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- Publisher Website: 10.1016/j.cities.2024.104918
- Scopus: eid_2-s2.0-85186978924
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Article: Building a Government-owned Open Data Platform for Connected and Autonomous Vehicles
| Title | Building a Government-owned Open Data Platform for Connected and Autonomous Vehicles |
|---|---|
| Authors | |
| Keywords | Connected and autonomous vehicles Data sharing Open data platform Social benefits Stakeholder analysis |
| Issue Date | 1-Jun-2024 |
| Publisher | Elsevier |
| Citation | Cities, 2024, v. 149 How to Cite? |
| Abstract | The growing recognition of the societal implications stemming from technological advancements highlights the need for innovative governance approaches, particularly in urban environments. Focused on connected and autonomous vehicles (CAVs), this study proposes an integrated analytical framework for a dedicated open data platform (ODP). By integrating thematic analysis with process landscape and stakeholder analysis methods, we present a holistic CAV-ODP model positioned to optimize benefits across industries, public sectors, and society as a whole. We argue that multiple stakeholders can enhance both supply and demand aspects of the CAV-ODP, catalyzing activities such as data access, management, transformation, incubation, coordination, education, and application. Importantly, it advocates for government-led integration of interests spanning private and public domains, fostering a collaborative social network for data application and innovation. This inquiry not only reveals the social benefits generated by open CAV data but also underlines the significance of the CAV-ODP platform as an innovative governance arrangement, serving as a catalyst for a culture of sharing, bolstering collaborative governance, and preserving public value amid disruptive technological changes. Overall, the concept of government leadership is universally applicable, with the tangible power dynamics among the government, private sectors, and society influencing the expeditious materialization of the proposed framework. |
| Persistent Identifier | http://hdl.handle.net/10722/357331 |
| ISSN | 2023 Impact Factor: 6.0 2023 SCImago Journal Rankings: 1.733 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Deng, Handuo | - |
| dc.contributor.author | Hu, Qi | - |
| dc.contributor.author | Guan, Chenghe | - |
| dc.contributor.author | Chen, Yi Samuel | - |
| dc.contributor.author | Menendez, Monica | - |
| dc.date.accessioned | 2025-06-23T08:54:45Z | - |
| dc.date.available | 2025-06-23T08:54:45Z | - |
| dc.date.issued | 2024-06-01 | - |
| dc.identifier.citation | Cities, 2024, v. 149 | - |
| dc.identifier.issn | 0264-2751 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/357331 | - |
| dc.description.abstract | <p>The growing recognition of the societal implications stemming from technological advancements highlights the need for innovative governance approaches, particularly in <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/sustainable-urban-development" title="Learn more about urban environments from ScienceDirect's AI-generated Topic Pages">urban environments</a>. Focused on connected and autonomous vehicles (CAVs), this study proposes an integrated analytical framework for a dedicated <a href="https://www.sciencedirect.com/topics/social-sciences/open-data" title="Learn more about open data from ScienceDirect's AI-generated Topic Pages">open data</a> platform (ODP). By integrating thematic analysis with process landscape and <a href="https://www.sciencedirect.com/topics/social-sciences/stakeholder-analysis" title="Learn more about stakeholder analysis from ScienceDirect's AI-generated Topic Pages">stakeholder analysis</a> methods, we present a holistic CAV-ODP model positioned to optimize benefits across <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/industry" title="Learn more about industries from ScienceDirect's AI-generated Topic Pages">industries</a>, public sectors, and society as a whole. We argue that multiple stakeholders can enhance both supply and demand aspects of the CAV-ODP, catalyzing activities such as data access, management, transformation, incubation, coordination, education, and application. Importantly, it advocates for government-led integration of interests spanning private and public domains, fostering a collaborative social network for data application and innovation. This inquiry not only reveals the <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/social-benefit" title="Learn more about social benefits from ScienceDirect's AI-generated Topic Pages">social benefits</a> generated by open CAV data but also underlines the significance of the CAV-ODP platform as an innovative governance arrangement, serving as a catalyst for a culture of sharing, bolstering <a href="https://www.sciencedirect.com/topics/social-sciences/collaborative-governance" title="Learn more about collaborative governance from ScienceDirect's AI-generated Topic Pages">collaborative governance</a>, and preserving public value amid disruptive technological changes. Overall, the concept of government leadership is universally applicable, with the tangible power dynamics among the government, <a href="https://www.sciencedirect.com/topics/social-sciences/private-sector" title="Learn more about private sectors from ScienceDirect's AI-generated Topic Pages">private sectors</a>, and society influencing the expeditious materialization of the proposed framework.</p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Cities | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | Connected and autonomous vehicles | - |
| dc.subject | Data sharing | - |
| dc.subject | Open data platform | - |
| dc.subject | Social benefits | - |
| dc.subject | Stakeholder analysis | - |
| dc.title | Building a Government-owned Open Data Platform for Connected and Autonomous Vehicles | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.cities.2024.104918 | - |
| dc.identifier.scopus | eid_2-s2.0-85186978924 | - |
| dc.identifier.volume | 149 | - |
| dc.identifier.eissn | 1873-6084 | - |
| dc.identifier.isi | WOS:001201970300001 | - |
| dc.identifier.issnl | 0264-2751 | - |
