ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/20/2024

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. 29 ID: Use Case Name: Enhancing traffic management efficiency and infraction detection accuracy with AI technologies
Application
Domain		Transportation
Deployment
Model		Cloud services and on-premise systems
StatusIn operation
ScopeUtilizing AI technologies in traffic monitoring and management
Objective(s)To increase the accuracy and efficiency of infraction detection, traffic monitoring and flow analysis, while minimizing the human effort and the overall solution cost.
Short
Description
(up to
150 words) 		Big data enabled AI technologies are applied to monitoring and managing the traffic in a large municipality in China. Multi-sourced data (traffic flow, vehicle data, pedestrian movement, etc.) is monitored, from which illegal operation of vehicles, unexpected incidents, surge of traffic etc. are detected and analysed with machine learning (ML) methods. ML tasks (including training and deployment) are carried out on a platform supporting the integration of various ML frameworks, models and algorithms. The platform is based on heterogeneous computing resources. The efficiency and accuracy of infraction detection, and the effectiveness of traffic management are significantly improved, with much reduced human effort and overall solution cost.
Complete Description With the population and the number of vehicles growing in large cities, managing the heavy traffic in urban areas has become a challenging yet essential task for the municipality. Addressing this issue has become particularly urgent for big cities in China, where millions of people live and commute every day.
In this use case, big data based AI technologies are applied to monitoring and managing the heavy traffic in a metropolitan in south China. Previously, significant human resources were involved in the vehicle and road monitoring, and large investment was made to the computing infrastructure specific to certain functionalities. To increase the efficiency of urban transportation, reduce the traffic jam and air pollution, as well as minimize the human effort, machine learning techniques (e.g. deep learning) are applied to image and video analysis, such as traffic flow analysis, infraction detection and incident detection. Example applications include but not limited to 1) detection of traffic rule violation, e.g. over-speeding, wrong driving lanes or parking. AI-enabled detection produces much faster and more accurate result, and helps in enforcing the traffic regulation. 2) traffic light optimization. Based on the modelling and analysis of multi-sourced traffic information (both real-time and historical data), traffic lights are dynamically configured to divert the flow, increase the passing speed of cars and reduce the traffic jam in major junctions.
The use of AI has obtained remarkable results: The infraction detection efficiency gets 10X increase, and the detection accuracy is greater than 95%. The urban area traffic jam is much alleviated, with vehicles’ passing speed through major junctions increases by 9%-25%.
StakeholdersUrban citizens (drivers and pedestrians), government, car companies, traffic administrative bureaus, logistics companies, etc.
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities		Low quality pictures, insufficient processing capability
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 accuracy The accuracy of infraction and incident detection from traffic pictures/videos To increase the accuracy of traffic monitoring and inspection
2 split Proportion of images requiring human inspection. The less the split, the higher the efficiency. To minimize the human effort in inspection
3 resource utilization ratio Achievable resource utilization ratio in the hardware infrastructure ( the higher the utilization ratio, the lower amount the required resource) To reduce the infrastructure investment and overall solution cost
AI Features Task(s)Recognition
Method(s)Machine learning, Deep learning
HardwareHeterogeneous computing platform (CPU plus heterogeneous accelerators such as GPU, FPGA etc.)
Topology
Terms &
Concepts Used		Heterogeneous resource pooling, on-demand resource scheduling
Standardization
Opportunities
Requirements
  • Requirement of computing infrastructure to empower AI applications in the transportation domain, e.g. the integration of acceleration units (GPU, FPGA, etc.), dynamic scheduling and on-demand allocation of heterogeneous resources
  • Support of mainstream ML frameworks, and the algorithms and models from different vendors, to prevent vendor lock-in
    • Challenges
    & Issues
  • Constant improvement in hardware architecture to increase the performance and efficiency of running ML/DL tasks
  • Consistent interfaces between applications, ML engines and heterogeneous resource pools
  • Support of new models and emerging algorithms for growing functionalities
    • Societal Concerns Description AI’s application in urban transportation significantly improves the quality of life for urban citizens, reduces the time wasted in heavy traffic and the air pollution from vehicles.
    SDGs to
    be achieved         		Sustainable cities and communities
    Data Characteristics
    Description Traffic data (vehicle, road, and pedestrian data)
    Source Traffic camera
    Type Image, video
    Volume (size) approx. 100TB/day
    Velocity Stream and batch
    Variety Traffic flows, vehicle information, pedestrian information, etc.
    Variability
    (rate of change)         		Subject to random surge (rush hour, accident, etc.)
    Quality Vary (depending on the weather condition, environment etc.)
    Scenario Conditions
    No. Scenario
    Name     		Scenario
    Description     		Triggering Event Pre-condition Post-Condition
    1 Training Train a model (e.g. neural network) with training samples
    2 Evaluation Evaluate whether the model is properly trained for the detection Meeting KPI requirements (e.g. accuracy, split) of the particular case
    3 Execution Deploy the model for infraction detection and traffic analysis The model has been evaluated as properly trained.
    4 Retraining Retrain a model with training samples
    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 ?Journal Published online Huawei Technologies Co., Ltd. Link

  • 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