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  • Research Article Speed-Density Model of Interrupted Traffic Flow Based on Coil Data

    Chen Yu,1 Jiajie Zhang,1 Dezhong Yao,1 Ruiguo Zhang,2 and Hai Jin1

    1Services Computing Technology and System Lab, Big Data Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China 2Siemens Ltd., ChinaCorporate Technology,Wireless Technology andWeb of SystemWuhan InnovationCenter,Wuhan 430074, China

    Correspondence should be addressed to Chen Yu; yuchen@hust.edu.cn

    Received 2 September 2016; Accepted 13 November 2016

    Academic Editor: Beniamino Di Martino

    Copyright Β© 2016 Chen Yu 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.

    As a fundamental traffic diagram, the speed-density relationship can provide a solid foundation for traffic flow analysis and efficient traffic management. Because of the change in modern travel modes, the dramatic increase in the number of vehicles and traffic density, and the impact of traffic signals and other factors, vehicles change velocity frequently, which means that a speed-density model based on uninterrupted traffic flow is not suitable for interrupted traffic flow. Based on the coil data of urban roads in Wuhan, China, a new method which can accurately describe the speed-density relation of interrupted traffic flow is proposed for speed fluctuation characteristics. The model of upper and lower bounds of critical values obtained by fitting the data of the coils on urban roads can accurately and intuitively describe the state of urban road traffic, and the physical meaning of each parameter plays an important role in the prediction and analysis of such traffic.

    1. Introduction

    Flow, speed, and density are known as the basic elements of traffic flow theory. Flow can measure the number of vehicles and the demand for traffic infrastructure. Speed is an impor- tant control index in road planning, and it is also an evalua- tion index of vehicle operation efficiency. Density reflects the intensity of the vehicles on the road and determines traffic management and controlmeasures.The relationships between flow, speed, and density called fundamental diagrams play a very important role in traffic flow theory and traffic engineering. For example, the speed-flow relationship can be used in highway capacity analysis in order to determine the highway service quality, and the speed-density relationship can reflect dynamic change in traffic flow, which can be used to study the disturbance propagation between vehicles.There- fore, sound mathematical models provide a solid foundation for traffic flow analysis and efficient traffic management. The relationship between speed and density which can reflect the quality of service received from the road is attracting considerable research attention.

    The earliest speed-density model was a linear model pro- posed byGreenshields et al. [1] in 1935.The linearmodel over- laps and classifies the observed data groups, which is proved to be unreasonable, and observation time is a holiday, with a narrow range of representations, so there are some deviations between the derived speed-density relation and the actual situation. Later, the relationship between speed and density was studied in greater depth, and the Greenberg logarithmic model, Edie model, Underwood exponent model, Pipes- Munjal model, modified Greenshields model, Newell model, and so forth, emerged in turn [2, 3]. Heydecker and Addison [4] studied the relationship between speed and density under various speed limits and found that zero speed induces traffic jams, not the other way around.Ma et al. [5] derived a general logistic model of traffic flow characteristics, which includes several traffic flow parameters with clear physical meanings and analyzed the effects of the parameters on speed-density logistic curves. The experimental results showed that this model can well describe the traffic flow characteristics in dif- ferent states. Shao et al. [6] proposed a speed-density model

    Hindawi Publishing Corporation Mobile Information Systems Volume 2016, Article ID 7968108, 12 pages http://dx.doi.org/10.1155/2016/7968108

  • 2 Mobile Information Systems

    under congested traffic conditions combined with the mini- mum safety spacing constraint, and the experimental results showed that the absolute error of this model was smaller than that of other models fitting the traffic data of two freeways. Wang et al. [7] proposed a family of speed-density models with different numbers of parameters with important physical significance and got good performance in the final experiment.

    All of the above studies are based on continuous traffic flow data. These data, also called uninterrupted traffic flow, are traffic flow with no effect of external fixation factors, such as freeway, urban expressway, and so forth. Discon- tinuous traffic flow, referred to as interrupted traffic flow, is periodically influenced by external fixation factors. The most common interrupted traffic flow is originated by signal lamps of urban intersections. Because of the variety of vehicle types, the periodic effect of signal lamps, shunts in the canal section, and other factors, the characteristics of interrupted traffic flow are very complex compared with uninterrupted traffic flow. In addition, the city is still in a rapid increase in popula- tion and, with the development of economy, people are more inclined to self-driving travel, thus more and more vehicles and more and more congestion in the city, which leads to the increase of travel time, the growth of fuel consumption [8], the aggravation of environmental pollution, and other awful issues [9, 10]. Compared with the highway, the urban road has a strong influence on the individual, society, and the environment. Therefore, further study of the characteristics of interrupted traffic flow to provide support formanagement decisions is particularly important.

    Research on interrupted traffic flow has attracted a lot of attention [11–15]. Many scholars see traffic flow located at a certain distance from the intersection as continuous traffic flow, believing that it can be described by continuous traffic flowmodels. Some of the literature [16, 17] suggests, however, that because of the short distance between intersections in the city and the influence of signal lamps, there are differences between traffic flow located at a certain distance from the intersection and the traffic flow of freeways. Because traffic data are difficult to obtain and for other objective reasons, only a few scholars focus on the speed-density model of discontinuous traffic flow. Wang et al. [18] introduced a four- parameter logit model for complete data fitting and estab- lished a speed-density logit model for left-turning, straight, and right-turning trafficflow.However, the experimental data were obtained by VISSIM simulation, and the simulation parameters were not accurate enough to depict the complex city road environment, so the experimental results have certain limitations.Wang et al. [19] thought that the stochastic model would contain more traffic information and put forward the stochastic speed-density model. This stochastic model can generate a probabilistic traffic flowmodel and can achieve real-time traffic prediction.

    In order to provide favorable data analysis and presen- tation for city traffic, thus to provide decision support for intelligent transportation, characterizing the speed-density relationship of interrupted traffic flow more accurately is full of importance. By analyzing a large amount of data, we pro- pose a description method for a speed-density relationship

    model which is suitable for discontinuous traffic flow, using the upper and lower curves to describe the upper and lower bounds of velocity values. Because of the discrepant charac- teristics of the traffic flow in the outer and inner lanes, the coil data of the outer and inner lanes are analyzed and verified.

    2. Speed-Density Model

    Three basic parameters (flow π‘ž, speed 𝑒, and density π‘˜) are the core content of the traffic flow model. The three have the following relationship:

    π‘ž = π‘˜ Γ— 𝑒; (1) that is, flow is the product of density and speed. The relationship between two parameters of the three is of great significance in traffic flow, and the relationship between speed and density has received a lot of research attention. Greenshields et al. was an early researcher, who proposed the speed-density linear relationship [1]:

    𝑒 = 𝑒𝑓 Γ— (1 βˆ’ π‘˜π‘˜π‘—) , (2) where 𝑒𝑓 is the speed of free flow, that is, the speed of vehicles unimpeded when the traffic density tends to zero, and π‘˜π‘— is the density of block flow, that is, the density when the traffic flow is blocked and cannot move. As shown in Figure 1, whenπ‘˜ = 0, the speed can reach the theoretical maximum value, namely, the free flow velocity 𝑒𝑓. The area surrounded by the abscissa, the ordinate of any point on the line, and the coordi- nate origin is the traffic flow.

    Equation (2) can change to

    π‘˜ = π‘˜π‘— Γ— (1 βˆ’ 𝑒𝑒𝑓) . (3) Respectively, introduce (2) and (3) into (1), and we get

    π‘ž = 𝑒𝑓 Γ— (π‘˜ βˆ’ π‘˜2π‘˜π‘—) , π‘ž = π‘˜π‘— Γ— (𝑒 βˆ’ 𝑒2𝑒𝑓) .

    (4)

    Equations (4) illustrate that π‘ž-π‘˜ and π‘ž-𝑒 are quadratic function relations, as shown in Figure 1.

    The linear model is too simple, and there are many deficiencies. In order to improve the model, scholars have proposedmodels based on the lin