ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/18/2024

Editor's comments and enhancements are shown in green. [✓ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 52 ID: Use Case Name: Automated Travel Pattern Recognition using Mobile Network Data for Applications to Mobility as a Service
Application
Domain		Transportation
Deployment
Model		Activity- based Modelling for New mobility Services
StatusPoC
ScopeDetect automatically travel pattern recognition from anonymized and aggregated Mobile phone Network Data
Objective(s)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.
Complete Description 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)
Stakeholders
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 Generation of Activities (land use information and time of travel) Purpose of activities is assigned based on land use information and time of travel. Census data and national/ local travel surveys will provide validation for the process Phrase 1
2 Generation of agents (travel times, speed on link) Agents generated will build up in the network creating realistic conditionsw of congestion. Speed on links. Phrase 2
3 Opeartion of service (number of users for the service) Optimisation of route and operation time in the day. Validation provided using data collected by Mobility service operators during the operation of service Phrase 3
AI Features Task(s)Assign purpose of each trip in the chain, assign model of transport for each trip in the chain, generate daily activity plans, generate static agents (users), generate dynamic agents (service)
Method(s)Agent Based Models with Activity based approach
HardwareNA
Topology
Terms &
Concepts Used		Data fusion, machine learning techniques
Standardization
Opportunities
Requirements
Challenges
& Issues
Societal Concerns Description
SDGs to
be achieved
Data Characteristics
Description
Source
Type
Volume (size)
Velocity
Variety
Variability
(rate of change)
Quality
Scenario Conditions
No. Scenario
Name 		Scenario
Description 		Triggering Event Pre-condition Post-Condition






Training Scenario Name:
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case 		Originator
Organization 		Link
1 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) Link
2 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”. Link
3 Franco P, McCormick E, Johnston R (2018) Multimodal activity Modelling for supporting mobility service operations, ITS World Congress Copenhagen, 17-21 September 2018
4 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
5 Proceedings 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. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • 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:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] 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.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] 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