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:
AI solution for traffic signal Optimization based on multi-source data fusion
Generate 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
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.
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%.
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.
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
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.
P. Lowrie, “Scats, sydney co-ordinated adaptive traffic system: A traffic responsive method of controlling urban traffic,” 1990.
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.
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. ).
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