Combined prediction method for short-time travel requirements of online hailed car

A travel demand and combination forecasting technology, applied in neural learning methods, marketing, biological neural network models, etc., can solve the problem that demand forecasting models are difficult to predict optimally, and achieve the effect of improving stability

Inactive Publication Date: 2019-11-05
HOHAI UNIV
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Problems solved by technology

The inventors found that in the actual forecasting of urban traffic systems, the existence of a large number of uncertain factor

Method used

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  • Combined prediction method for short-time travel requirements of online hailed car
  • Combined prediction method for short-time travel requirements of online hailed car
  • Combined prediction method for short-time travel requirements of online hailed car

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Embodiment

[0148] According to the content of the invention, the application description is given through actual cases.

[0149] Step 1: Obtain the online car-hailing demand data for 8 consecutive days in a certain area, divide the data at each time into half an hour, and obtain the online car-hailing demand data for each time period as shown in Table 1.

[0150] Table 1. Online car-hailing demand data

[0151]

[0152] Step 2: Substituting the data difference into the ARIMA model for prediction, and the prediction results shown in Table 2 are obtained.

[0153] Table 2. ARIMA model prediction results

[0154]

[0155] Step 3: Randomly initialize weights and thresholds in [0,1], substitute historical demand data into the BP neural network model for initial prediction, adjust weights and thresholds according to the error, perform backpropagation, and iterate until the error converges to a fixed value. The output final prediction results are shown in Table 3.

[0156] Table 3. BP ...

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Abstract

The invention discloses a combined prediction method for short-time travel requirements of on online hailed car. The method comprises the following specific steps: acquiring historical travel demand data; based on the acquired historical travel demand data, establishing an ARIMA model and a BP neural network model, and performing online car-hailing short-term travel requirement prediction; performing weighted combination on the ARIMA model and the BP neural network model, and calculating a weight value of the weighted combination by utilizing a principle of minimum error in an approximate historical time period to obtain a final combined prediction model; and carrying out online car-hailing travel short-time travel requirement prediction according to the constructed combined prediction model. According to the method, the advantages of two linear and nonlinear prediction models are integrated; optimal estimation can be obtained through linear iteration based on historical data in the same time period, dynamic characteristics of online car hailing requirements can be reflected through the strong nonlinear mapping capacity of the BP neural network, overlarge errors of a single prediction model can be effectively reduced, and therefore the precision of online car-hailing short-time travel requirement prediction is improved.

Description

technical field [0001] The invention relates to the technical field of urban transportation planning and management, in particular to a combined forecasting method based on short-term travel demand for online car-hailing. Background technique [0002] The demand forecasting problem of online car-hailing is the basis of operation and scheduling management of online car-hailing. Using the real-time prediction model to obtain the demand for online car-hailing in a short period of time in the future for online car-hailing operation scheduling is conducive to realizing the reasonable allocation of online car-hailing cars, reducing the empty mileage of online car-hailing cars, reducing the empty driving rate of online car-hailing cars, and improving the urban network. Ride-hailing operational efficiency provides an effective solution. The inventors found that in the actual forecasting of urban traffic systems, the existence of a large number of uncertain factors in complex traffi...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q30/06G06N3/04G06N3/08
CPCG06Q30/0202G06Q30/0607G06N3/084G06N3/044G06N3/045
Inventor 沈金星杨婷张琪霍豪齐军杰郑长江
Owner HOHAI UNIV
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