increasingly facing problems of traffic congestion, road safety, energy dependency and air pollution, and therefore encouraging a shift towards sustainable mobility habits based on active mobility is of central importance. Active modes, such as cycling, should be particularly encouraged, especially for local recurrent journeys (e.g., home--to--school, home--to--work). In this context, addressing and mitigating commuter-generated traffic requires engaging public and private stakeholders through innovative and collaborative approaches that focus not only on supply (e.g., roads and vehicles) but also on transportation demand management. In this paper, we present an end-to-end solution, called Play&Go Corporate, for enabling urban cyclability and its concrete exploitation in the realization of a home-to-work sustainable mobility campaign (i.e., Bike2Work) targeting employees of public and private companies. To evaluate the effectiveness of the proposed solution we developed two analyses: the first to carefully analyze the user experience and any behaviour change related to the Bike2Work mobility campaign, and the second to demonstrate how exploiting the collected data we can potentially inform and guide the involved municipality (i.e., Ferrara, a city in Northern Italy) in improving urban cyclability.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1.
1 paper.
- 2024Large Language Models are Zero-Shot Next Location Predictorscited by 22
- 2024Large Language Models are Zero-Shot Next Location Predictors31 May 2024