Parking Allocation for Smart Cities

A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 15 September 2026 | Viewed by 1096

Special Issue Editor

College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: transportation planning; parking system optimization; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of technology-driven parking allocation is an important component of building smarter, more sustainable cities. As urbanization intensifies, conventional parking systems, reliant on static planning and manual oversight, are increasingly mismatched with the demands of modern mobility. In contrast, parking allocation driven by emerging technologies is becoming a critical enabler of smart city objectives. By harnessing innovations such as IoT sensors, machine learning, and cloud-based platforms, cities can transform parking from a fragmented resource into a dynamic, interconnected component of urban infrastructure. This shift not only addresses immediate challenges like traffic congestion and emissions but also supports long-term goals of resource efficiency, equity, and climate resilience—cornerstones of the smart city vision. The core of smart parking allocation is to optimize space utilization, reduce vehicle travel time, and seamlessly integrate with broader transportation networks. For instance, IoT-enabled sensors provide granular occupancy insights, while AI algorithms predict demand fluctuations across temporal and spatial dimensions. These technologies collectively empower cities to dynamically adjust parking availability, balance commercial, residential, and public needs and minimize environmental impact. Such advancements are not merely incremental improvements, they represent a paradigm shift in urban governance, where technological agility drive equitable, citizen-centric solutions.

This Special Issue invites papers related to parking allocation for smart cities. Research areas may include (but not limited to) the following:

  • Cutting edge methodology for parking allocation.
  • Dynamic parking demand prediction and space optimization.
  • Integration of parking allocation and urban mobility systems.
  • Sustainable and equitable parking allocation solutions.
  • Environmental impact mitigation through optimized parking allocation.
  • Adaptive reuse of parking spaces in autonomous vehicle eras.
  • Parking allocation strategies for disaster preparedness
  • Case studies and best practices related to parking allocation.

I look forward to receiving your contributions.

Dr. Zhenyu Mei
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • parking allocation
  • parking planning
  • parking system optimization

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Published Papers (1 paper)

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Research

38 pages, 2267 KB  
Article
Sustainable Parking Allocation for Smart Cities Using Digital Twin and Agentic Optimization
by Hamed Nozari and Zornitsa Yordanova
Future Transp. 2026, 6(3), 95; https://doi.org/10.3390/futuretransp6030095 - 26 Apr 2026
Viewed by 513
Abstract
The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, [...] Read more.
The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, and presents an integrated approach based on digital twin and multi-objective optimization. In this framework, a digital model of the urban parking system is created that is able to analyze real and simulated data related to parking demand, space occupancy status, and traffic flow and support optimal allocation decisions. The results of the analysis show that using the proposed framework can reduce parking search time by an average of 28%, make the distribution of parking use more balanced, and consequently reduce the amount of pollutant emissions from vehicle movement by about 17%. Also, sensitivity and scalability analyses show that the proposed model also has stable and reliable performance in large urban networks. These results indicate that the proposed framework can be used as an effective tool for developing sustainable parking management systems in smart cities. Full article
(This article belongs to the Special Issue Parking Allocation for Smart Cities)
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