ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications   04/25/2024

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. 49 ID: Use Case Name: AI solution for traffic signal Optimization based on multi-source data fusion
Application
Domain		Transportation
Deployment
Model		Cloud services
StatusIn operation
ScopeGenerate traffic signal timing plans by analyzing traffic flow status and patterns based on fusing internet data, induction coils data and video data, and control the traffic signal with the generated timing plans in a real-time, self-adaptive and cooperative way
Objective(s)To find an effective and efficient solution to improve the road utilization efficiency by increasing traffic flow speed and reducing traffic flow waiting time.
Short
Description
(up to
150 words) 		An AI solution was developed that could recognize real-time traffic flow status and abstract traffic flow patterns by fusing internet data, induction coils data and video data, and could generate optimized traffic signal timing plan by self-adaptively responding to real-time traffic flow fluctuation and with regards to traffic flow coordination among multiple intersections within a given region.
Complete Description By far, traffic administrator produces traffic signal timing plans by observing traffic flow situation on-site at intersections or through videos, and relies on her/his personal experience. Then, the timing plans are input into and executed by the traffic signal control system. The disadvantages of this manual traffic signal timing plan generation approach are as follows: 1. Low computing efficiency, it consumes very long time for traffic administrator to observe and analyze traffic patterns. 2. Low computing precision, traffic administrator only cares about the macro traffic flow tendency at intersections without computing detailed traffic parameters such as speed, queue length in each lane, etc. 3. Slow response to traffic flow fluctuation, it is hard for traffic administrator to produce adaptive timing plan in time with respect to real-time traffic flow fluctuation, due to her/his limited computing ability, not mention to coordinate traffic flows among multiple intersections by controlling the traffic signal in real-time. 4. Experienced traffic administrators are severely in short for cities with the scale of thousands intersections.

For solving the above problems, the AI provider applies a multi-source data fusion approach to recognize the traffic flow status and generalize the traffic flow pattern by analyzing the internet data (i.e., vehicle driving trajectory data provided by internet service supplier), detector data collected by induction coils, and structured data recognized from videos. Furthermore, the AI provider develops an optimization method to figure out optimized traffic signal timing plan by self-adaptively responding to real-time traffic flow fluctuation and with regards to traffic flow coordination among multiple intersections.

The developed methods have been applied in practice within a given region from a large city. It generates traffic signal timing plans for all the intersections in the region according to their real-time traffic flow fluctuation with an updating frequency of 5 minutes per time. Compared with the manual traffic signal timing plans form the traffic administrators, the plans generated by the new method have increased the average vehicle driving speed by 9%, and reduced the average vehicle waiting time by 15%.

StakeholdersDOT, DOP
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities		new privacy threats, new security threats
Performance
Indicators (KPIs) 		Seq. No. Name Description Reference to mentioned
use case objectives
1 Average vehicle driving speed Average vehicle driving speed on all the road sections in a given region Improve the road utilization efficiency
2 Average vehicle waiting time Average vehicle waiting time at all the intersections in a given region Improve the road utilization efficiency
AI Features Task(s)Optimization
Method(s)Deep learning, Bayesian network, Time series analysis, Operational research optimization method (i.e., Mixed integer linear programming, etc.)
HardwareECS
TopologyCloud Service
Terms &
Concepts Used		Traffic signal self-adaptive and coordinative control for a large number of intersections. Issues: 1. Not all intersections are equipped with detectors such as induction coil or video. 2. The detectors may output abnormal values which need data clean processings
Standardization
Opportunities
Requirements
Challenges
& Issues		Challenges:Traffic signal self-adaptive and coordinated control for a large number of intersections. Issues: 1. Not all intersections are equipped with detectors such as induction coil or video. 2. The detectors may output abnormal values which need data clean processing.
Societal Concerns Description Relieve urban road congestion
SDGs to
be achieved 		Sustainable cities and communities
Data Characteristics
Description Internet data, Induction coil data, Video data
Source Internet, Detector
Type Structured text and number, Structured text and number, Unstructured video
Volume (size)
Velocity Internet data updated daily, Induction coil data updated every 5 minutes, Video data updated in real-time
Variety From multiple domains
Variability
(rate of change) 		Dynamic
Quality Exists missing values or abnormal values
Scenario Conditions
No. Scenario
Name 		Scenario
Description 		Triggering Event Pre-condition Post-Condition
1 Training Train multiple models (deep learning, Bayesian network, Time series analysis) for recognizing traffic flow volume and abnormal values in the input data
2 Optimization Based on the data processed by the trained models, optimize the period length, split, and key phase offsets among multiple intersections for traffic signal timing plans Completion of missing values or abnormal values processings
3 Evaluation Pre-evaluate the execution effects of the optimized traffic signal timing plans, which include the period lengths, splits, and key phase offsets among multiple intersections Input prediction of traffic flow situation in the next period The pre-evaluated execution effects of the optimized traffic signal timing plan is superior to the current one
4 Execution Execute the optimized traffic signal timing plan The pre-evaluated execution effects of the optimized traffic signal timing plan is superior to the current one
Trainng Scenario Name:  
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement
1 Dataset is ready Transform video data into structured data AI provider Transform video data into structured data by deep learning
2 Completion of Step 1 Data clustering AI provider Recognize abnormal value patterns and label them in internet data, induction coil data, and structures video data by data clustering
3 Completion of Step 2? Processing of missing value and abnormal value AI provider Recognize abnormal value and process them, and fill missing values by data clustering, time series analysis and Bayesian network
4 Completion of Step 3 Data fusion AI provider Compute traffic status parameters such as traffic volume, vehicle driving speed, etc. by fusing internet data, induction coil data and structured video data
Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement
1 Completion of optimization Construct the evaluation model of the traffic signal timing plan AI provider Construct the evaluation model of the traffic signal timing plan based on traffic engineering theory
2 Completion of Step 1 Evaluate the effect of the computed traffic signal timing plan Traffic administrator Pre-evaluate the effect of the computed traffic signal timing plan with the evaluation model
Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity 		Primary
Actor 		Description of
Process/Activity 		Requirement
1 Completion of evaluation Execute the computed traffic signal timing plan Traffic administrator Execute the computed traffic signal timing plan The pre-evaluated execution effects of the optimized traffic signal timing plan is superior to the current one
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 patent ZHANG MAOLEI;WEI LIXIA;CHEN XIAOMING;LI JIN.?”Crossing traffic jam judging and control method and system based on sensing detectors ”.CN201310395431.2013 QINGDAO HISENSE TRANS TECH CO Link
2 patent ZHANG MAOLEI;WEI LIXIA;CHEN XIAOMING;LIU XIN;LIU HONGMEI;LI JIN.“Multi-strategy and multi-object self-adaptation traffic control method”. CN201310548921.2013 QINGDAO HISENSE TRANS TECH CO Link
3 patent WANGMENGJIA;MINWANLI.” Road traffic optimization method and device and electronic equipment”. CN201710081075.2017 ALIBABA GROUP HOLDING LTD; Link
4 patent HUA XIANSHENG” Assessment method and device of traffic condition”. CN201610645412.2016 ALIBABA GROUP HOLDING LTD; Link
5 patent HUA XIANSHENG,REN PEIRAN,SHEN CHEN,CHU WENQING,LIU YAO.” Road intersection traffic flow control method and device”. CN201610644132.2016 ALIBABA GROUP HOLDING LTD; Link
6 paper Liang Yu,Jingqiang Yu,Maolei Zhang?Xin Zhang?Yuehu Liu.”Large Scale Traffic Signal Network Optimization-a Paradigm Shift Driven by Big Data”. ICDE2019 Alibaba Cloud Computing Hangzhou?China
7 M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, and Y.Wang, “Review of road traffic control strategies,” Proceedings of the IEEE, vol. 91, no. 12, pp. 2043–2067, 2003.
8 paper P. Lowrie, “Scats, sydney co-ordinated adaptive traffic system: A traffic responsive method of controlling urban traffic,” 1990.
9 paper F. Corman, A. D’Ariano, D. Pacciarelli, and M. Pranzo, “Evaluatio of green wave policy in real-time railway traffic management,” Transportation Research Part C: Emerging Technologies, vol. 17, no. 6, pp. 607–616, 2009.
10 paper L. Singh, S. Tripathi, and H. Arora, “Time optimization for traffic signal control using genetic algorithm,” International Journal of Recent Trends in Engineering, vol. 2, no. 2, p. 4, 2009.

  • 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