SUMBooST2
Sustainable Urban Mobility Boost Smart Toolbox Upgrade
SUMBooST2 develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrix from the anonymised big data set gathered from telecom operator.
The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in the 2021 to detect trips made by active mobility modes and public transport.
This project if funded by EIT Urban Mobility, an initiative of the European Institute of Innovation and Technology (EIT), a body of the European Union. EIT Urban Mobility acts to accelerate positive change on mobility to make urban spaces more livable. Learn more: eiturbanmobility.eu.
Strategic objectives
Project mission
The SUMBooST2 Toolbox provides a fast and efficient way to obtain an accurate data set based on which city planners can develop new solutions.
Project vision
The usage of the SUMBooST2 Toolbox can set basis for the development of efficient transport measures and generally to develop a transport system in a green, safe, and sustainable way.
Sustainable Urban Mobility Boost Smart Toolbox Upgrade
SUMBooST2 project results
The main result of the SUMBooST2 project is the upgraded toolbox with enhanced functionality, wider applicability and better accuracy. The initial toolbox was developed in the 2020 with an algorithm for the extraction of the mobility data from the big data set. The algorithm was designed to extract origin – destination matrices for the trips made by passenger car. The toolbox was successfully applied and validated in one pilot city, City of Rijeka.
SUMBooST2 project is an upgrade of the initial SUMBooST 2020 project. The initial algorithm for the detection of trips made by passenger car is refined and upgraded to a higher level of accuracy. The most significant upgrades are the new algorithms for detecting active modes of transport (cycling and walking) and public transport. Upgraded toolbox with new algorithms is validated in three different cities with different urban area specifics, proving the toolbox’s wide applicability.
In the framework of the SUMBooST2 project, data on positions and position changes of mobile phones in three cities included in the project (Cities of Rijeka, Dubrovnik, and Zagreb) was collected. The data was gathered from the telecom operator anonymously following GDPR regulations. Algorithms for the detection of trips and transportation modes were validated with the data set collected by traditional field research. Using developed algorithms, origin-destination matrices were created for one typical day for all trips and for each mode of transport separately.
Origin – destination matrices were analysed, and pilot zone pairs were determined for each pilot city. The goal was to identify a positive and negative example of a zone pair based on the mobility parameters between the two zones. Positive zone pairs are those with an above average use of sustainable modes of transport for the mobility in between two zones. That type of mobility has to be improved and further encouraged. Negative zone pairs are those with an above average use of passenger cars between the zones. That kind of mobility has to be reduced and shifted to sustainable modes. For the identified zone pairs, the project team proposed solutions that could increase the use of sustainable modes of transport. Each solution was presented to local stakeholders through focus groups and to the local public through an online survey. Both local stakeholders and the local public gave their positive feedback on proposed solutions.
SUMBooST2 project resulted in upgraded methodology/toolbox for the development of sustainable urban mobility solutions. The main advantage of upgraded methodology is that it defines the ‘as is’ state of traffic in a more accurate, simple and cheaper way than traditional surveying methods. It proved that innovative methods of collecting data can replace the data obtained by conducting complex and challenging surveys and traffic counting.
About Us
Faculty of Transport and Traffic Sciences
The Faculty is the lead organization in the consortium because of its experience in planning of the transport systems for numerous cities. Within the project, Faculty will manage project development.
Also, The Faculty will define and analyze scope area within the advanced data analytics of Mobile Networks big data sets and support Ericsson Nikola Tesla in the analysis of the gathered data. Main expertise of the Faculty will be used for the development of the solutions for the sustainable transport system in the establishment of measures/proposals for urban mobility improvements. Faculty, as well, will have important role in the use of its data science capabilities for data analytics of other relevant and available data sets.
