Real Traffic Flow Research Articles

Digital traffic state analysis for urban regions considering complex multi-directional flow changes

In this research, a digital analytical model based on three-flow full-cycle state macroscopic fundamental diagram (MFD) with observable different perimeter control management and flow loading modes has been proposed on the basis of real network and peak hour traffic flow loading scenarios in Rostov-on-Don. It is known that the aim of digital management of urban intelligent traffic is to ultimately optimize the overall state of multi-area traffic operation in the city. The goal of this research was to provide a strong basis to establish digital management strategies for intelligent transport, determine the change trends of steady-state equilibrium of regional traffic flow and their effects on full-cycle states of traffic flow under various peripheral control management strategies and traffic loading patterns. Simulation results confirmed that with the evolution of traffic state, stricter peripheral control management always results in a wider attraction area ultimately giving rise to steady-state equilibrium of traffic flow.

Exploring the Relationship between Data Sample Size and Traffic Flow Prediction Accuracy

Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

A Knee-Guided Evolutionary Algorithm for Multi-Objective Air Traffic Flow Management

Air traffic flow management plays a crucial role in efficient aviation. Most existing studies assume the flight speed as constant throughout the trip, leading to ineffective fixed-speed schedules. To address this issue, we propose a new problem model, which allows variable speed control to improve the flexibility and maneuverability of the management. In addition, we consider two conflicting objectives, which are minimizing the total flight delays and conflicts between flights, where the conflicts depend on the flight 4D trajectories (3D position plus time). To solve this new challenging problem, we propose a novel multi-objective evolutionary algorithm with new problem-specific individual representation and search operators. Specifically, the multi-chromosomes encoding scheme is designed to adapt to different types of operations. Then, to search the huge search space effectively, we develop a hybrid crossover operator that recombines the parents based on their flight routes. Furthermore, to balance the exploration and exploitation, we develop a new mutation strategy to utilize the heterogeneous search potential of different individuals. For exploitation, the knee individual in the Pareto front is improved by a new time shift operator for exploitation, and other non-dominated solutions are mutated by fixed-route mutation. For exploration, the dominated solutions are mutated randomly. To verify the effectiveness, we compare it with the real air traffic flow management schedules and the state-of-the-art algorithms on a range of real-world air traffic datasets. Extensive results show that the proposed algorithm can significantly outperform the baselines in generating safe and efficient 4D trajectories.

Traffic flow model considering the dynamics prediction of the leading vehicle

Traffic flow modeling has become increasingly important in today’s society due to the growing number of vehicles on the roads, leading to congestion and traffic incidents. Mathematical modeling can aid in the planning and optimization of transportation systems, enhancing overall efficiency and safety. The main result of this study is a mathematical model that describes the dynamics of multiple vehicles, taking into account the prediction of the leading vehicle’s behavior. The model is formulated as a system of delay differential equations. A computer program has been developed based on this model, which simulates traffic flow in various road scenarios. The simulation results are consistent with observed data from real traffic flows.

Travel Characteristics and Vulnerability Analysis of Road Resource Utilization Based on Taxi GPS Data

Residents’ travel and logistics are greatly affected by urban transportation networks, which are one of the most important supports for urban socio-economic activities. Urban transportation systems tend to cripple when faced with challenges such as natural hazards and social unrest. This paper proposes a framework for a vulnerability analysis of urban road networks (URNs) based on real traffic flows with GPS data. An improved K-shell critical node identification method is proposed based on structural and traffic characteristics. Then, a cascade failure model is proposed to analyze the structural and functional vulnerability of the URN by combining the load capacity model and the vulnerability model. This paper takes the Harbin main city URN as an example and first analyzes the passenger travel distribution and the relationship between travel orders, population and POI. Four deliberate attack methods are proposed to analyze the vulnerability of the URN under deliberate attack on commute days and rest days. The experimental results show that URNs exhibit intense vulnerability, with the fastest cascading failure occurring based on improved K-shell node failure. Furthermore, URNs are more vulnerable on rest days compared to commuter days. These findings could be used to inform a vulnerability-based spatiotemporal design of UBNs and provide theoretical support for managing traffic congestion on different days.

A hybrid machine learning approach for congestion prediction and warning

ABSTRACT Global traffic management encounters a significant challenge in traffic congestion. This paper presents a hybrid machine learning method for predicting traffic congestion. It leverages Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) network for parameter prediction and combines clustering and classification models, using the K-means algorithm to categorize traffic states into four levels and constructing a KNN classification model based on this segmentation. This results in the K-means-KNN model. Predicted parameters are inputted into the K-means-KNN model for congestion level prediction. Validation with real traffic flow data shows that the CNN-GRU network can capture spatiotemporal features more effectively. For instance, in traffic flow prediction, it reduces the MAPE by 7.39% and 51.14% compared to CNN and GRU, respectively. K-means-KNN excels in traffic state discrimination, achieving a congestion prediction accuracy of 91.8%. These results underscore the efficacy of the hybrid machine learning method in assessing and predicting urban traffic congestion.

Traffic Flow Prediction by an Ensemble Framework with Data Denoising and Functional Principal Components Analysis

Accurate traffic flow data are important for congestion identification and real-time traffic control. Raw traffic flow data may be disturbed by different noises during the acquisition process, which leads to the degradation of model prediction performance. To address this problems, this paper proposes a data denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet threshold denoising (WTD) to suppress potential outliers in the data, and then introduces functional principal component analysis to accomplish the traffic forecasting task. We use the proposed framework for real traffic flow prediction and validate the prediction performance of the proposed framework using root mean square error and mean absolute error. In the prediction performance comparison, five denoising methods are considered: empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN, and WTD. The prediction results show that the model combined with the denoising method outperforms the model without the denoising method. ICEEMDAN combined with WTD’s denoising method improves prediction performance more than other methods. Furthermore, the WTD method with dmey type achieves higher accuracy than other types.

Investigation the Stochastic behaviour of the Traffic Flow: A Case Study of a Section of a Road

The stochastic behavior is one of the key for the current state of vehicles flow for the real time traffic behavior. This paper describe the study to investigate the stochastic behavior of real time traffic flow for a section of road using probability distribution fit over the section of road, the traffic data was collected for a week from 7:00 to 19:00 at the location Nawabshah Pakistan. The different distribution such as Normal, Lognormal, Weibull, Gamma, Exponential distribution was fit using MATLAB distribution fit by probability plot of traffic flow data. The same distribution was used for the goodness-of-fit tests by considering Kolmogorov-Smirnov, Kolmogorov-Smirnov modified, Anderson-Darling were used with p-values at 95% of confidence level and justification to accept the hypothesis test are accepted or rejects. The hypothesis accept for Normal, Weibull and Gamma distribution which accept the all hypothesis test and among these three accepted fit distribution the Normal probability distribution fit is most fitted distribution using the rank by p-value of the hypothesis tests. Keywords: Traffic flow, Goodness-of-fit, Probability Distributions, Nawabshah

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.

FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time.

Failure recovery in the MANTRA architecture with an IPoWDM SONiC node and 400ZR/ZR+ pluggables

The utilization of IP over wavelength division multiplexing (IPoWDM) boxes in optical networks introduces coordination issues at the control plane level between the optical and packet domains. Indeed, IPoWDM boxes are technically packet devices, but the configuration of coherent optical pluggables requires knowledge of optical network state information (e.g., wavelength availability). MANTRA (metaverse ready architectures for open transport) aims to define a new SDN control architecture for managing IPoWDM, enhancing coordination between the packet and optical controllers. Within MANTRA, two distinct architectures were presented: Dual and Single. However, only the roles related to controller management are defined, while clear procedures are not specified for dealing with resource provisioning and failure recovery. This leads to the need for the definition and implementation of tailored procedures. Besides the characterization of the coherent optical pluggables performance, this paper presents and compares three procedures based on MANTRA to coordinate and control IPoWDM nodes in a multi-layer network performing failure recovery. The considered solutions have been designed and experimentally validated with a focus on the achievable traffic recovery performance considering IPoWDM configuration and communication within the SDN control architecture. An experimental testbed, comprising a sliced IPoWDM node running an extended version of the open-source SONiC network operating system, has been deployed to validate these solutions, with a comprehensive analysis of the time required to recover a real traffic flow that spans both the packet and optical domains.

Statistical risk quantification of two-directional internet traffic flows

We develop statistical methodology for the quantification of risk of source-destination pairs in an internet network. The methodology is developed within the framework of functional data analysis and copula modeling. It is summarized in the form of computational algorithms that use bidirectional source-destination packet counts as input. The usefulness of our approach is evaluated by an application to real internet traffic flows and via a simulation study.

Learning traffic as videos: A spatio-temporal VAE approach to periodic traffic raster data imputation

For modern Intelligent Transportation System (ITS), data missing during traffic raster acquisition can be inevitable because of the loop detector malfunction or signal interference. Nevertheless, missing data imputation is meaningful due to the periodic spatio-temporal characteristics and individual randomness of traffic raster data. In this paper, traffic raster data collected from all spatial regions at each time interval are considered as a multiple channel image. Accordingly, the traffic raster data over a period of time can be regarded as video, on which an unsupervised generative neural network called MSST-VAE (Multiple Streams Spatial Temporal-VAE) is proposed for traffic raster data imputation, and this model can even robustly performs at varied missing rates while many other approaches fail to conduct. Two major innovations can be summarized in MSSTVAE: Firstly, it uses multiple periodic streams of Variational Auto-Encoders (VAEs) with Sylvester Normalizing Flows (SNFs), which shows strong generalization ability. Secondly, after the traffic raster data are transferred into videos, an ECB (Extraction-and-Calibration Block) consisting of dilated P3D gated convolution and multi-horizon attention mechanism is employed to learn global-local-granularity spatial features and long-short-term temporal features. Extensive experiments on three real traffic flow datasets validate that MSST-VAE outperforms other classical traffic imputation models with the least imputation error.

The initial value problem of coupled Aw-Rascle traffic model with Chaplygin pressure

This paper studies the initial value problem of coupled Aw-Rascle (CAR) traffic model with Chaplygin pressure. A definition of weak solution to CAR model is developed to obtain the waves with phase transition. Combining the waves of Aw-Rascle traffic model, we construct the solutions to the Riemann problem of CAR model with Chaplygin pressure. Without imposing additional coupling conditions, we obtain the existence, uniqueness and stability of Riemann solutions. Then we study the interaction problem of the CAR model with Chaplygin pressure, which models traffic phenomenon with careless driving in a certain section of the highway. From the solutions to the interaction problem, we can observe the formation and disappearance of traffic jam. The mathematical mechanism of traffic jam is analyzed, and the result is in good agreement with real traffic flow.

A hybrid approach for fatigue assessment of reinforced concrete bridge deck considering realistic bridge-traffic interaction

Reinforced concrete (RC) bridge decks are directly exposed to daily traffic loads and often experience surface cracking caused by excessive stress or fatigue accumulation. The bridge deck fatigue performance over traffic loads needs to be assessed based on accurate dynamic interaction analysis of the bridge and realistic moving vehicles. Most of existing studies on fatigue assessment focus on either the bridge global response without sufficient details about the bridge deck, or refined bridge deck modeling without considering the full dynamic interaction between moving traffic and the bridge structure realistically. A hybrid fatigue assessment approach is developed by combining mode-based global bridge-traffic dynamic interaction analysis, finite-element (FE)-based refined bridge deck model, and fatigue assessment method directly based on the vehicle loads and shear strength of the bridge deck. The proposed approach is demonstrated with a typical 3-span concrete bridge under realistic traffic and road surface conditions. Based on the dynamic interaction and stress analysis results, the fatigue damage factor is further investigated with different road surface roughness levels and heavy truck proportions. It is found that the proposed analytical approach provides a useful tool to predict the bridge deck response and potential fatigue damage under realistic traffic flow.

DeTorrent: An Adversarial Padding-only Traffic Analysis Defense

While anonymity networks like Tor aim to protect the privacy of their users, they are vulnerable to traffic analysis attacks such as Website Fingerprinting (WF) and Flow Correlation (FC). Recent implementations of WF and FC attacks, such as Tik-Tok and DeepCoFFEA, have shown that the attacks can be effectively carried out, threatening user privacy. Consequently, there is a need for effective traffic analysis defense. There are a variety of existing defenses, but most are either ineffective, incur high latency and bandwidth overhead, or require additional infrastructure. As a result, we aim to design a traffic analysis defense that is efficient and highly resistant to both WF and FC attacks. We propose DeTorrent, which uses competing neural networks to generate and evaluate traffic analysis defenses that insert 'dummy' traffic into real traffic flows. DeTorrent operates with moderate overhead and without delaying traffic. In a closed-world WF setting, it reduces an attacker's accuracy by 61.5%, a reduction 10.5% better than the next-best padding-only defense. Against the state-of-the-art FC attacker, DeTorrent reduces the true positive rate for a .00001 false positive rate to about .12, which is less than half that of the next-best defense. We also demonstrate DeTorrent's practicality by deploying it alongside the Tor network and find that it maintains its performance when applied to live traffic.

Application of Autoformer to Short-Term Traffic Flow Prediction

This paper investigates the application of Autoformer model in the field of short-term traffic flow prediction. Traffic flow prediction is crucial for urban planning and traffic management, and is important for relieving traffic congestion and improving traffic efficiency. Autoformer, as a Transformer model based on the self-attention mechanism, provides a powerful modeling capability for the task of traffic flow prediction through its ability to automatically learn feature relationships. The paper first introduces the background and importance of traffic flow prediction and outlines current prediction methods and their limitations. Subsequently, the structure and working principle of Autoformer are described in detail, elucidating the advantages over traditional models. Autoformer’s self-attention mechanism can effectively capture the long-range dependencies in the input sequences and better adapt to the dynamic changes of traffic flow. To verify the performance of Autoformer in short-time traffic flow prediction, experiments are conducted using real traffic flow datasets. The results show that Autoformer significantly improves the prediction accuracy and generalization ability compared to traditional time series models. In addition, model interpretive analysis was conducted, revealing the advantages of Autoformer for automatic extraction of traffic flow features and correlation modeling.

Vulnerability analysis of public transit systems from the perspective of the traffic situation

The critical to sustainable urban development that a robust bus transit system is constructed to alleviate urban congestion problems. Unfortunately, disruptions in bus transit systems can lead to dysfunction and threaten sustainable development. This paper proposed a vulnerability analysis model for constructing complementary urban bus networks (UBNs), which considers the actual capacity of the bus network at the speed of transport roads. In addition, the UBNs aimed analysis method is presented from three specific aspects: (a) utilizing the stochastic user equilibrium model and different traffic situations to measure the UBNs of passenger flow distribution according to multi-source data; (b) introducing the node capacity and structural, functional vulnerability analysis model by combining real traffic flows and the complex network theory; (c) analysing the vulnerability characteristics of UBNs cascading failures from different passenger flow distributions and increasing capacity parameters, and comparing with those under different attack methods. Then, the new vulnerability analysis model is applied to the Harbin trunk bus. Considering different attack scenarios, the UBNs show strong vulnerability in the case of high congestion. Compared with other deliberate attacks, the bus network based on the attack mode of the maximum intensity site is more vulnerable at different times. These insights could be used to inform a vulnerability-based spatiotemporal design of bus transit networks.