Artificial Intelligence for Smart Cities

There is a growing importance of ICT in profiling the competitiveness of cities. The next step for the smart city is the automated city – one that is predictive and responsive without human intervention. Such a city could avoid traffic congestion before it occurs and distribute resources, such as emergency services and maintenance, without time-consuming human decision-making. Urban mobility applications will also rely on collecting available information from sensor networks in and around the city and make the operation of public services (like parking, lighting, heating, garbage collection, etc.) intelligent. The smart city can make intelligent responses to different kinds of needs, including daily livelihood, environmental protection, public safety and city services, industrial and commercial activities. Among the different notable goals of smart cities, construction of intelligent transportation systems could have a significant impact to residents of future cities. Advanced Traffic Management Systems (ATMSs) and Intelligent Transportation Systems (ITSs) integrate information, communication and other technologies and apply them in the field of transportation to build an integrated system of people, roads and vehicles. These systems constitute a large, full-functioning, real-time, accurate and efficient transportation management framework.

Our research group focuses on investigation of data analytics and machine learning methods, appropriate for optimizing the transport and other public services in smart cities, using the gathered sensor network and crowdsensing information. We develop novel data gathering and filtering methods , together with data analysis and machine learning algorithms, which would help to predict future vehicle and crowd dynamics.

Research topics/competences

• Machine learning and data analytics for intelligent and automated cities
• Behavior prediction in smart cities based on historical data
• Crowdsensing for mass event surveillance and urban transport optimization
• Autonomous task allocation in flocking systems
• Patrolling strategies for autonomous vehicles

Latest results

• Developing an AI based Intelligent Transportation System for the VKE project, 2019-
• Developed machine learning solutions for warehouse optimization for the Autonomous warehouse and last mile delivery (AWARD) project, EIT Digital, 2018
• Developed digital profiling for Nokia Bell Labs, 2017-2018
• Developed a machine learning based solution for the intelligent parking system for T-Systems, 2017
• Developed mobile crowdsensing methodology for smart cities, Ericsson High Speed Networks (HSN) CFT project, 2014-2015
• Developed controlling algorithms for vehicle flocking in the FIRST project (Future Internet Research, Services and Technology)

Publications

• Attila M. Nagy, Vilmos Simon: “A novel congestion propagation modeling algorithm for smart cities“,
  Pervasive and Mobile Computing, Volume 73, 2021
• Attila M. Nagy and Vilmos Simon: “Traffic congestion propagation identification method in smart cities“, Infocommunications Journal, Vol. XIII, No 1, March 2021, pp. 45-57.
• Mohammad Bawaneh, Vilmos Simon: “Anomaly Detection in Smart City Traffic Based on Time Series Analysis“, International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6, September 19-21, 2019
• Attila Nagy, Vilmos Simon: „Survey on traffic prediction in smart cities”, Pervasive and Mobile Computing, Vol 50, pp. 148-163, October 2018
• Attila Nagy, Vilmos Simon: „Identifying Hidden Influences of Traffic Incidents’ effect in Smart Cities”, Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, Poznan, Poland, Vol. 15, pp 651–658, September 9-12, 2018
• Bernát Wiandt, Vilmos Simon: „Autonomous Graph Partitioning for Multi-Agent Patrolling Problems”, Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, Poznan, Poland, Vol. 15, pp 261–268, September 9-12, 2018
• Bernat Wiandt, Vilmos Simon, Andras Kokuti: „Self-organized graph partitioning approach for multi-agent patrolling in generic graphs”, IEEE EUROCON 2017 -17th International Conference on Smart Technologies, Ohrid, Macedonia, 6-8 July 2017
• Attila Nagy, Vilmos Simon: „Integrated mass surveillance system for large scale events”, IEEE International Smart Cities Conference (ISC2), Trento, Italy, pp 214-219, September 12-15, 2016
• Attila Nagy, Vilmos Simon: “A crowdsensing platform for mass surveillance”, 58th International Symposium ELMAR-2016, Zadar, Croatia, pp 189-194, September 12-14, 2016
• Andras Kokuti, Vilmos Simon, Bernat Wiandt: „Token-based Autonomous Task Allocation in Flocking Systems”, Federated Conference on Computer Science and Information Systems, Gdansk, Poland, pp 1501-1506, September 11-14, 2016
• Armin Petkovics, Vilmos Simon, Istvan Godor, Bence Borocz: “Crowdsensing Solutions in Smart Cities towards a Networked Society”, EAI Endorsed Transactions on Internet of Things 15(1): e6, October 2015, October 2015
• Armin Petkovics, Vilmos Simon, Istvan Godor, Bence Borocz: “Crowdsensing Solutions in Smart Cities: Introducing a Horizontal Architecture”, 13th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2015), Brussels, Belgium, pp 33-37, 11-13 December, 2015