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Macroscopic model for evaluating traffic conditions on the expressway based on speed-specific VKT distributions
Abstract
With the continuous expansion of urban land, expressways have become key factors that support the entire urban road traffic operation. Accurate evaluation of traffic condition is the key of traffic management. This paper introduces the macroscopic fundamental diagram model and develops macroscopic traffic condition index (MTCI) based on speed-specific vehicle kilometers traveled (VKT) distributions by taking western 3rd ring-road expressway as an example. First, a macroscopic fundamental diagram model is established based on the RTMS data. Macroscopic traffic conditions on the expressway are classified into five levels (free-flow, near-free-flow, light congestion, moderate congestion, and severe congestion) based on the macroscopic fundamental diagram model. Subsequently, the speed spatial distribution model is designed using the floating car data. Furthermore, associating macroscopic traffic conditions with the cumulative distribution of travel speeds on the expressway, this study develops the MTCI evaluation method based on speedspecific VKT distributions. Finally, the proposed model is tested and verified using the western 3rd ring-road expressways.
... However, there are only three traffic conditions. Yue (2014) et al. [16] established the actual road network's MFD model with the help of traffic data of remote traffic detectors and designed the macrotraffic state index of expressway based on speed mileage distribution and divided the traffic state of expressway into five grades, including smooth, basic smooth, mild congestion, moderate congestion, and congestion. However, this method only discussed and verified the feasibility of expressway traffic state discrimination, and it did not discuss whether it was suitable for the traffic state division of the whole road network. ...
... However, both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support [13,17] or that traffic status could not be subdivided [14,15] or that the application scope of the method is limited [16]. If data mining methods and MFD can be combined, the accuracy of road network traffic state identification will be greatly improved. ...
Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.
... Currently, the research on trafc state identifcation mainly focuses on the above four categories of trafc states. Yue et al. [3] classifed trafc states into fve levels (free fow, near free fow, light congestion, moderate congestion, and severe congestion) based on the vehicle kilometers traveled (VKT) for a specifc travel speed. However, these fve levels still need to portray the characteristics of diferent trafc state subcategories fully and still belong to larger-grained trafc state categories. ...
Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.
In order to identify the real-time traffic state of the regional road network effectively, this paper proposes a method to determine the thresholds of the selected index corresponding to different traffic states, which combines the macroscopic fundamental diagram (MFD) of the road network with the K-means clustering. Firstly, in order to facilitate real-time control of regional traffic, the number of accumulated vehicles inside the road network is selected as the discriminant index, and the cluster K value is determined according to the data characteristics of MFD and related specifications. Then select the K-means algorithm to cluster the MFD scatter plots to get the range of indicators corresponding to each traffic state. The case study shows that the method can quickly identify the macroscopic traffic state of the road network according to the number of internal vehicles in the road network. Moreover, the difference between the threshold of the indicator obtained by clustering the MFD and the threshold obtained according to the speed of the road network is within 10 veh, which proves the effectiveness of the method. In addition, by comparing and analyzing the index thresholds identified under different control methods, the application value of this method in the evaluation of traffic measures is illustrated.
Observations of traffic pairs of flow vs. density or occupancy for individual locations in freeways or arterials are usually scattered about an underlying curve. Recent observations from empirical data in arterial networks showed that in some cases by aggregating the highly scattered plots of flow vs. density from individual loop detectors, the scatter almost disappears and well-defined macroscopic relations exist between space-mean network flow and network density. Despite these findings for the existence of well-defined relations with low scatter, these curves should not be universal. In this paper we investigate if well-defined macroscopic relations exist for freeway network systems, by analyzing real data from Minnesota's freeways. We show that freeway network systems not only have curves with high scatter, but they also exhibit hysteresis phenomena, where higher network flows are observed for the same average network density in the onset and lower in the offset of congestion. The mechanisms of traffic hysteresis phenomena at the network level are analyzed in this paper and they have dissimilarities to the causes of the hysteresis phenomena at the micro/meso level. The explanation of the phenomenon is dual. The first reason is that there are different spatial and temporal distributions of congestion for the same level of average density. Another reason is the synchronized occurrence of transitions from individual detectors during the offset of the peak period, with points remain beneath the equilibrium curve. Both the hysteresis phenomenon and its causes are consistently observed for different spatial aggregations of the network.
In order to develop a macroscopic flow model of urban network conditions suitable for the traffic control, the criteria for choosing macroscopic traffic variables are proposed, and then the macroscopic traffic variables are determined. The relationships among the traffic variables are derived by applying the two-fluid theory. Therefore, the macroscopic traffic condition model of urban networks is developed, and the calculation method of the optimal network capacity is presented based on this model. The model parameters are calibrated by the least squares method. The evolution process of traffic conditions is simulated by VISSIM and the relationships among variables determined by the calibrated model are compared with those from simulation. The results show that the macroscopic traffic conditions of urban networks can be accurately quantified by the calibrated model. The model parameters are easily estimated, so the proposed model can be applied in practice.
Macroscopic fundamental diagrams (MFDs) exist in large urban networks in which traffic conditions are homogenous. They can be used for estimation of the level of service on road networks, perimeter control, and macroscopic traffic modeling. However, before the MFD concept can be applied, the factors that influence the MFD shape should be identified and their effects investigated. A microscopic simulation model is used to change conditions, that is, to derive MFDs under different conditions and for different types of networks. Results indicate that a relationship indeed exists between production and accumulation for the whole network as well as for parts of the network focused on freeway or urban links. MFD shape is a property not only of the network itself but also of the applied traffic control measures. At the same time, congestion onset and resolution lead to heterogeneous traffic conditions with congestion at specific locations in the network, resulting in loops in congested parts of the MFD. Investigation of the effect of traffic demand on MFD also indicates that rapidly changing traffic demands drastically affect MFD shape.
This paper shows that a macroscopic fundamental diagram (MFD) relating average flow and average density must exist on any street with blocks of diverse widths and lengths, but no turns, even if all or some of the intersections are controlled by arbitrarily timed traffic signals. The timing patterns are assumed to be fixed in time. Exact analytical expressions in terms of a shortest path recipe are given, both, for the street’s capacity and its MFD. Approximate formulas that require little data are also given.For networks, the paper derives an upper bound for average flow conditional on average density, and then suggests conditions under which the bound should be tight; i.e., under which the bound is an approximate MFD. The MFD’s produced with this method for the central business districts of San Francisco (California) and Yokohama (Japan) are compared with those obtained experimentally in earlier publications.
A two-fluid model of town traffic has been developed by extending ideas formulated in an earlier kinetic theory of multilane
traffic. The-two fluids are taken to consist of moving cars and cars stopped as a result of traffic conditions. The average
speed of the moving vehicles is assumed to be proportional to the fraction of the vehicles that are moving raised to a power
that reflects the "goodness" of the traffic. It is then found that the trip time per unit distance is essentially linearly
related to the stop time per unit distance, in general accord with data obtained in many cities. Relations are developed on
this basis for flow, among other variables, versus average speed. These relations contain a new parameter that is identified
with the quality of the traffic network system.