CN108615360B - Prediction method of daily evolution of traffic demand based on neural network - Google Patents

Prediction method of daily evolution of traffic demand based on neural network Download PDF

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CN108615360B
CN108615360B CN201810431068.3A CN201810431068A CN108615360B CN 108615360 B CN108615360 B CN 108615360B CN 201810431068 A CN201810431068 A CN 201810431068A CN 108615360 B CN108615360 B CN 108615360B
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刘志远
程启秀
刘洋
魏薇
俞俊
李喆康
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Abstract

本发明公开了一种基于神经网络的交通需求逐日演变预测方法，根据需求预测区域内各小区之间的手机信令数据，构建某时间段内交通出行的生成吸引OD矩阵，利用机器学习中深度学习的方法，建立循环神经网络模型和特征级融合神经网络模型，从而预测该区域逐日演变的动态交通需求量，并且充分考虑了时间和空间的内在关联性。本发明提出循环神经网络模型和特征级融合神经网络模型，对于短期动态交通需求预测有着很高的灵活性及准确度。

Figure 201810431068

The invention discloses a method for predicting the daily evolution of traffic demand based on a neural network. According to the mobile phone signaling data between each cell in the demand prediction area, the generation and attraction OD matrix of traffic travel in a certain period of time is constructed, and the depth of machine learning is utilized. The learning method establishes a recurrent neural network model and a feature-level fusion neural network model, so as to predict the dynamic traffic demand of the area evolving day by day, and fully consider the inherent correlation of time and space. The invention proposes a cyclic neural network model and a feature-level fusion neural network model, which has high flexibility and accuracy for short-term dynamic traffic demand prediction.

Figure 201810431068

Description

Traffic demand day-to-day evolution prediction method based on neural network
Technical Field
The invention relates to the technical field of urban traffic, in particular to a traffic demand day-to-day evolution prediction method based on a neural network.
Background
The traffic demand prediction is to build a model according to the traffic conditions and characteristics of the past and the present situation and predict the future traffic flow change. The key to solve the urban traffic problem lies in realizing the balance of traffic supply and demand, and the accurate analysis of traffic demand is the basis for solving the contradiction between supply and demand.
Travel generation prediction is the basis of traffic demand prediction in urban traffic planning, and important support is provided for an effective and accurate dynamic traffic distribution model. However, the existing traffic demand prediction model is mainly based on the traditional manual survey mode and a large amount of relevant data such as economy and population, so that huge manpower and material resources are consumed, the result of the model is lack of accuracy and timeliness, and the dynamic and real-time management requirements on a large-scale traffic network in an intelligent traffic system cannot be met.
In summary, the existing traffic demand prediction method has many defects; the traditional traffic generation prediction research based on the four-stage traffic demand prediction method ignores the space-time internal relevance of a traffic network, and further cannot realize the dynamic real-time analysis of a large-range traffic network in a short time.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a traffic demand day-by-day evolution prediction method based on a neural network, and data mining is performed by using a mobile phone signaling resource, so that the defects of the traditional traffic demand prediction method in the field of traffic planning in the aspects of precision and efficiency are overcome, the road network performance of each day in the traffic network day-by-day dynamic evolution process is fully considered, and the method has strong applicability.
In order to solve the technical problem, the invention provides a traffic demand day-to-day evolution prediction method based on a neural network, which comprises the following steps:
(1) dividing specific partitions of the urban traffic network and numbering;
(2) determining a cell set during neural network model training and a cell set during model verification based on the traffic network partition established in the step (1);
(3) grouping the obtained data sets according to the travel generation cells and the small time segments, and performing data preprocessing;
(4) establishing a cyclic neural network model and a feature level fusion neural network model for predicting traffic demands, wherein the cyclic neural network model and the feature level fusion neural network model are used for function estimation and weight updating of each cell under different time states;
(5) inputting the travel generated quantity of each cell as a training sample, and completing weight determination of the recurrent neural network model and the feature level fusion neural network model; when prediction is carried out, tested community traffic demand data is input into the trained cyclic neural network model and the trained feature level fusion neural network model for calculation, and finally the predicted traffic demand of the region is obtained.
Preferably, in the step (3), the data preprocessing specifically includes the following steps:
(31) filling missing data, replacing the missing data by using the average value of data of two adjacent time periods when single data is missing, and replacing the missing data by using data of the same time period in the next day when a plurality of data are missing;
(32) extracting features by using a sliding time window with the size of r and the step value of 1;
(33) the data of the requirements is standardized and the data of the requirements is,
x＝(x-mean)/std
where std is the standard deviation of the data.
Preferably, in the step (4), the establishing of the recurrent neural network model and the feature level fusion neural network model for predicting the traffic demand specifically includes the following steps:
(41) constructing a cyclic neural network model and a feature level fusion neural network model, and determining the forward propagation process of an RNN (neural network) model, wherein the RNN model consists of an input unit, an output unit and a cyclic hidden layer, and xtIs an input value at time t, stIs the hidden layer state at time t
st＝f(Uxt+Wst-1)
Wherein, the function f is a nonlinear function such as tanh or ReLU, and U and W are corresponding weights respectively;
(42) determining the input value in the neural network model and the calculation mode of a hidden layer, and for a hidden unit:
Figure BDA0001653437690000021
Figure BDA0001653437690000022
Figure BDA0001653437690000023
the input sequence x with the length of T is provided with I input units, H hidden units and K output units;
Figure BDA0001653437690000024
representing the value of the input quantity i at time t,
Figure BDA0001653437690000025
and
Figure BDA0001653437690000026
the input quantity and the excitation value omega of the whole traffic network to the hidden unit h at the time tihAnd ωh′hTo adjust the weight, θhIs composed of
Figure BDA0001653437690000031
To
Figure BDA0001653437690000032
The excitation function of (a);
(43) for the feature level fusion neural network model, taking OD matrix data, the average temperature and weather condition of the region and 5 corresponding variables of date and time as input quantities to train;
(44) the weights in the model are derived using the BPTT algorithm.
Preferably, in step (44), the weight in the model obtained by using the BPTT algorithm is specifically:
(a) in the interval
Figure BDA0001653437690000033
Inner random initialization weights (U, V, W), where n is the previous level incomings connections;
(b) let epoch be 1;
(c) running a forward propagation process in the neural network model;
(d) calculating a loss function L and a state gradient
Figure BDA0001653437690000034
And
Figure BDA0001653437690000035
Figure BDA0001653437690000036
Figure BDA0001653437690000037
Figure BDA0001653437690000038
(e) training an RNN model by using a random gradient descent and backward propagation process, and adding 1 to an epoch count value;
(f) if epoch is equal to M, the loop is stopped, at which point (U, V, W) is the weight value sought, otherwise (c) is returned.
The invention has the beneficial effects that: the method can dynamically predict the travel demand of a large-scale traffic network in real time, provides a recurrent neural network model and a feature level fusion neural network model, and has high flexibility and accuracy for predicting the short-term dynamic traffic demand.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of traffic cell division of the present invention.
Fig. 3(a) is a schematic diagram of the prediction result of the deep neural network model according to the present invention.
FIG. 3(b) is a schematic diagram of the prediction result of the recurrent neural network model of the present invention.
Fig. 3(c) is a schematic diagram of the prediction result of the feature level fusion neural network model of the present invention.
Detailed Description
As shown in fig. 1, a traffic demand day-to-day evolution prediction method based on a neural network constructs a generation attraction OD matrix of traffic travel in a certain time period according to mobile phone signaling data between cells in a demand prediction region, and establishes a recurrent neural network model and a feature level fusion neural network model by using a deep learning method in machine learning, thereby predicting a dynamic traffic demand of the region day-to-day evolution, the method comprising the following steps:
the method comprises the following steps: and dividing specific partitions of the urban traffic network and numbering.
The analysis area is divided into 1261 inner areas (1-3999) and 6 outer areas (4000-;
step two: determining a cell set during neural network model training based on the traffic network partition established in the first step, wherein the cell set is OD (optical density) quantities generated by an external cell with the number 4000-4004 and an internal cell 1261; the cell set during model verification is the OD quantity generated by the external cell with the number 4005 and the internal cell 1261;
step three: in order to solve the problems of data loss, too short time period and the like of the mobile phone signaling data, the obtained data sets are grouped according to the travel generation cell and the small time period, and data preprocessing is carried out, and the method specifically comprises the following steps:
3.1 filling missing data, when single data is missing, replacing by using the data average value of two time slices adjacent to the data, and when a plurality of data are missing, replacing by using the data of the same time slice on the next day;
3.2, extracting features by using a sliding time window with the size of r and the step value of 1;
3.3 the demand data is standardized,
x＝(x-mean)/std
wherein std is the standard deviation of the data;
step four: establishing a cyclic neural network model and a feature level fusion neural network model for predicting traffic demands, and using the cyclic neural network model and the feature level fusion neural network model for function estimation and weight updating of each cell under different time states, wherein the specific steps are as follows:
4.1 constructing a recurrent neural network model and a feature level fusion neural network model, and determining the forward propagation process of the RNN model, wherein the RNN model consists of an input unit, an output unit and a recurrent hidden layer, and xtAnd otRespectively input and output values at time t, stIs the hidden layer state at time t
st＝f(Uxt+Wst-1)
Wherein the function f is a non-linear function such as tanh or ReLU;
4.2 determining the input value in the neural network model and the calculation mode of the hidden layer, and for the hidden unit:
Figure BDA0001653437690000051
Figure BDA0001653437690000052
Figure BDA0001653437690000053
the input sequence x with length T has I input units, H hidden units and K output units.
Figure BDA0001653437690000054
Representing the value of the input quantity i at time t,
Figure BDA0001653437690000055
and
Figure BDA0001653437690000056
the input quantity and the excitation value of the whole traffic network to the hidden unit h at the time t;
4.3 for the feature level fusion neural network model, taking OD matrix data and 5 variables such as the average temperature, the weather condition, the corresponding date and the corresponding time of the region as input quantities to train;
4.4 use BackPropassivation through Time (BPTT) algorithm to get the weights in the model.
Further, in the inventive method, the BPTT algorithm flow in step 4.4 is:
a) in the interval
Figure BDA0001653437690000057
Internal random initialization weights (U, V, W), where n is the previous level of incoming connections
b) Let epo equal to 1
c) Running forward propagation processes in neural network models
d) Calculating loss value
Figure BDA0001653437690000058
Figure BDA0001653437690000059
Figure BDA00016534376900000510
e) Training an RNN model by using a random gradient descent and backward propagation process, and adding 1 to an epoch count value;
f) if the epoch is equal to M, stopping the circulation, wherein (U, V, W) is the weight value, otherwise, returning to c);
step five: inputting the travel generated quantity of each cell as a training sample, and completing weight determination of the recurrent neural network model and the feature level fusion neural network model; secondly, when prediction is carried out, the tested community traffic demand data is input into the trained cyclic neural network model and the trained feature level fusion neural network model for calculation, and finally the predicted traffic demand of the region is obtained.
Fig. 3(a), 3(b) and 3(c) show the output results of three models for the day-by-day dynamic traffic demand prediction problem. The experimental results prove that the cyclic neural network (RNN) is superior to the Deep Neural Network (DNN) in daily traffic demand prediction, and the characteristic-level fusion neural network model performs best in the three models, and the average relative error is lower than 18%.

Claims (2)

1.基于神经网络的交通需求逐日演变预测方法，其特征在于，包括如下步骤：1. The daily evolution prediction method of traffic demand based on neural network, is characterized in that, comprises the following steps: (1)划分城市交通网络的具体分区为内部区域和外部区域并编号；(1) The specific divisions of the urban transportation network are divided into internal areas and external areas and numbered; (2)基于步骤(1)中建立的交通网络分区，确定神经网络模型训练和检验时的小区集合，集合内数据为外部区域与内部区域之间产生的OD量，OD量为手机信令数据；(2) Based on the traffic network partition established in step (1), determine the set of cells when the neural network model is trained and tested, the data in the set is the OD amount generated between the external area and the internal area, and the OD amount is the mobile phone signaling data ; (3)将已得的数据集根据出行生成小区和小时段进行分组，进行数据预处理；(3) Group the obtained data sets according to the travel generation cells and hourly segments, and perform data preprocessing; (4)建立预测交通需求的特征级融合神经网络模型，用于对各小区不同时间状态下的函数估计和权值更新；建立预测交通需求的特征级融合神经网络模型具体包括如下步骤：(4) Establish a feature-level fusion neural network model for predicting traffic demand, which is used to estimate functions and update weights in different time states of each community; establishing a feature-level fusion neural network model for predicting traffic demand specifically includes the following steps: (41)构建循环神经网络模型，确定循环神经网络模型的前向传播过程，循环神经网络模型由输入单元、输出单元和循环隐藏层构成，xt为时间t时的输入值，st为时间t时的隐藏层状态(41) Build a recurrent neural network model and determine the forward propagation process of the recurrent neural network model. The recurrent neural network model is composed of an input unit, an output unit and a recurrent hidden layer, x t is the input value at time t, and s t is time. The state of the hidden layer at time t st＝f(Uxt+Wst-1)s t =f(Ux t +Ws t-1 ) 其中，函数f为非线性函数，U和W分别为相应的权值；Among them, the function f is a nonlinear function, and U and W are the corresponding weights respectively; (42)确定循环神经网络模型中的输入值和隐藏层的计算方式，对于隐藏单元：(42) Determine the input value in the recurrent neural network model and the calculation method of the hidden layer, for the hidden unit:
Figure FDA0003348586900000011
Figure FDA0003348586900000011
Figure FDA0003348586900000012
Figure FDA0003348586900000012
Figure FDA0003348586900000013
Figure FDA0003348586900000013
 其中，长度为T的输入序列x，其拥有I个输入单元，H个隐藏单元以及K个输出单元；
Figure FDA0003348586900000014
代表时间为t时，输入量i的值，
Figure FDA0003348586900000015
和
Figure FDA0003348586900000016
为时间t时整个交通网对隐藏单元h的输入量和激发值，ωih和ωh′h为调整权值，θh为
Figure FDA0003348586900000017
到
Figure FDA0003348586900000018
的激发函数；Among them, the input sequence x of length T has I input units, H hidden units and K output units;
Figure FDA0003348586900000014
Represents the value of the input quantity i when the time is t,
Figure FDA0003348586900000015
and
Figure FDA0003348586900000016
is the input and excitation value of the entire traffic network to the hidden unit h at time t, ω ih and ω h′h are the adjustment weights, and θ h is
Figure FDA0003348586900000017
arrive
Figure FDA0003348586900000018
the excitation function; (43)对于循环神经网络模型，将OD矩阵数据和该地区的平均气温、天气状况和对应的日期、时间5个变量一起作为输入量进行训练，得到特征级融合神经网络模型；(43) For the cyclic neural network model, the OD matrix data and the average temperature of the region, the weather condition and the corresponding date and time 5 variables are used for training together as input quantities to obtain a feature-level fusion neural network model; (44)使用BPTT算法得到特征级融合神经网络模型中的权重；使用BPTT算法得到特征级融合神经网络模型中的权重具体为：(44) Using the BPTT algorithm to obtain the weights in the feature-level fusion neural network model; using the BPTT algorithm to obtain the weights in the feature-level fusion neural network model is specifically: (a)在区间
Figure FDA0003348586900000021
内随机初始化权重(U,V,W)，其中n为之前层级incomingconnections；(a) in the interval
Figure FDA0003348586900000021
Randomly initialize the weights (U, V, W) inside, where n is the incoming connections of the previous level; (b)令epoch＝1；(b) Let epoch=1; (c)运行特征级融合神经网络模型中的前向传播过程；(c) Running the forward propagation process in the feature-level fusion neural network model; (d)计算损失函数L和状态梯度
Figure FDA0003348586900000022
和
Figure FDA0003348586900000023
(d) Calculate the loss function L and the state gradient
Figure FDA0003348586900000022
and
Figure FDA0003348586900000023
Figure FDA0003348586900000024
Figure FDA0003348586900000024
Figure FDA0003348586900000025
Figure FDA0003348586900000025
Figure FDA0003348586900000026
Figure FDA0003348586900000026
 (e)用随机梯度下降和后向传播过程训练特征级融合神经网络模型，epoch计数值加1；(e) The feature-level fusion neural network model is trained with stochastic gradient descent and back-propagation process, and the epoch count value is increased by 1; (f)若epoch等于M，则停止循环，此时的(U,V,W)为所求的权重值，否则返回(c)；(f) If epoch is equal to M, stop the loop, and (U, V, W) at this time is the required weight value, otherwise return to (c); (5)输入各小区的出行生成量作为训练样本，完成特征级融合神经网络模型的权值确定；在进行测试时，将测试的小区交通需求数据输入到已经训练完成的特征级融合神经网络模型中进行计算，得到最终的特征级融合神经网络模型。(5) Input the trip generation volume of each community as a training sample, and complete the weight determination of the feature-level fusion neural network model; when testing, input the traffic demand data of the tested community into the feature-level fusion neural network model that has been trained. Calculated in the final feature-level fusion neural network model. 2.如权利要求1所述的基于神经网络的交通需求逐日演变预测方法，其特征在于，步骤(3)中，数据预处理具体包括如下步骤：2. the daily evolution prediction method of traffic demand based on neural network as claimed in claim 1, is characterized in that, in step (3), data preprocessing specifically comprises the steps: (31)填补缺失数据，单个数据缺失时，使用该数据临近的两个时间段的数据平均值代替，多个数据缺失时，使用后一天同时间段的数据代替；(31) Fill in missing data. When a single data is missing, the average value of the data in the two adjacent time periods is used to replace it. When multiple data are missing, the data of the same time period on the next day is used instead; (32)利用大小为r，步值为1的滑动时间窗提取特征；(32) Extract features using a sliding time window with a size of r and a step value of 1; (33)将需求数据进行标准化，(33) Standardize demand data, x＝(x-mean)/stdx=(x-mean)/std 其中std为数据标准差。where std is the standard deviation of the data.
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