Elevator Traffic Flow Model Based On Dynamic Passenger Distribution

Accurate traffic flow prediction is crucial for improving transportation efficiency. To improve the accuracy of traffic flow prediction, we developed a traffic flow prediction framework—namely, traffic flow multicomponent network—that appropriately processes the noise, volatility, and nonlinearity in traffic flow data. This framework comprises three components: a factor selection component, traffic flow decomposition component, and traffic flow prediction component. The factor selection component considers the dynamic effects of weather‐related, environmental, and spatiotemporal factors on traffic flow; it then extracts and analyzes factors exhibiting strong correlations with traffic flow. The traffic flow decomposition component optimizes the parameters of variational mode decomposition on the basis of the envelope entropy by using the sparrow search algorithm; it then transforms traffic flow into multiple intrinsic mode functions to enable accurate traffic flow prediction. Finally, the traffic flow prediction component constructs dynamic feature matrices by using a bidirectional gated recurrent unit model to identify relationships within the data. Moreover, it uses an attention mechanism to assign different weights to different features on the basis of the importance of these features to traffic flow prediction, thereby enabling the efficient processing of a large volume of data. The performance of the proposed framework was examined in experiments conducted on large volumes of traffic flow data with different time granularities. The results indicated that the proposed framework achieved high prediction accuracy and stability for various time granularities, data samples, dataset sizes, and noise conditions. Moreover, it generally outperformed existing traffic flow prediction models under all experimental conditions.

Accurate short‐term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data‐driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy‐to‐implement yet effective boosting model based on extreme gradient boosting and enhance it by wavelet denoising for short‐term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance.

The state of the art of macroscopic vehicle traffic flow models is discussed and the traffic flow stability of these models is analyzed. A nonlinear traffic flow stability criterion is investigated using the wavefront expansion technique. Qualitative relationships between traffic flow stability and model parameters are derived for an entire class of second-order macroscopic traffic flow models. Numerical results axe obtained using the CLAWPACK package for the well-known Payne-Whitham model, in order to illustrate the stability criterion. The newly derived stability results are consistent with previous reported results obtained using both microscopic models and approximate linearization methods. Moreover, the stability criteria derived in the paper can provide more refined information regarding the propagation of traffic flow perturbations and shock waves in second-order models than previously established methodologies.

Unmanned Aerial Vehicles (UAVs) are opening up new opportunities for extensive applications. The traffic flow model is critical to evaluate the traffic needs of various applications in designing and deploying UAV system. However, the traffic flow model has not been explored in multi-services oriented UAV system. To this end, this paper proposes a general traffic flow model for multi-services orientated UAV system. Under such a model, the network services are first categorized into three subsets, each corresponding to one of telemetry, Internet of Things (IoT), and streaming data. According to the Pareto distribution, all UAVs are further partitioned into three subgroups relying on their network usages. We can measure the packet arrival rate for the nine segments, each of which represents one map relationship between a services subset and a UAV subgroup. Therefore, we can also obtain the number of packets for each network service and total data size. Simulation results are presented to illustrate that the number of packets and the data size predicted by our traffic model can well match with these in real scenarios with different network services.

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated.

Introduction. Problematic sections of the road network are intersections with high traffic intensity of conflicting pedestrian and traffic flow, in particular, right and left turns. At such crossings, there are significant traffic delays. In the case of vehicles turning left or right at an intersection when passing at a traffic light signal that allows traffic, should give way to pedestrians crossing the roadway on the crosswalk. In the case of high intensity of pedestrian traffic, it is impossible to complete the turn of such vehicles, they remain at the intersection, so there are significant traffic delays for returning vehicles.Purpose. Therefore, the work aims to study the interaction of traffic and pedestrian flows with high intensity at regulated intersections to improve their capacity.Methods. To study the parameters of traffic flows, in particular, the length of congestion, we used traffic flow models, which include a psychophysiological model of following a vehicle moving in front, and a model based on traffic rules. To model, pedestrian flows the Wiedemann model was used. The leastsquares method was used to obtain empirical nonlinear dependences of congestion length on the intensities of conflicting pedestrian and traffic flows.Results. The paper uses known methods for solving applied problems in the field of traffic for some unique cases, namely the intersection of intensive -traffic and pedestrian flows. Studies of road and pedestrian traffic parameters at crossings have shown that the length of congestion significantly depends on the intensity of traffic flow, but with increas-ing intensity of pedestrian traffic more than 1000 people/h, the length of congestion changes insignificantly. If there are trams at the intersection, the length of congestion increases by an average of 1.14% and depends significantly on the intensity of traffic and pedestrian flows.It is proposed to introduce three-phase traffic light regulation on such sections when one of the phases allows traffic only for pedestrians. The simulation results showed that the length of congestion in the absence of rail vehicles on the approaches to the intersection decreases by an average of 45.3% and is weakly dependent on the intensity of pedestrian flows. If there are rail vehicles at the crossing, the length of congestion can reduce by an average of 13.7%. But in some cases may increase.Conclusion. The research methodology presented in this paper can be used for intersections with another traffic organization scheme to justify the feasibility of introducing threephase traffic light control when one of the phases allows traffic only to pedestrians.

Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham- Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for some decision variable. We use this fact to pose the problem of detecting spoofing cyber-attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber-attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California. © 2013 IEEE.

A variety of macroscopic traffic flow models have been applied to formulate the link-based system optimal dynamic traffic assignment (SO-DTA) problem in a many-to-one network. It is expected that SO-DTA models based on various traffic flow models usually result in different optimal link traffic evolution patterns, but whether and how traffic flow models affect the minimal system cost and the corresponding optimal arrival pattern at the destination is unclear. Three traffic flow models–point-queue, spatial-queue, and cell-transmission–are examined, and their resulted minimal system cost in many-to-one networks is compared. It is proved that for three simple networks–a linear network with two sequential bottlenecks, a two-in-one-out merge network, and a one-in-two-out diverge network–the minimal system costs for the SO-DTA models based on these three traffic flow models are identical. Numerical experiments show that this property appears to be held for general many-to-one networks. The reason may be that all three traffic flow models propagate the uncongested flows at the same speed and release the congested flows always at the link capacity. In SO-DTA applications where only the minimal system cost or arrival pattern at the destination is of major interest, the findings can be utilized to substantially expedite the solution procedure.

With rapid economic growth and urbanization, the accelerated increase in car ownership has brought massive pressure on urban traffic, and accurate traffic flow prediction information can provide an important basis for urban traffic dynamic planning. The existing methods have problems such as low efficiency, large error, and inability to adapt to short‐term traffic changes. To solve the above problems, the CEEMDAN‐SE‐GWO‐LSTM method was proposed in this paper. First, the traffic flow data is processed for outliers and missing values. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the traffic flow data, and sample entropy (SE) is used to reconstruct the subsequence, which is used to improve the quality of the input data. Then, the Grey Wolf Optimizer (GWO) is used to optimize the parameters of the long‐short‐term memory (LSTM) in order to improve the prediction accuracy and prevent the model from falling into a local optimum. Three models are used to compare with the ensemble model proposed in this paper, including back propagation neural network (BPNN), LSTM, and long‐short‐term memory optimized by Grey Wolf Optimizer (GWO‐LSTM). Root mean square error (RMSE) is reduced by 40.9% to 66.7%; R2 score is improved by 1.5% to 7.1%. The experimental results show that CEEMDAN‐SE‐GWO‐LSTM has a higher prediction accuracy than the existing traffic flow prediction models. Finally, this paper uses the model prediction error to establish an interval prediction model based on the kernel density estimation theory, which enhances the generalization of the model and the practical application value.

Traffic flow forecasting is an important part of intelligent transportation systems. Accurate traffic flow forecasting can not only provide travel advice for people, but also improve traffic management efficiency. In this paper, we propose a novel traffic flow prediction model based on sparse regression, which can effectively and conveniently combine spatio-temporal factor. We analyze the traffic flow data, and design a multi-factor dictionary with spatio-temporal factor. Temporal factor and spatial factor are added to the multi-factor dictionary in the form of basis vectors which make the model easy to expand. The multi-factor coefficient solution based on LASSO guarantees the sparsity of the coefficients, which can quantitatively analyze the influence of spatio-temporal factor. Experimental results in urban and suburban scenarios indicate that the prediction accuracy is improved after adding the temporal factor and spatial factor, and traffic flow on urban roads is more susceptible to spatial factors. We compared our method with other models in three different prediction intervals, and the experimental results show that our method achieved the best prediction performance.

Hopf bifurcation control for the traffic flow model considering the tail light effect

The short-term traffic flow prediction and modeling of highways are the core content and important foundation of highway management decision-making support systems. It is of great significance to improving the level of highway management. Based on the macrodynamic traffic flow model, this article establishes a method for establishing a highway traffic flow prediction model based on the BP neural network theory and gray theory. We collected and carry out modeling and prediction of highway traffic flow data near a certain station. It is learned from the prediction results that the traffic flow prediction model based on the BP neural network and gray theory has a high degree of reliability.

A Single traffic flow prediction method has weak applicability for short-time traffic flow prediction. In order to adapt to the needs for traffic guidance and signal control, a short-time traffic flow RBF neural network model combination prediction method based on wavelet de-nosing processing is put forward. First, the traffic flow data are decomposed and reconstructed by using the wavelet transform technique. Then Under the intensive analysis the characteristics of short-time traffic flow, the low frequency outline signal and high frequency detail signal are fitted respectively by using two different RBF neural network models, and particle swarm optimization (PSO) algorithm is proposed to train RBF neural network. The confirmation analysis is carried on with traffic flow data from typical roads in some city urban districts. The results show that the precision of combination prediction method is significantly improved.

Elementary cellular automata (ECA) rule 184 can be used as mathematical models of traffic flows. The slow-to-start model is obtained from the ECA rule 184 model by taking time lag for restart into consideration and is represented by 3-state 3-neighbor cellular automata (CA). In the present paper, we propose a traffic model where vehicles following the slow-to-start rule and those not following the slow-to-start rule are mixed. This model, called the mixed slow-to-start model, is represented by 4-state 3-neighbor CA. Further, we introduce the vector representation of CA with the slow-to-start rule and with the mixed slow-to-start rule, and then get their corresponding fuzzy CA. These fuzzy CA provide continuous-valued traffic models with the slow-to-start effect. Comparing the fundamental diagrams of the the slow-to-start model, the mixed slow-to-start model, and their fuzzy counterparts, we investigate the influence of the density and the mixing ratio of vehicles on traffic jams.

Limitations of current traffic models and strategies to address them