Mathematical Problems in Engineering

Volume 2015, Article ID 348036, 7 pages

http://dx.doi.org/10.1155/2015/348036

## Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

^{1}Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China^{2}National Defense Transportation Department, Military Transportation University, Tianjin 300161, China

Received 2 August 2014; Accepted 27 February 2015

Academic Editor: Emilio Insfran

Copyright © 2015 Hua-pu Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.

#### 1. Introduction

It is of practical significance to predict traffic flow quickly, precisely, and timely. Short-term traffic flow forecasting provides an important basis for traffic guidance and control. Existing studies of short-term traffic flow forecasting can be classified into six categories in transportation literature:(a)linear system theory based models, such as Autoregressive Integrated Moving Average (ARIMA) [1] and Kalman Filtering model [2];(b)data mining based models, such as Neural Network [3], Nonparametric Regression [4], and Support Vector Machine [5];(c)nonlinear system theory based models, such as Wavelet Analysis [6], Catastrophe Theory [7], and Chaos Theory [8];(d)simulation based models [9];(e)combination model based models [10];(f)the other models.

In the era of big data, it brings both opportunities and challenges to short-term traffic flow forecasting. During data processing, traffic big data meets the same difficulties with the general big data, such as capture, storage, search, sharing, analytics, and visualization. Therefore, short-term traffic flow forecasting method needs to have the capacity to deal with traffic big data. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Considering the advantages of traffic big data, data-driven based mathematical models can be set up. The physical meaning of these models can by described clearly. In addition, we can put forward real-time correction algorithm to improve the accuracy of traffic flow forecasting.

However, taking into account all the present researches in this field, there is still a lack of consideration of traffic big data and real-time correction for traffic flow forecasting. Further researches remain to be conducted on the direction of traffic big data analysis. In this paper, the method of short-term traffic flow forecasting is proposed in detail. The remainder of this paper is organized as follows. Section 2 presents basic mathematical model. In Section 3, real-time corrected traffic correlation model is established. A case study based on a real-world road network is carried out in Section 4 to demonstrate the performance and applicability of the proposed method. Finally, conclusions are drawn in Section 5.

#### 2. Basic Mathematical Model

##### 2.1. Big Data Driven Based Traffic Correlation Model

Traffic big data has a strong temporal-spatial-historical correlation as follows.(i) In the temporal series, the traffic flow of last moment can be regarded as a continuation of current traffic flow. Dynamic traffic flow data continuously change over time with a certain trend.(ii) In the spatial series, the traffic flow of downstream sections can be seen as a continuation of the upstream traffic flow. There exists a spatial association between traffic flow data of neighboring junctions or sections and that of target junctions or sections.(iii) In the historical series, the traffic demand characteristics determine that traffic flow characteristics of the same day in the same period are similar. The law of traffic flow cycle is especially evident.

Therefore, the basic form of traffic correlation model [11] is expressed aswhere is the traffic flow parameter of section at time , representing flow , speed , or occupancy . , , and are the estimated value of . , , and are coefficients of these three variables.

is calculated by temporal correlation analysis, generally based on Regression Analysis Model [12]. is calculated by spatial correlation analysis, generally based on Neighbor Regression Model [13]. is calculated by historical correlation analysis, generally based on Discrete Fourier Transform Model [14].

Thus, simplified equation of traffic correlation model is obtained:where is regression coefficient of . is the number of .

##### 2.2. Correction Coefficients Optimization Algorithm

It is found that the speed and accuracy of data processing are both important for big data driven method. To improve the speed of traffic correlation model, the number of unknown variables in formulation (2) must be reduced. However, variables reduction may decrease the accuracy of the model.

Therefore, this paper defines a threshold of computing speed and derives the maximum of acceptable number of variables. Thus, the number of unknown variable can be achieved. The correlation coefficients () between the studied section and the related section are used to choose variables, the number of which is :

In addition, for , a unique value of is determined. Therefore, a lot of variables are reduced. When the max of correlation coefficient () corresponding to each section is calculated, corresponding time delay () and the unique value of are obtained:

If the value of is large enough, the variable is preserved. Otherwise, the variable is reduced. After several data tests, it is found that the values of are not very different with relatively high values when is less than 4; the rapid decay of is observed with relatively low values when is more than 8. Therefore, .

Variables reduction makes meaningless. So, a new variable , which is normalized , is selected to replace the variable :

Since the alternative process will bring some errors, which are likely to be systematic, a linear correction algorithm is present. Two correction variables and are introduced for calibration error. Simplified traffic correlation model iswhere is the actual value of .

#### 3. Model Improvement

##### 3.1. Real-Time Correction Problem Statement

Basic mathematical model can be used for traffic flow forecasting. The error of traffic flow forecasting is written as

Therefore, is achieved:

The variation range of reflects the accuracy of traffic flow forecasting. To improve the accuracy of traffic flow forecasting, is made as a compensation variable for . Then, is replaced by in formulation (8):

For different traffic state, the propagation of traffic congestion is different. So, the temporal-spatial-historical correlation variables are dynamic. As shown in Section 2, basic mathematical model for traffic flow forecasting is put forward based on temporal-spatial-historical correlation characteristic of traffic big data. However, the multistate characteristic is largely ignored; the temporal-spatial-historical correlation variables are seen as static variables. Although a linear fitting method is used to improve accuracy, the error which is the main part of still exists.

Effective analysis of traffic correlation model is shown in Figure 1. The error of traffic congestion stage is larger than that of traffic smooth stage. The error of last moment may affect the error of current moment.