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In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction.
By integrating the modified RL method, traffic assignment and traffic control, the modified REinforcement Learning TRANSYT-7F DynusT (RELTRAD) model is proposed to solve the dynamic ENDP.
Rather by processing images extracted from Google Street View API using a computer vision method, traffic signs are detected and categorized into four categories of regulatory, warning, stop, and yield signs.
Using computer vision method, traffic signs are detected and classified into four categories of regulatory, warning, stop, and yield signs by processing images extracted from Google Street View API.
To apply this method, traffic images were collected from the Jungja intersection and the Naejung intersection in Sungnam City, Korea and were analyzed using traffic data by means of image procedure technology.
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In our method of traffic measurement, the traffic loading factor ( left({V}_T^iright) ) is defined as: {V}_T^i v kern0.5em =kern0.5em left{frac{V_T^i v)}{1}right}kern0.5em mathrm{if}kern0.5em frac{V_T^i(v)<1}{mathrm{otherwise}} (8).
This was an older method of traffic aggregation.
Consequently, in this paper, we devise a heterogeneous grooming method called traffic partition grooming.
The commonly used proportion method includes traffic volume-based schemes [7, 8] and buffer-based schemes [9 11].
Our method aggregates traffic information so that the upper bounds of link utilization after route changes can be calculated by using the aggregated traffic information.
In this paper, we present a method for traffic signal control including simultaneously signal plan design and signal timing optimization with real-time information on the network dynamics.
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