Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management

Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.

Car-following models of vehicular traffic

The Car-following models is a kind of microscopic simulation model for vehicular traffic, which describe the one-by-one following behaviors of vehicles in the same traffic lane. As a common traffic phenomenon, following behavior is of great importance in the micro-study of intelligent traffic control. Compared with other traffic-flow models, car-following model embodies the human factors and reflects the real traffic situation in a better way. This paper gives a systematic review of the development and actuality of car-following models by introducing and analyzing in detail the advantages and disadvantages of GHR model, OV model, CA model and fuzzy-logic model. In addition, local stability and asymptotic stability of car-following models are discussed in this paper.

A novel conditional cell transmission model for oversaturated arterials

The objective of this work is to develop a novel feature for traffic flow models, when traffic queues on two-way arterials periodically extend until then they block an upstream signal in oversaturated conditions. The new model, proposed as conditional cell transmission model （CCTM） has been developed with two improvements. First, cell transmission model （CTM） is expanded for two-way arterials by taking account of all diverging and merging activities at intersections. Second, a conditional cell is added to simulate periodic spillback and blockages at an intersection. The results of experiments for a multilane, two-way, three-signal sample network demonstrate that CCTM can accommodate various traffic demands and accurate representation of blockages at intersections. The delay of left turns is underestimated by 40 % in moderate conditions and by 58% in oversamrated condition when using the CTM rather than CCTM. Finally, the consistency between HCS 2000 and CCTM shows that CCTM is a reliable methodology of modeling traffic flow in oversaturated condition.

Traffic flow prediction of urban road network based on LSTM-RF model

Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.

A New Macro Model for Traffic Flow on a Highway with Ramps and Numerical Tests

In this paper, we present a new macro model for traffic flow on a highway with ramps based on the existing models. We use the new model to study the effects of on-off-ramp on the main road traffic during the morning rush period and the evening rush period. Numerical tests show that, during the two rush periods, these effects are often different and related to the status of the main road traffic. If the main road traffic flow is uniform, then ramps always produce stop-and-go traffic when the main road density is between two critical values, and ramps have little effect on the main road traffic when the main road density is less than the smaller critical value or greater than the larger critical value. If a small perturbation appears on the main road, ramp may lead to stop-and-go traffic, or relieve or even eliminate the stop-and-go traffic, under different circumstances. These results are consistent with real traffic, which shows that the new model is reasonable.

Numerical analysis on car-following traffic flow models with delay time

Effects of the speed relaxation time on the optimal velocity car-following model (OVM) with delay time due to driver reaction time proposed by Bando et al.(1995) were studied by numerical methods. Results showed that the OVM including the delay is not physically sensitive to the speed relaxation times. A modified car-following model is proposed to overcome the deficiency. Analyses of the linear stability of the modified model were conducted. It is shown that coexisting flows appear if the initial homogeneous headway of the traffic flow is between critical values. In addition, phase transitions occur on varying the initially homogeneous headway.

Length-Based Vehicle Classification in Multi-lane Traffic Flow

For the realtime classification of moving vehicles in the multi-lane traffic video sequences, a length-based method is proposed. To extract the moving regions of interest, the difference image between the updated background and current frame is obtained by using background subtraction, and then an edge-based shadow removal algorithm is implemented. Moreover, a tbresholding segmentation method for the region detection of moving vehicle based on lo- cation search is developed. At the estimation stage, a registration line is set up in the detection area, then the vehicle length is estimated with the horizontal projection technique as soon as the vehicle leaves the registration line. Lastly, the vehicle is classified according to its length and the classification threshold. The proposed method is different from traditional methods that require complex camera calibrations. It calculates the pixel-based vehicle length by using uncalibrated traffic video sequences at lower computational cost. Furthermore, only one registration line is set up, which has high flexibility. Experimental results of three traffic video sequences show that the classification accuracies for the large and small vehicles are 97.1% and 96.7% respectively, which demonstrates the effectiveness of the proposed method.

Phase Transitions Induced by Top-Priority of Randomization

We study the characteristics of phase transition to take the top-priority of randomization in the rules of NaSch model （i.e. noise-first model） into account via computing the relaxation time and the order parameter. The scaling exponents of the relaxation time and the scaling relation of order parameter, respectively, are obtained.

Traffic Flow Model Based on Power Flow

The article is research on the traffic situations of freeways. Different rules make different traffic situations. It is meaningful to research on traffic situations under different conditions. The author analyzes factors like traffic flow and safety, peflbrmance respectively, and proposes the theor3, basis for making more reasonable rules. The author first establishes evaluation system of traffic safety. Then, we compare the freeway network to power network to find a solution. and establish a traffic flow model based on power flow （TFPF）. It calculates power flow. So it applies the formula mode back to the traffic network, chalking up the perforumnce of the traffic condition. We utilize cellular automata （CA） method to simulate traffic circulation, and verify the accuracy of above model with the obtained data.

Cellular Automaton Models of Highway Traffic Flow Considering Lane-Control and Speed-Control

As two kinds of management modes of highway tramc control, lane-control, and speed-control produce different effect under different conditions. In this paper, traffic flow cellular automaton models for four-lane highway system with two opposing directions under the above two modes are established considering car and truck mixed running. Through computer numerical simulating, the fundamental diagrams with different parameters are obtained, and after the analysis of density-flux diagrams, the variation discipline of flux with traffic density under different control models is gained. The results indicate that, compared with lane-control, utilization ratio of road can be further improved with speed-control when the truck number increases. The research result is of great significance for reasonable providing theoretical guidance for highway traffic control.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

A New Macro Model for Traffic Flow on a Highway with Bus Stop

Based on the difference between the online bus stop and the offline bus stop, two macro models are developed to describe the two types of bus stops. The numerical results show that the two models can qualitatively reproduce some complex phenomena resulted by the two types of bus stops and that the otttine bus stop is more effective than the online bus stop when the initial density is relatively low.

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

A relaxation scheme for a multi-class Lighthill-Whitham-Richards traffic flow model

We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-explicit Runge-Kutta method for time integration. The resulting method retains the simplicity of the relaxation schemes. There is no need to involve Riemann solvers and characteristic decomposition. Even the computation of the eigenvalues is not required. This makes the scheme particularly well suited for the MCLWR model in which the analytical expressions of the eigenvalues are difficult to obtain for more than four classes of road users. The numerical results illustrate the effectiveness of the presented method.

A Macro Model for Traffic Flow with Consideration of Static Bottleneck

In this paper, we develop a macro model for traffic flow with consideration of static bottleneck to explore the impacts of static bottleneck on traffic flow. The analytical and numerical results show that the proposed model can qualitatively describe the equilibrium flux, uniform flow and small perturbation under the action of a static bottleneck.

A new cellular automaton model for traffic flow considering realistic turn signal effect

In real traffic,any vehicle must give lane-changing signal(i.e.the turn signal) before changing lanes;at this time,the vehicles behind the lane-changing vehicle will be hindered and may form "plugs" due to the turn signal effect.However,few studies focus on exploring the effect.In this paper,the turn signal effect was taken into account by proposing a new symmetric two-lane cellular automaton(T-STCA) model,and the new model was set to compare with the STCA,H-STCA and A-STCA models.Numerical results show that using the T-STCA model to describe lane-changing or overtaking,the process appeared in several consecutive time steps;while using the other three models,the process appeared only in one time step.In addition,the T-STCA model could describe the mixed traffic flow more realistically and the turn signal effect could help the plugs to dissolve more quickly.

Impact of the Next-Nearest-Neighbor Interaction on Traffic Flow of Highway with Slopes

In this paper,we study the motion course of traffic flow on the slopes of a highway by applying a microscopic traffic model,which takes into account the next-nearest-neighbor interaction in an intelligent transportation system environment.Three common gradients of the highway,which are sag terrain,uphill terrain,and downhill terrain on a single-lane roadway,are selected to clarify the impact on the traffic flow by the next-nearest-neighbor interaction in relative velocity.We obtain the current-density relation for traffic flow on the sag,the uphill and the downhill under the next-nearest-neighbor interaction strategy.It is observed that the current saturates when the density is greater than a critical value and the current decreases when the density is greater than another critical value.When the density falls into the intermediate range between the two critical densities it is also found that the oscillatory jam,easily leads to traffic accidents,often appears in the downhill stage,and the next-nearest-neighbor interaction in relative velocity has a strong suppressing effect on this kind of dangerous congestion.A theoretical analysis is also presented to explain this important conclusion.

An Improved Discrete-Time Model for Heterogeneous High-Speed Train Traffic Flow

This paper aims to present a simulation model for heterogeneous high-speed train traffic flow based on an improved discrete-time model（IDTM）.In the proposed simulation model,four train control strategies,including departing strategy,traveling strategy,braking strategy,overtaking strategy,are well defined to optimize train movements.Based on the proposed simulation model,some characteristics of train traffic flow are investigated.Numerical results indicate that the departure time intervals,the station dwell time,the section length,and the ratio of fast trains have different influence on traffic capacity and train average velocity.The results can provide some theoretical support for the strategy making of railway departments.

An Enhanced Safe Distance Car-Following Model

Gipps' model, a well-known safe distance car-following model, has a very strict restriction on the car-following behavior that the following vehicle has to maintain the exact safe distance to the leading vehicle to avoid rear crash. However, this restriction is not consistent with the real traffic condition. Due to that, an enhanced safe distance car-following model is proposed first, and then calibrated and evaluated using the field data. Furthermore, the simulation is conducted to analyze the characteristics of the new model. The results of evaluation and simulation illustrate that the proposed model has higher simulation accuracy than the original Gipps' model, and can reproduce the stable flow and shock wave phenomena that are very common in real traffic.Moreover, the simulation results also prove that the enhanced model can better stabilize the traffic flow than Gipps' model.

A New Lattice Model of Two-Lane Trafc Flow with the Consideration of the Honk Efect

In this paper, a new lattice model of two-lane traffic flow with the honk effect term is proposed to study the influence of the honk effect on wide moving jams under lane changing. The linear stability condition on two-lane highway is obtained by applying the linear stability theory. The modified Korteweg-de Vries （KdV） equation near the critical point is derived and the coexisting curves resulted from the modified KdV equation can be described, which shows that the critical point, the coexisting curve and the neutral stability line decrease with increasing the honk effect coe^cient. A wide moving jam can be conceivably described approximately in the unstable region. Numerical simulation is performed to verify the analytic results. The results show that the honk effect could suppress effectively the congested traffic patterns about wide moving jam propagation in lattice model of two-lane traffic flow.