Method and device for intelligently and automatically controlling urban elevated expressway ramps based on multi-source data and storage medium
A multi-source data and automatic control technology, applied in the field of urban road traffic control management, can solve problems such as rough means, inability to adapt to traffic situation, manual observation, etc.
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Embodiment 1
[0060] A method for intelligent automatic control of urban elevated expressway ramps based on multi-source data, such as figure 1 shown, including:
[0061] (1) Data collection, including: road network data, road condition speed, OD data, date and time, and control measures;
[0062] Road network data refers to the topology data of the basic road network, including the upstream and downstream relationships between each elevated road section, the elevated exit, the elevated entrance, the relationship between the elevated entrance and exit and the elevated road section, and the relationship between the elevated and auxiliary roads, and are included in the attribute database;
[0063] The road condition speed refers to the historical road condition speed and real-time road condition speed of each road section;
[0064] OD data refers to: the bayonet device monitors the vehicle flow of the elevated road and the entrances and exits of each elevated road in real time, and identifie...
Embodiment 2
[0085] A method for intelligent automatic control of urban elevated expressway ramps based on multi-source data according to Embodiment 1, the difference is:
[0086] After step (3), predict the traffic flow and optimize the control scheme, which specifically refers to:
[0087] Since the traffic flow changes with time, the control scheme calculated by the above decision tree model is based on the results obtained from the current real-time data. In order to adapt to the subsequent changes in the traffic flow, the traffic flow conditions are first predicted, and then the current calculation results are calculated by the optimization algorithm. optimization.
[0088] D. Short-term forecast of traffic flow
[0089] The autoregressive model is used, as shown in formula (II):
[0090]
[0091] In formula (II), m refers to the autoregressive order, a i refers to the autoregressive coefficient (AR coefficient) of period i, w n is the white noise, q n means the traffic flow; ...
Embodiment 3
[0100] A computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the processor implements the steps of the method for intelligent automatic control of an urban elevated expressway ramp based on multi-source data in Embodiment 1 or 2.
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