PROJECT HISTORY
Sustainable Urban Mobility Boost Smart Toolbox
SUMBooST
Sustainable Urban Mobility Boost Smart Toolbox (SUMBooST) is supported by the EIT (European Institute of Innovation and Technology) Urban Mobility, an initiative of the EIT, a body of the European Union. Project was developed within the “RIS (Regional Innovation Scheme) 2020 Program”.
Project has resulted in a proven and validated methodology for fast and efficient transport data collection, fusion, and analytics needed for the transport planning process.
The results show that the proposed methodology and related activities open a new dimension of big data usage in transportation engineering, enabling quick efficient safe mobility patterns analytics.
SUMBooST aimed to
Use big data analytics and field research to identify pair of urban zones with a high percentage of passenger car commuters.
Identify reasons for the high number of passenger car commuters.
Define and propose measures for the modal shift from passenger car to sustainable transport mode.
Anonymized big data sets
SUMBooST in numbers
The big data research and analysis was performed on anonymized big data sets originating from mobile telecommunication network operator with significant market share of 35% and it resulted with a mobility parameters data base.
The extraction of mobility data from the big data set is the most innovative part of the toolbox and element which makes the toolbox unique.
The extraction of mobility data from the big data set is the most innovative part of the toolbox and element which makes the toolbox unique.
"As is" mobility situation
Big data reasearch
Big data research included the process of collecting organizing analyzing large sets of anonymized data gathered from mobile telecom operators to obtain information on citizens daily migration patterns, which are important for urban mobility planning.
Data validation
The results of the big data analysis were validated by the results of traditional field research.
Field research
Field research was performed through an online and phone survey on commuter patterns and modal split, through analysis of traffic flow distribution based on automatic license plate recognition system (ALPR), and through analysis of traffic flow volume and structure (traffic counting).
Validation results
The comparison of results of traditional and novel big data method was performed on the next parameters: modal split, trip distribution (number of trips between typical zones) and traffic volume. Comparative analysis of results of modal split of travel showed the existence of a strong connection between the results obtained by classical field research and the results obtained by the analysis of a big data sets. The average deviation in trip distribution from the analysed zones towards the city center was determined in the amount of about 7.5% (based on household survey).
The validation was successful, and it confirmed the correlation between the two sets of results, which resulted in the basis for the defining transport issues, challenges and solutions for the defined challenges.
Pilot study
The methodology was successfully completed in a pilot study, and it resulted in a set of possible solutions for modal shift from passenger cars to sustainable mobility modes. The solutions were proposed for each pair of zones with the highest share of passenger car commuting. The local public and stakeholders confirmed the quality of the proposed transport solutions (proposed measures).
