Smart parking for sustainable urban mobility

Roaming for parking contributes to hampering business and environment sustainability in urban cores today, while future autonomous vehicles are expected to free drivers from daunting activities, including parking.

In both current and future cases, the search for parking should not exacerbate traffic in city centers. This project brings together cities, car manufacturers and parking-service providers to take a step ahead towards addressing current urban-mobility issues and get ready to anticipate future ones. The proposed approach utilizes visibility over urban traffic and parking dynamics through cutting-edge Internet-connected sensors and other device technologies that expose new sources of data.

This Internet of Things (IoT) evolution provides historical grounds for optimizing the outcomes of parking-related decision-marking processes, in order to improve sustainability indexes. Our proposed IoT-based approach to cloud-based parking and navigation is supported by a judicious algorithm which employs contemporary data-analytics techniques. The approach is demonstrated through traffic simulators to scale its predictive accuracy to the higher-end, as further parking resources are made available, while reducing transit duration.

Vital services such as healthcare, water supply, food supply, and related information-communication processes, depend on reliable electrical-power supply. The power grid involves many operators such as energy producers, retailers, and various municipalities. The infrastructure components are integrated with each other leading to interwoven services which functionality spread across other critical infrastructures, e.g., healthcare systems. Thus, interdependency between components is high and complex, and hence a failure in one component can lead to a cascading effect within the infrastructure or, in the worst case, can cause failures in other infrastructures that may disrupt some vital services.

Our work covers two national priorities related to critical infrastructures:

  1. Identification of dependencies and vulnerabilities, and
  2. Consequence analysis of failures at some level within the infrastructure dependency network.

Our approach is supported by analytical modelling techniques and applied simulations. We employ analytical, monitoring and evaluation processes to investigate target indicators of failure and consequence measures, that contribute to cascading effects. Some important questions answered in this project are:

  • How do power-grid infrastructure components relate to other components of critical infrastructures?
  • What are possible preconditions for a power grid component failure?
  • What are possible consequences of a failure?
  • What is the actual status of the power-grid network with respect to resilience against critical failures? and,
  • How can calls and messages from citizens and stakeholders about failing services or relevant observations be integrated into the assessment of a critical state and into the reasoning process about a failure and its related cascading-effects assessment?

We develop models depicting a more complex and holistic picture of risks, in terms of dependencies and consequences in critical infrastructures. In doing so, we apply information fusion algorithms to multi-source data from critical infrastructures, such as sensors, logs, citizens and power-grid operators. The aim is to improve situational awareness and resilience index, as well as to perform post-failure forensic analysis.  Simulations assert the validity of the proposed models as part of an initial framework that is expected to be extended to real-time simulation scenarios.

 

Yacine Atif
School of Informatics
Professor of Informatics
Email: yacine.atif@his.se
Work: 0500-448312
Room: PA420K

 

Smart parking for sustainable urban mobility

Duration: 11/2017 – 11/2019
Financed by: Vinnova-FFI
Collaborating partners/Partnership: VTI, Stockholm Parkering, Stockholms Stad, Kista Science City

Researchers related to the current project 
Ding Jianguo, Senior Lecturer
Sten F. Andler, Professor