Multi-modal trip data in urban transportation ecosystems - Acting upon carbon-emission regulations

Joint position statement - QROWD consortium

Background

The 13th sustainable development of the United Nations urges to combat climate change and its impacts. Both in the USA [1] and Europe [2], transport is responsible of 30% of the CO2 emissions, with private cars representing more than 60% of them. City authorities and urban planners are actively looking for ways to optimize urban transportation, and take measures to encourage citizens to use greener alternatives (bike, walk, multi-modal schemes), with some cities planning to completely ban cars in their urban cores in the next 10-20 years [3]. The impact of this measures is not limited to the transport of persons. Delivery and transport of goods will also be affected by these regulations. This underlines the importance of including data from vehicles different to cars in transport data standards.

QROWD [4], is a multi-disciplinary project funded by the European Commission as part of its Big Data Value - Public Private Partnerships (BDV-PPP) program. Transportation is one of the main themes of BDV-PPP and QROWD is one of the projects that addresses issues in this sector. QROWD's consortium is comprised of Research and Technology Organisations (University of Southampton, InfAI Leipzig, University of Trento), large and small service providers (TomTom, Atos, AI4BD), and public sector (Municipality of Trento) and has two main objectives (i) cross-sectorial data integration along the urban transportation data value chain, and (ii) how to integrate citizens in the loop of urban mobility services.

QROWD's innovation is centred around two use cases that involve a public and a private data providers. The goal of the first use case was to facilitate the involvement of different stakeholder groups on collaborative data exchange, by integrating (via RDF-ization) public open data sources on infrastructure, strategic routes and Points of Interest, with private floating car data in order to provide road information services. The second use case concerns providing agile and accurate estimation of modal split, i.e., the distribution of usage of transportation modes for moving in a given area. From an administration perspective, modal split is an important indicator for driving the design and evaluation of transport policies, e.g., which pair of neighbournhoods have the largest car use, and, if any incentive for greener transportation modes had an impact after its implementation. From a solution provider point of view, this data enables the identification of business opportunities based on the current needs of the city. In QROWD, we focused on engaging citizens to contribute their trip data through a mobile application, and confirming however, having transport operators provide trip data in a standardized format would facilitate the computation of modal split with a larger sample.

Discussion topics

Given our background, we would like to lead the discussion on models for multi-modal trip information. We also believe that a further topic to be discussed should be what alignments should be provided with established related vocabularies, like DATEX II (traffic control and management) [5] and FIWARE data models (Smart Cities) [6]. We aim at driving the discussion to answer the following " "what"s:

Representative

Dr. Luis-Daniel Ibáñez, technical coordinator of QROWD, would represent the project in the workshop. He is a Research Fellow at the University of Southampton with more than 5 years of experience in Linked Data and Semantic Web Technologies.

Links
  1. https://www.epa.gov/greenvehicles/fast-facts-transportation-greenhouse-gas-emissions
  2. http://www.europarl.europa.eu/news/en/headlines/society/20190313STO31218/co2-emissions-from-cars-facts-and-figures-infographics
  3. https://www.businessinsider.com/cities-going-car-free-ban-2018-12
  4. http://qrowd-project.eu
  5. https://datex2.eu/
  6. https://fiware-datamodels.readthedocs.io/en/latest/Transportation/doc/introduction/index.html