SIADE SaaS: Spatial Decision Support System for Transportation Planning

Projektinformationen

SIADE SaaS

ID Finanzhilfevereinbarung: 778764

DOI

10.3030/778764

Projekt abgeschlossen

EK-Unterschriftsdatum 8 August 2017

Startdatum 1 September 2017

Enddatum 29 Februar 2020

Finanziert unter

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Gesamtkosten

€ 1 268 901,50

EU-Beitrag

€ 888 231,05

Koordiniert durch

TERRAIN TECHNOLOGIES SL
Spain

Periodic Reporting for period 2 - SIADE SaaS (SIADE SaaS: Spatial Decision Support System for Transportation Planning)

Berichtszeitraum: 2018-09-01 bis 2020-02-29

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The global demand for public transportation is huge. Large metropolises like London or Madrid are managing fleets of thousands of buses, providing an efficient way of transporting people. We can't forget that public transport is the lifeblood of a city's economy; particularly in Europe, public transport by motor coaches, buses and trolley buses is one of the main drivers of 21st century mobility and economy, with Hungary (22,3%) and Turkey (34.9%) being the countries with the highest use share.

The demand for passenger transport is estimated to grow up to 51% by 2050 and infrastructure will reach its performance limits. With a growing market of €100.6 billion by 2019, Intelligent Transport Systems (ITS) could raise the capacity of existing infrastructure by 20-30%. Particularly, for the Transport Sector and Public Authorities, the potential of data from citizens' transport cards for bus transport optimization and planning is immense. However, a great challenge remains unresolved today: how to accurately describe the actual state of transport networks in terms of flow while transforming smart card data into intelligence and knowledge.

SIADE SaaS objectives go beyond this challenge. SIADE SaaS effectively solves the problem of public transportation data mining, knowledge extraction and representation. It allows the input from any e-ticketing system available today on the market and delivers accurate user mobility patterns, but it also aims at becoming a fundamental tool that will improve any public transport network performance, contributing to a more efficient European transport sector, jobs creation, environment awareness and smart mobility, providing an efficient way to deliver the best quality service by analysing mobility patterns, route performance and occupancy as well as optimizing bus stops locations and routes. SIADE can integrate socio-economic and environmental data jointly with transportation and traffic data, and analyses the spatial relationships between different features. Thus, transport infrastructures can be economically optimized and adapted to real social mobility needs in the most effective and innovative way. Thanks to the EU Horizon 2020 Programme guidance and support, it's on the right track to achieve this aim.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

We have successfully concluded the development and deployment of the Data Mining module. Using raw data obtained from AFC (Automatic Fare Collection) systems , we have generated a highly accurate and discrete Origin-Destination (OD) Matrix, segmented by other available dimensions (e.g. route, day, time, fare group, occupancy, etc.). This information has been combined with geospatial data including Automatic Vehicle Location (AVL) data in order to provide information and knowledge about the network and people’s mobility patterns. With the support of EMTUSA, the public transport operator in Gijón (Spain) -who has proven to be an excellent collaborator- we have been able to deliver a product that allows the company to understand the citizens’ mobility patterns, assisting EMTUSA’s decisions affecting changes in headways, routes, bus stops, etc. while improving the service provided to citizens and therefore increasing patronage.

Besides the mobility patterns analysis of different kind of users, we have included in this version bus bunching analysis and several KPIs (Key Performance Indicators) as proposed by EMTUSA.

Moreover, this good and agile relationship with EMTUSA allowed succeeding our first pilot in the best conditions making possible to point out the SIADE strengths like:
• Interactive and responsive GIS representations of KPIs.
• Allows real time queries on any represented elements.
• Quick and complete adaptation to client needs.

We have started the development of SIADE SaaS' flagship component, the simulator and optimizer. It is a predictive analytics tool that will include the possibility of defining “what-if” scenarios. Requires the deployment of a new algorithm complementary with SIADE that iterates the possible alternatives of the network and choose the most logical regarding several parameters whose weight can be predefined by the user. Data has been provided by OTL (Public Transport Company from Oradea, Romania) and SETA SpA (Società Emiliana Trasporti Autofiloviari), the sole operator of the local automotive public transport service in the provincial territories of Modena, Reggio Emilia and Piacenza and aMo, (Agenzia per la mobilità e il trasporto pubblico locale di Modena S.p.A) a company born within the process of reforming the local public transport system with the aim of opening the sector to competition while ensuring a unified mobility management in Modena. We have been supported by our excellent collaborators in both regions, Mobilissimus (Hungary) and TBridge (Italy). Thanks to the collaboration with our partners, we will be able test the validity of SIADE's simulator.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

The use of Artificial Intelligence, combined with the power of GIS and the generation and implementation of unique OD matrices developed at a microscopic level, will bring SIADE SaaS a huge competitive advantage. This fact has already attracted the attention of several companies and Public Transport Operators/Mobility Agencies such as the EMT – Madrid, the Ministry of Transport (Israel), the Ministry of Transport (Paraguay), T-Systems, IDOM or ESRI.

EMT has requested a pilot using its data, whose results will be disseminated in next ESRI User Conference in Madrid (October 2018). The fact that EMT is the third biggest Public Transport Operator in Europe will open SIADE SaaS the door of the most important players in the Public Transport sector in the world.

The socio-economic impact and the societal implications of the project are clear: efficient transport planning is a key driver to improve public transport, therefore its improvement has the ability to reduce the use of private car, reducing road congestion, pollution and providing mobility to people without access to a car (including low income groups, young, elderly and mobility-impaired people)

SIADE Data mining module (EMTUSA data)

SIADE SaaS logo