In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.
Recently, the rapid development of urban economy brings along the improvement of resident consumption levels. With an increasing amount of private car ownership, there is an increasing trend of serious traffic problems. Urban traffic congestion has gradually spread from a single point line to a connected piece of area and even the whole road network. The evolution of traffic status follows certain patterns, especially the pattern of regional traffic status, which is the prominent jam phenomenon shown by literature [
Regional traffic status, like the road traffic status, belongs to a kind of traffic participants’ awareness information, which means it is a subjective concept. Therefore, many studies on regional traffic status demonstrate different understandings. However, there is few research for regional traffic status at this stage. The related research is divided into two categories: qualitative research and quantitative research. The studies of regional traffic status mostly stay on the qualitative research, especially most of the ones studied by Chinese scholars.
Guo et al. [
Therefore, Leng [
In the absence of considering the real regional traffic state classification level, Xiao-feng Ji directly referred to the standard of the evaluation index system of the city traffic management to divide the regional traffic state into four grades. Then, he extracted the traffic flow characteristics to qualitatively analyze the regional traffic state. This part of the research is not rigorous.
Considering the necessity for the principle of clustering algorithms to set the number of categories and, also, considering the principle of the need to set the number of categories, Guo directly divided the regional traffic state into three levels according to the “flow-density” diagram.
To deal with this problem, He introduced Silhouette average width to determine the optimal clustering number of the sample. However, the research aims at the spatial distribution of regional traffic congestion problem.
Zhang et al. [
Daganzo [
Hence, this paper, on the basis of the existing research, from a macro point of view, introduces the global efficiency as the evaluation parameter. Then, it uses the complex network clustering algorithm for quantitative analysis of the reasonable regional traffic condition level. Finally, it puts forward a quantitative method to solve the problem for the regional traffic state discrimination.
The main contribution of this paper is to propose a method for confirming the level of dividing regional traffic state, as the basis to identify regional traffic state. So, this paper uses Complex Network Theory to research regional traffic state from the perspective of quantitative analysis. A rough discriminant standard for regional traffic status will finally be obtained, which is realized to generally determine the regional traffic state.
Due to the complex structure in regional road network, the research should take the connection relationships between the roads into consideration. Hence, abstract the urban regional transportation network into a complex network. Then, use CNM (Clauset-Newman-Moore) Community Division Method to deeply explore regional traffic status information from a data set composed of traffic parameters named global efficiency, and dynamically divide the regional traffic status into several grades. On this basis of the above, build the corresponding relation between the global efficiency and the parameters of determining current urban trunk state discriminant standard. Finally, according to the function transformation, this paper roughly makes sure of the state discriminant threshold of regional road network under various traffic states and obtains a general standard which will quantitatively reflect the regional network traffic state situation.
After this introduction, the remainder of the paper is organized as follows: Section
The research of this article mostly uses the Complex Network Theory. The reasons why this article adopts the Complex Network Method lie in three aspects: From the topology structure level of traffic network, complex network abstracted by urban traffic network conforms with scale-free network but also shows the features of small-world complex network [ From the dynamics behavior level of traffic network, it is found that the cause of the wide range of traffic congestion is a threshold in either point in scale-free networks, and edge congestion or network cascading failure. If it exceeds the threshold, the traffic network will face larger areas of congestion. From evaluation characteristics of traffic network, urban traffic network has self-similarity structure. Its fractal dimension is not invariable and has a reciprocal relationship with the time value.
Based on the above, this article constructs a complex network to analyze the complex regional traffic status and applies the Complex Network Theory to divide the regional traffic status.
For the study of complex urban regional traffic status, this research will not only consider the static network topology character of the urban area network but also analyze the features of dynamic time-varying embodied traffic flow operating condition from the network. Therefore, this article will firstly abstract urban regional traffic network into a complex network. Then, it will research the essence of the regional road network traffic flow operating state from the angle of network topology.
This article uses the Primal Approach Method [
The research of the complex network edge-weight roughly has been divided into two categories: fixed edge-weight model and dynamic-weight model [
In addition, some scholars [
In view of the two edge-weights calculation forms, put traffic flow data of an arterial road in Changchun to analyze the sensitivity shown by two edge-weights calculation equations. So, the contrastive analysis between BPR and V/C is seen in Figure
Contrastive analysis between BPR and V/C.
In Figure
However, the road traffic flow volume
Therefore,
After solving the problems of determining edge-weight value in complex network, the network information transmission efficiency needs a parameter to be quantitatively evaluated. In directed and weighted network, global efficiency is an evaluation parameter for the network information transmission efficiency, and its numerical size reflects the complex network connecting degree, which means depicting the specific situation of the regional traffic state. Therefore, this article introduces the global efficiency to evaluate the regional road network traffic flow operating state. And literature [
The global efficiency is a measure for reflecting network operation ability, and it can judge the efficiency of the whole network information transmission, namely, the operating efficiency of traffic flow within regional road network [
In undirected complex network, the distance
For the weighted network, there are two kinds of edge-weights—dissimilar-weight and similar-weight. Normally, the most popular aspect is the dissimilar-weight relating to distance: the larger weight value means the further distance between two nodes. Nevertheless, it is significantly different from the similar-weight: the larger weight value indicates the closer distance between nodes (higher density among nodes) [
The distance
Therefore, based on the complex network environment built in this paper, the equation for calculating the shortest path is as follows:
At this time, the efficiency
In the weighted network, the global efficiency
From (
Based on the recent research, in order to quantitatively analyze the regional traffic state, firstly study the regional traffic state classification method for determining a rough regional traffic state identification standard.
Literature [
At present, Community Partition Algorithm is roughly divided into two categories: graphics segmentation algorithms in computer science and hierarchical clustering algorithm in sociology [
The calculation of modularity in undirected network module is as follows:
The CNM algorithm directly constructs a modularity incremental matrix
In the face of massive traffic information from the regional road network, the quantitative method of analyzing regional traffic state should have a high operation rate and large storage space. The CNM algorithm constructs a modularity incremental matrix, with saving the computing storage space and also improving the operation rate. Considering the advantage of the CNM algorithm, the method this paper proposed is able to improve the operating speed of regional traffic state identification.
Flow chart of quantitative method.
Through the collected information of regional traffic flow parameters in multiple time scales, calculate the global efficiency of regional road network under the whole time and construct the initial sample set of global efficiency
According to the following equations, calculate initial samples of Euclidean distance matrix
Due to the built complex network as an undirected network, it can be ratiocinated that
Based on the constructed Euclidean distance matrix
Assuming that the complex network
In that way, the initial elements in modularity incremental matrix
The maximum heap contains the largest element
After Step
According to the value of the maximum heap
After the communities merging, update modularity incremental matrix
(1) The updating rules of
(2) The updating rules of
(3) The updating rules of
In the process of merging communities, if the updated largest element of network modularity incremental matrix changes from positive to negative, stop this merger. The division of the community structure is optimal.
The differences between the divided communities demonstrate the differences among regional traffic status. If the number of divided communities is more, regional traffic status changes more; when the number of communities is small, the state of regional traffic is stabilized in this case. But when the number of the communities is only one, it is shown that there are two kinds of traffic condition in this regional network: firstly, the overall regional network is unblocked, namely, any roads in a state of free-flow area. Secondly, the overall regional network is blocked, namely, all roads in the crowded state.
Therefore, in determining the quantitative standard of regional traffic status, all-time traffic data will be chosen as this research object.
According to the division principle in Section
Process of determination method of the rough regional traffic congestion standard.
This article utilizes a regional network in Changchun as an example for confirming the proposed method feasibility, where this network has obvious regional traffic congestion characteristic. There are 14 crossings and 21 links in this traffic network, as shown in Figure
Schematic diagram of example area.
Complex network based on global efficiency data set.
Correlogram of nodes in complex network.
In Figure
In addition, through the analysis of Figure
Dividing result with one-day data in regional traffic network.
In Figure
Model summary and parameter estimates.
Equation | Model summary | Parameter estimates | |||||||
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df1 | df2 | Sig. | Constant |
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|
|
Linear | 0.9139 | 5064.8053 | 1.0000 | 477.0000 | 0.0000 | −0.0452 | 0.0029 | ||
Logarithmic | 0.8494 | 2689.9098 | 1.0000 | 477.0000 | 0.0000 | −0.2259 | 0.0793 | ||
Quadratic | 0.9722 | 8324.9882 | 2.0000 | 476.0000 | 0.0000 | 0.0559 | −0.0042 | 0.0001 | |
Cubic | 0.9705 | 7827.1259 | 2.0000 | 476.0000 | 0.0000 | 0.0249 | −0.0008 | 0.0000 |
|
Compound | 0.9461 | 8369.1022 | 1.0000 | 477.0000 | 0.0000 | 0.0046 | 1.0751 | ||
Power | 0.9078 | 4697.8380 | 1.0000 | 477.0000 | 0.0000 | 0.0000 | 1.9898 | ||
S | 0.8394 | 2494.0357 | 1.0000 | 477.0000 | 0.0000 | −1.4498 | −51.3362 | ||
Growth | 0.9461 | 8369.1022 | 1.0000 | 477.0000 | 0.0000 | −5.3797 | 0.0725 | ||
Exponential | 0.9461 | 8369.1022 | 1.0000 | 477.0000 | 0.0000 | 0.0046 | 0.0725 | ||
Logistic | 0.9461 | 8369.1022 | 1.0000 | 477.0000 | 0.0000 | 216.9557 | 0.9301 |
The independent variable is 39.480000.
Regression analysis of evaluation parameters.
In Figure
As seen in Figure
According to the relationship of quantitative parameters, convert the level of the evaluation index system of the city traffic management into regional transportation discriminant criteria, as shown in Table
Status identification standard.
First level | Second level | Third level | Forth level | |
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Average travel speed |
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Global efficiency |
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Therefore, the traffic state of this example area is divided into four levels. Get real-time traffic parameters to determine the global efficiency and then obtain the corresponding state level of regional traffic congestion, which will provide strong information support for traveler to travel and traffic managers to make the congestion control scheme.
According to the example in Section
Based on the global efficiency data set, this regional traffic status is also divided into 4 levels by using
Function relationship of evaluation parameters.
Use the SOM Algorithm, the
The comparison results of three methods.
Proposed method | SOM Algorithm |
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Calculation time (s) | 8.510 | 18.193 | 10.830 |
Because of the traffic flow data related to the area scope, the Neural Network Algorithm spends too much time in building a network. So, it has the longest time. The clustering algorithms needs to firstly calculate the clustering centers. But, the proposed algorithm directly picks up the parameters used in the operation and stores them in the form of the maximum heap. Therefore, it takes the shortest time to operate. Table
Based on the lack of the present quantitative research of urban regional traffic status and the demand of the urban transportation system, this paper studies the intrinsic characteristics of regional traffic flow parameters in the network environment. From the perspective of complex network topology, this article abstracts the urban regional network into a directed and weighted complex network and regards it as study foundation. It introduces the global efficiency as quantitative parameter of regional road network traffic state. With complex network community division theory, traffic state level in the region has been obtained. Then, build the corresponding relationship between the global efficiency and the average travel speed of arterial road in regional network, which further determines the boundary value of each class of discriminant standard. Thus, this article can provide the final regional traffic state discriminant criteria.
The authors declare that they have no conflicts of interest.
This research is funded by the National Natural Science Foundation of China (no. 51308249) and Shandong Province Tube Enterprises Technological Innovation Projects (no. 20122150251-1).