CN110929926A - Short-term explosion passenger flow prediction method based on long and short-term memory network and random forest - Google Patents
Short-term explosion passenger flow prediction method based on long and short-term memory network and random forest Download PDFInfo
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- 230000007787 long-term memory Effects 0.000 title claims abstract description 15
- 230000006403 short-term memory Effects 0.000 title claims abstract description 15
- 238000004880 explosion Methods 0.000 title claims abstract description 8
- 230000015654 memory Effects 0.000 claims abstract description 17
- 238000012549 training Methods 0.000 claims description 15
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- 230000037323 metabolic rate Effects 0.000 claims description 3
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Abstract
The invention discloses a short-term explosion passenger flow prediction method based on a long-term and short-term memory network and a random forest, which is used for solving the technical problem of poor practicability of the conventional passenger flow prediction method. The technical scheme includes that two single models, namely a long-short term memory network and a random forest, are combined, the long-short term memory network is used for fitting daily passenger flow time series data, then the random forest is used for fitting residual errors between the long-short term memory network and the random forest, and finally prediction results of the two trained single models are superposed to obtain a combined model prediction result. The combined model combines the advantages of two single models, improves the prediction accuracy compared with the single model, has higher prediction stability, correspondingly improves the prediction of the passenger flow peak value, is particularly suitable for the prediction of short-term explosive passenger flow, and has good practicability.
Description
Technical Field
The invention relates to a passenger flow prediction method, in particular to a short-term explosion passenger flow prediction method based on a long and short-term memory network and a random forest.
Background
The document "design U.S. tourist areas using optimal simple tourist map analysis, Tourism Management,2015,46: 322-135" discloses a method for predicting tourist needs using Singular Spectral Analysis (SSA), which article shows significant advantages in predicting the number of tourists going to the united states. The model construction mainly comprises two stages of decomposition and reconstruction, wherein the decomposition stage mainly comprises two steps of embedding and singular value decomposition; in the reconstruction phase, two steps of grouping and diagonal averaging are mainly included, so that the model reaches the optimal prediction state. However, the method is single model prediction, needs to be improved in the aspects of application range, accuracy and the like, does not consider the current situation that domestic tourism is greatly influenced by factors such as festivals and holidays, and is not suitable for solving the problem of short-term explosive passenger flow prediction.
Disclosure of Invention
In order to overcome the defect that the existing passenger flow prediction method is poor in practicability, the invention provides a short-term explosion passenger flow prediction method based on a long-term and short-term memory network and a random forest. The method combines two single models, namely a long-short term memory network and a random forest, firstly uses the long-short term memory network to fit daily passenger flow time series data, then adopts the random forest to fit residual errors between the long-short term memory network and the random forest, and finally superposes the prediction results of the two trained single models to obtain a combined model prediction result. The combined model combines the advantages of two single models, improves the prediction accuracy compared with the single model, has higher prediction stability, correspondingly improves the prediction of the passenger flow peak value, is particularly suitable for the prediction of short-term explosive passenger flow, and has good practicability.
The technical scheme adopted by the invention for solving the technical problems is as follows: a short-term explosion passenger flow prediction method based on a long-term and short-term memory network and a random forest is characterized by comprising the following steps:
step one, selecting a prediction variable factor.
And comprehensively considering correlation, repeatability and feasibility factors, and selecting the daily passenger flow volume, the daily weather conditions such as temperature, wind direction, wind speed and humidity, the network search index and the holiday data of the scenic spot as the prediction variable factors by the passenger flow prediction model.
And step two, training a prediction model.
And preprocessing the original data to convert the data into data suitable for the model. Selecting prediction variable factors, preprocessing data comprising three indexes of a warm-wet index, a wind effect index and a dressing index, respectively expressing the three indexes by XTHI, XWCI and XICL, and calculating formulas shown in formulas (1) to (3).
XTHI＝(1.8t+32)-0.5(1-f)(1.8t-26) (1)
t is the temperature in centigrade, f isRelative humidity%, v represents wind speed m/s, s represents sunshine hours H/d, H represents 75% W/m of human metabolic rate2A represents the absorption condition of human body to solar radiation, the numerical value of A is 0.06, R represents the solar radiation received by the unit area of the vertical sunlight, and the value is (1385 +/-7) W/m2And α represents the solar altitude.
Secondly, training the long-term and short-term memory network is carried out.
And converting the corresponding year, month and day of the passenger flow into an ordered sequence. The information of each day is recorded as a record, the record comprises a serial number converted from the date, a current day comfort index, a current day holiday index, a yesterday search index and yesterday passenger flow, and the corresponding result is the current day passenger flow. After part of the abnormal data is removed, the size of the Batch size is set as the whole data set, and the iteration number is 1000. And importing the data into the long-short term memory network to obtain the prediction result of the long-short term memory network.
And thirdly, training the random forest.
The number of the maximum submodels of the random forest is set to be 1000, the method for judging whether the node is continuously split is the mean square error, all the characteristics are involved in the judgment when the node is split, and the maximum depth of the random forest is not limited. Meanwhile, in order to increase the training speed and in consideration of the load-bearing capacity of the machine itself, the number of parallels is set to 16. And importing the record into a random forest, and training residual errors to obtain a prediction result of the random forest.
And finally, carrying out model combination work.
The sum of the prediction results of the long-term and short-term memory network model and the random forest model is the final prediction result of the combined model.
The invention has the beneficial effects that: the method combines two single models, namely a long-short term memory network and a random forest, firstly uses the long-short term memory network to fit daily passenger flow time series data, then adopts the random forest to fit residual errors between the long-short term memory network and the random forest, and finally superposes the prediction results of the two trained single models to obtain a combined model prediction result. The combined model combines the advantages of two single models, improves the prediction accuracy compared with the single model, has higher prediction stability, correspondingly improves the prediction of the passenger flow peak value, is particularly suitable for the prediction of short-term explosive passenger flow, and has good practicability.
The present invention will be described in detail with reference to the following embodiments.
Detailed Description
The invention will now be further described with reference to the example of traffic prediction in the scenic spot of the four girl mountains. The four girl mountains are a typical mountain type scenic spot and have a certain degree of popularity across the country. Most importantly, the informatization process is promoted earlier by the four girl mountains, sufficient data are provided, and daily passenger flow data are easy to obtain.
The invention discloses a short-term explosion passenger flow prediction method based on a long-term and short-term memory network and a random forest, which comprises the following specific steps:
step 1: and selecting a predictive variable factor.
The factors such as relevance, repeatability and feasibility are comprehensively considered, and the daily passenger flow, the daily weather conditions (temperature, wind direction, wind speed and humidity), the network search index and the holiday data of the scenic spot of the four girl mountains are selected as the factors of the prediction variables by the passenger flow prediction model.
Step 2: and training a prediction model.
And preprocessing the original data to convert the data into data suitable for the model. In combination with the specific predictor variables selected in this example, the data pretreatment included three indices, namely, the temperature-humidity index, the wind efficiency index and the dressing index, which were respectively expressed by XTHI, XWCI and XICL, and the calculation formulas are shown in formulas (1) to (3).
XTHI＝(1.8t+32)-0.5(1-f)(1.8t-26) (1)
t is temperature in centigrade (DEG C), f is relative humidity (%), v represents wind speed (m/s), s represents sunshine hours (h ^ er)d) H represents 75% (W/m) of human metabolic rate2) A represents the absorption condition of the human body to solar radiation (after the practical condition is comprehensively considered, the numerical value of A is selected to be 0.06), and R represents the solar radiation (with the value of 1385 +/-7) W/m received by the unit area of the land vertical to the sunlight2) And α represents the solar altitude.
Secondly, training the long-term and short-term memory network is carried out.
And converting the corresponding year, month and day of the passenger flow into an ordered sequence. The information of each day is recorded as a record, the record comprises a serial number converted from the date, a current day comfort index, a current day holiday index, a yesterday search index and yesterday passenger flow, and the corresponding result is the current day passenger flow. After part of the abnormal data is removed, the size of the Batch size is set as the whole data set, and the iteration number is 1000. And importing the data into the long-short term memory network to obtain the prediction result of the long-short term memory network.
And thirdly, training the random forest.
The number of the maximum submodels of the random forest is set to be 1000, the method for judging whether the node is continuously split is the mean square error, all the characteristics are involved in the judgment when the node is split, and the maximum depth of the random forest is not limited. Meanwhile, in order to increase the training speed and in consideration of the load-bearing capacity of the machine itself, the number of parallels is set to 16. And importing the record into a random forest, and training residual errors to obtain a prediction result of the random forest.
And finally, carrying out model combination work.
The sum of the prediction results of the long-term and short-term memory network model and the random forest model is the final prediction result of the combined model.
And comparing the long-short term memory network model with the random forest model, wherein the three models belong to regression models, and two evaluation indexes, namely root mean square error and R square, are selected to verify the prediction effects of the combination model, the long-short term memory network model and the random forest model, and the specific results are shown in tables 1 and 2.
TABLE 1 comparison of the results of the three models
TABLE 2 three model explosive passenger flow prediction results
The experiment and the final experiment result show that the combined model combining the long-term and short-term memory network model and the random forest model is superior to a single model. The combined model is optimal from either the root mean square error or the R-squared index. In the aspect of short-term explosive passenger flow prediction, the combined model also has outstanding advantages which cannot be compared with a single model. The combined model obtains stronger nonlinear fitting capability by selecting two single models which are excellent in nonlinear prediction, so that the combined model has certain advantages in passenger flow prediction.
Claims (1)
1. A short-term explosion passenger flow prediction method based on a long-term and short-term memory network and a random forest is characterized by comprising the following steps:
step one, selecting a prediction variable factor;
comprehensively considering correlation, repeatability and feasibility factors, selecting daily passenger flow of scenic spots, daily weather conditions such as temperature, wind direction, wind speed and humidity, network search indexes and holiday data as prediction variable factors by a passenger flow prediction model;
step two, training a prediction model;
preprocessing the original data and converting the preprocessed original data into data suitable for a model; selecting prediction variable factors, preprocessing data including three indexes of temperature-humidity index, wind efficiency index and dressing index, respectively expressing the three indexes by XTHI, XWCI and XICL, and calculating formulas shown in formulas (1) to (3);
XTHI＝(1.8t+32)-0.5(1-f)(1.8t-26) (1)
t is the temperature in centigrade, f is the relative humidity percent, v represents the wind speed m/s, s represents the sunshine hours H/d, H represents 75 percent W/m of the human body metabolic rate2A represents the absorption condition of human body to solar radiation, the numerical value of A is 0.06, R represents the solar radiation received by the unit area of the vertical sunlight, and the value is (1385 +/-7) W/m2α represents the solar altitude;
secondly, training the long-term and short-term memory network;
converting the corresponding year, month and day of the passenger flow into an ordered sequence; recording the information of each day as a record, wherein the record comprises a serial number converted from the date, a current day comfort index, a current day holiday index, a yesterday search index and yesterday passenger flow, and the corresponding result is the current day passenger flow; after part of abnormal data is removed, setting the size of the Batch size as a whole data set, wherein the iteration times are 1000 times; importing the data into the long-short term memory network to obtain a prediction result of the long-short term memory network;
thirdly, training the random forest;
setting the number of the maximum submodels of the random forest to be 1000, judging whether the node is continuously split by adopting a mean square error method, wherein all characteristics participate in judgment when the node is split, and the maximum depth of the random forest is not limited; meanwhile, in order to accelerate the training speed and take the bearing capacity of the machine into consideration, the parallel number is set to be 16; importing the record into a random forest, training residual errors, and obtaining a prediction result of the random forest;
finally, model combination work is carried out;
the sum of the prediction results of the long-term and short-term memory network model and the random forest model is the final prediction result of the combined model.
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Cited By (3)
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| CN111985706A (en) * | 2020-08-15 | 2020-11-24 | 西北工业大学 | Scenic spot daily passenger flow volume prediction method based on feature selection and LSTM |
| CN112215424A (en) * | 2020-10-16 | 2021-01-12 | 平安国际智慧城市科技股份有限公司 | Medical index prediction method, device, electronic equipment and storage medium |
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Cited By (5)
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