Listen
❯
Time Series
❯Raw Data
❯AI solution for traffic signal optimization based on multi-source data fusion
Carrier interference detection and removal for satellite communication
For:
Operators of satellite communication systems
Operators of other communication systems (satellite or non-satellite) that are
potential sources of interference
Users of satellite communication systems
Regulation authorities
Space agencies
Scope:
Machine-learning-based detection, classification and removal of interference
signals for satellite communication systems.
Goal:
Other
AI solution for traffic signal optimization based on multi-source data fusion
For:
Department of transportation (DOT)
Department of police (DOP)
Scope:
Generate traffic signal timing plans by analysing the 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.
Goal:
Improve Operation Efficiency
AI solution for traffic signal optimization based on multi-source data fusion
For:
Department of transportation (DOT)
Department of police (DOP)Goal:
Improve Operation EfficiencyProblem addressed
To find an effective and efficient solution to improve road utilization efficiency
by increasing traffic flow speed and reducing traffic flow waiting time.
Scope of use case
Generate traffic signal timing plans by analysing the 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.
Description
A traffic administrator produces traffic signal timing plans by
observing the traffic flow situation on-site at intersections or
through videos, and relies on her/his personal experience.
These timing plans are then 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 takes a very long
time for a traffic administrator to observe and analyse traffic
patterns. 2. Low computing precision - a traffic administrator
only focuses on 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 a traffic
administrator to produce an adaptive timing plan in time to
handle real-time traffic flow fluctuation, due to her/his
limited computing efficiency, not to mention having to
coordinate traffic flows among multiple intersections by
controlling the traffic signals in real-time. 4. Experienced
traffic administrators are severely in short supply for cities
with a scale of thousands of intersections.
To solve 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 analysing
internet data (i.e., vehicle driving trajectory data provided by
internet service suppliers), detector data collected by
induction coils, and structured data recognized from videos.
Furthermore, the AI provider develops an optimization
method that works out an optimized traffic signal timing
plan by self-adaptively responding to real-time traffic flow
fluctuation and can provide traffic flow coordination among
multiple intersections.
The developed methods have been applied in practice within
a given region in 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 m/t. Compared with the manual traffic signal timing
plans from 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 %.
Raw Data
AI: Perceive
Bayesian network
time series analysis
AI: Understand