The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:
Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
Use Case Name:
Automated Travel Pattern Recognition using Mobile Network Data for Applications to Mobility as a Service
Activity- based Modelling for New mobility Services
Phase 1: Attribute trip purpose and mode of transport to multimodal door-to-door journeys from Mobile phone Network Dataset using AI and machine learning techniques (Activity based model) Phase 2: Generate daily activities for static agents in the Agent Based Model Phase 3: Optimisation of New Mobility services in integration with mass transit
Short Description (up to 150 words)
Activity- based modelling has the capability to exploit big data source generated by smart cities to create a digital twin of urban environments to test Mobility as a Service schemes. MND data have been used to create activities for an Agent Based Model. AI is used to automatically detect purpose and mode of transport in multimodal round trips, obtained by anonymized and aggregated MND trip-chains dataset. Data fusion techniques and SQL queries were also used to consider land use and facilities in the urban area of interest.
Activity- based modelling has the capability to exploit big data source generated by smart cities to create a digital twin of urban environments to test Mobility as a Service schemes. Given the rise of location- based data and Mobile phone Network Data (MND) for transport modelling purpose, Agent based modelling has become a viable tool to explore a sustainable introduction of mobility services, exploring the integration with mass transit. AI is used in detecting purpose and mode of transport in multimodal round trips and assign purpose and mode of transport to trip- chains dataset coming from MND. The methodology has been developed for the Innovate UK funded Mobility on Demand Laboratory Environment (MODLE) project and will undergo a validation process during the Demand Modelling and Assessment through a Network Demonstrator (DeMAND) project for the Department for Transport (UK)
Franco P, Johnston R, McCormick (2019) Demand Responsive transport: generation of activity patterns from mobile phone network data to support the operation of flexible mobility services. - Special issue of Transportation Research Part A (TRA) on developments in Mobility as a Service (MaaS) and intelligent mobility (forthcoming)
Franco P, Johnston R, McCormick E (2018) Role of Intelligent Transport Systems applications in the uptake of mobility on demand services, United Nation “Transport and Communications Bulletin for Asia and the Pacific, 2018, No. 88 - Intelligent Transport Systems”.
Franco P, McCormick E, Johnston R (2018) Multimodal activity Modelling for supporting mobility service operations, ITS World Congress Copenhagen, 17-21 September 2018
Franco P, McCormick E, Van Leeuwen K, Ryan Johnston, Gregor Engelmann (2017) Multi-Modal Activity-Based Models to support Flexible Demand Mobility Services. ITS World Congress 2017, Montreal 29 October- 2 November 2017. Awarded Best Paper
Franco P, McCormick E, Van Leeuwen K (2017) Framework for modelling MaaS using ABM and real-time data from ride-sharing services. 12ve ITS Europe Congress 2017, Strasbourg, 19-22 June 2017.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
High quality company whitepapers and presentations
Publicly accessible sources with sufficient detail
This list is not exhaustive. Other credible sources may be acceptable as well.
Examples of credible sources:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013