Mobilität INTERNATIONAL routes timetables and fares, among dif- ferent computer systems, which can be collected from various stakeholders. • • Cost reduction: reduces administrative burdens related to data sharing. • • Data analysis: timetable creation, visuali- sation, analysis tools for planning, real- time information, and interactive voice response (IVR) systems. Depending on the collected data and the PT services that need to be supported by the PTM system, different outputs are obtained and they can be used as the main KPIs. Here we focus on selected outputs since different PTM systems have different purposes to achieve: • • location of origin and destination infor- mation for riders, • • waiting times at the location, • • information on average boarding per PT line, • • information on boarding time at each stop, including specific times throughout the day, • • data on load-factor and estimation of vehicle crowding, • • identification of first/last-mile barriers to ridership, • • information on fleet availability, • • information on transportation means availability (vehicle, bikes, scooter, etc.), • • breakdown of common line transfers. As an equivalent to the levels of MaaS integration topology, an evolution of the lev- ACKNOWLEDGEMENT This article was the result of the joint efforts and contributions of all the nuMIDAS project partners involved: Sven Maerivoet, Bart Ons and Kristof Carlier (TML), Steven Boerma, Rick Overvoorde, Anton Wijbenga, Tessel van Ballegooijen, Dennis Hofman, Levi Broeksma, Martijn Harmenzon, Luc van der Lecq, and Jaap Vreeswijk (MAPtm), Evange- los Mitsakis, Dimitris Tzanis, Chrysostomos Mylonas, and Maria Stavara (The Centre for Research & Technology – Hellenic Institute of Transport, CERTH-HIT), Carola Vega, and Eglantina Dani (Factual Consulting), Ondřej Přibyl, Magdalena Hykšová, Jana Kuklová, Pavla Pecherková, and Jan Přikryl (Czech Technical University in Prague, CTU), Valerio Mazzeschi, Alessandro Lue, Gabriella Atzeni, Fabio Vellata, Valerio Paruscio, and Roberta Falsina (Poliedra Polytechnic Milano), Pablo Recolons, Ramon Pruneda, and Xavier Conill Espinàs (Àrea Metropolitana de Barcelona, AMB Informació), Valentino Sevino, Alessan- dro Giovannini, Cristina Covelli, Paolo Cam- pus, and Adriano Loporcaro (Agenzia Mobil- ità Ambiente Territorio, AMAT), and Eli Nomes, Tim Asperges, and Fatma Gözet (City of Leuven). Figure 3: Levels of MaaS integration and data-led mobility management [11] els of Mobility Management is briefly shown in Figure 3. This presentation focuses on penetration and contribution of mobility data in each level that corresponds to Maas Integration levels. In total, there are 5 levels, yet since the Level 0 represents no integra- tion or no information, it is not discussed here. In the past years, many cities have achieved Level 1 of Mobility Management, securing access to mobility data from pri- vate fleet operators. With this data, public agencies can make more informed decisions about where to place new infrastructure (e.g., kerb loading, scooter parking), ensure that services are equitable (i.e., that they are accessible in historically underserved com- munities), and determine how new mobility services can be leveraged to reduce conges- tion and climate impacts. Level 2 of mobility management is achieved when cities are able to leverage the data that they receive from mobility operators to set more effec- tive policies. Level 3 mobility management is achieved when cities effectively leverage pricing strategies, including subsidies, to influence how travellers decide whether to walk, drive, use micromobility, or use public transport. With Level 4 mobility manage- ment, public agencies will be able to influ- ence how travellers make transportation decisions across modes to promote societal goals: transportation climate impacts, limiting congestion, and expand- ing equitable access to mobility. reducing In order to reach Level 4 mobility man- agement, cities will need an access to data from the various transportation services delivered on their public right-of-way in order to make data driven decisions, includ- ing the implementation of pricing and sub- sidies. Level 4 mobility management can more easily be achieved together with Level 4 MaaS solutions. That is, the mechanism through which real-time information about transportation options, and specifically new pricing and subsidies, could be more easily delivered to a large population of travellers through one, or more likely, multiple, MaaS ■ consumer-facing applications. REFERENCES [1] Public Transport | SWARCO. www.swarco.com/solutions/public- transport [2] Ambrož, M.; Korinšek, J.; Blaž, J.; Prebil, I. (2016): Integral manage- ment of public transport. Transportation Research Procedia, vol. 14, pp. 382–391. [3] Pribyl, O.; et al. (2021): State-of-the-art assessment. Deliverable 2.1 of the nuMIDAS project (H2020). https://numidas.eu/index.php/ project-deliverables [4] MobilityData/gtfs-flex – githubmemory. https://githubmemory. com/repo/MobilityData/gtfs-flex [5] General Transit Feed Specification – TransitWiki. www.transitwiki. org/TransitWiki/index.php/General_Transit_Feed_Specification [6] MobilityData/gtfs-flex: A data format that models flexible public transportation services as an extension to GTFS. https://github.com/ MobilityData/gtfs-flex [7] OpenTripPlanner – TransitWiki. www.transitwiki.org/TransitWiki/ index.php/OpenTripPlanner [8] GTFS Realtime Overview | Realtime Transit | Google Developers. https://developers.google.com/transit/gtfs-realtime [9] NeTEx | Network Timetable Exchange. https://netex-cen.eu/ [10] SIRI standard – Transmodel. www.transmodel-cen.eu/siri-stand- ard/ [11] Populus. www.populus.ai/ André Maia Pereira, Ing. Ph.D. candidate and researcher, Czech Technical University, Prague (CZ) maiapand@fd.cvut.cz Josep Laborda CEO & Managing Partner, FACTUAL, Sant Cugat Del Valles (ES) josep@factual-consulting.com Internationales Verkehrswesen (75) 1 | 2023 43