Traffic Model Research Articles

Retrospective Cost Saving Comparison Between TNM 2.5 and TNM 3.2 Barrier Analysis Results

In December of 2021, the Federal Highway Administration (FHWA) Traffic Noise Model (TNM) version 3.1 was released to the public for use on traffic noise projects and subsequently updated to version 3.2 in December 2023. However, most or all state Departments of Transportation (DOT) continue to accept and prefer the use of TNM 2.5 (released in 2004). A presentation at NoiseCon 2023 (TNM 2.5 vs TNM 3.1 Noise Level Prediction Comparison by Mukul Pal and Michael Parsons) came to the conclusion that, generally, TNM 2.5 tends to overpredict traffic noise levels in comparison to the TNM 3.1, resulting in additional impacts and subsequent additional mitigation that may not be necessary when analyzed with the updated algorithms. In this paper, the authors determine potential cost savings by migrating from TNM 2.5 to TNM 3.2 by comparing results from previously approved traffic noise projects. By determining the potential cost savings of a migration, DOTs may be better informed about whether or not they should continue to utilize TNM 2.5 or make the transition to TNM 3.2.

Applying reflections in the FHWA Traffic Noise Model

The latest version of the Federal Highway Administration (FHWA) Traffic Noise Model (TNM), version 3.2, was released early 2024 and is encouraged for use to conduct highway noise impact and noise abatement analyses. TNM 3.2 has the advantage of allowing single order reflections in addition to a parallel barrier/wall analysis. This paper presents a process to apply single order reflections considering both their sound contribution and their efficiency in modeling and calculations. The process was applied to a real highway project to evaluate sound level increases due to single reflections and to compare predicted sound levels with and without reflections to measured sound levels. Reflecting surfaces evaluated include noise walls on one side of a highway, building facades facing the highway, and sides of buildings.

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.

An intellectual drivers' sensitivity traffic model for adapting taillight effects

An intellectual drivers' sensitivity traffic model for adapting taillight effects

Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks

The rapid expansion of large urban areas underscores the critical importance of road infrastructure. An accurate understanding of traffic flow on road networks is essential for enhancing civil services and reducing fuel consumption. However, traffic flow is influenced by a complex array of factors and perpetually changing conditions, making comprehensive prediction of road network behavior challenging. Recent research has leveraged deep learning techniques to identify and forecast traffic flow and road network conditions, enhancing prediction accuracy by extracting key features from diverse factors. In this study, we performed short-term traffic speed predictions for road networks using data from Mobileye sensors mounted on taxis in Daegu City, Republic of Korea. These sensors capture the road network flow environment and the driver’s intentions. Utilizing these data, we integrated convolutional neural networks (CNNs) with spatio-temporal graph convolutional networks (STGCNs). Our experimental results demonstrated that the combined STGCN and CNN model outperformed the standalone STGCN and CNN models. The findings of this study contribute to the advancement of short-term traffic speed prediction models, thereby improving road network flow management.

Non-local time evolution equation with singular integral and its application to traffic flow model

We consider an integro-differential equation model for traffic flow, which extends the Burgers equation model. To derive exact solutions to the equation, we first examine the features of integrable integro-differential equations and find that they are obtained through the residue formula from integrable equations in complex domains. Using this approach, we construct several new integrable equations with double singular integrals and elliptic singular integrals. Then, we discuss the traffic model and show that it exhibits interactions between free-flow and congested regions, as well as the deadlock phenomenon, depending on the non-locality parameter.

Method for utilizing the reserved lane capacity: Formation of the mixed traffic flow

As the surge in demand for the motor travel continues, the lane capacity is becoming increasingly ineffective in matching the operation of the traffic flow. A method that can utilize the reserved lane capacity and thereby effectively increase the lane capacity is urgently needed for this problem. It is considered in this paper that the formation of a mixed traffic flow which includes both the human-driven vehicles (HDVs) and the connected autonomous vehicles (CAVs) is one such method. Relying on the exploration of the following rules between the HDVs and the CAVs, a heterogeneous vehicle following model (HVFM) can be established as a guide for the formation of the mixed traffic flow. Under the guidance of the HVFM, the effects of different following mode types on the formation characteristics of the mixed traffic flow can be defined, determined and evaluated. At the same time, with the applicability of the three-phase traffic theory in mind, a three-phase traffic flow model under the mixed traffic flow condition (M-TTFM) is established for providing guidance on the operating mechanism of the mixed traffic flow, the determining method of the lane capacity and the utilizing method of the reserved lane capacity at the micro-theoretical level. The maximum lane capacity under the mixed traffic flow conditions can be up to 205.94 % of the lane capacity under HDV traffic flow conditions with the formation of the mixed traffic flow, which indicates that the reserved lane capacity can be greatly utilized by forming the mixed traffic flow. In particular, by mixing the CAV fleets platooned by the CAVs into the HDV traffic flow, the utilizing effect for the reserved lane capacity can be improved with a maximum level of 194.25 %, which is called the in-depth utilization of the reserved lane capacity. What is more, the fundamental diagrams expressing the relationship between the traffic flow rate and the density under the mixed traffic flow condition can be obtained and the recommended value of the lane capacity under the mixed traffic flow can be measured. In summary, the utilizing method for the reserved lane capacity in this paper is expected to further enhance the lane capacity and contribute to solving the problems caused by the lack of the lane capacity.

A new microscopic traffic-flow model based on the spatiotemporal continuous system concept considering nonlinear human response

A brand-new traffic model is introduced to precisely simulate the individual movement of vehicles, departing from the traditional macroscopic perspective (Eulerian scope or fluid dynamics concept) to adopt a microscopic approach (Lagrangian scope). This model is built on the first principle of Newton’s kinetic equation, explicitly incorporating nonlinear relations to represent human responses to a physical stimulus (observed information), such as the velocity gap between one’s own vehicle and a certain targeted one or the gap to a preceding vehicle, unlike the conventional car-following concept. The model posits that a driver’s recognition occurs intermittently rather than continuously, following specific probabilistic distributions of which the formulation is physically justified. Overall, the model can be described as a spatiotemporally continuous formulation unlike cellular automaton (CA) traffic models, crucial for capturing traffic dynamics at the micro and macro levels. The model is validated using a real traffic-flow dataset from a highway, and the results of the conventional CA traffic model are compared with those of the present one” along with a note for authorial verification. Animated simulations confirm that the present model can reproduce realistic flow dynamics characterized by smoother acceleration and deceleration compared to the conventional CA traffic model. The complete simulation source code for replicating the model is made publicly available.

Moral and formal model-based control strategy for autonomous vehicles at traffic-light-free intersections

Making an effective traffic strategy is a challenging problem that involves efficiency and safety, where traffic lights play an important role. Especially in cases without traffic lights, creating traffic strategies benefits significantly more complex and challenging. This complexity underscores the importance of research on autonomous vehicles, which has been trained widespread attention in recent years. In order to avoid these potential crises, the designed traffic strategy at traffic light-free intersections must be effective and reliable, ensuring the basic processes work properly. In this paper, we propose a novel method to formally model the traffic strategy for vehicles to make sure they safely and orderly pass through traffic light-free intersections. We use the state space analysis method to analyze the vehicle traffic model to ensure the decision made at each step is correct. By using Petri net, our proposed model achieves excellent analytical capabilities for asynchronous or highly concurrent systems than simpler Unified Modeling Language (UML) systems, as well as the ability to dynamically analyze system security and reliability.

Study on the Construction of Paid Traffic Test Model for Tiktok Car Account

Study on the Construction of Paid Traffic Test Model for Tiktok Car Account

Knowing the unknowns: Network traffic detection with open-set semi-supervised learning

Network traffic classification plays a crucial role in network security and network quality of service. The new emerging traffic data in the real world is unlabeled and may contain unknown classes, which introduces the problem of identifying unknown-class traffic in unlabeled data. However, existing deep learning-based methods either rely on large amounts of labeled data or assume the labeled and unlabeled data share the common label space. Therefore, they are unable to identify unknown traffic, resulting in decreased detection accuracy. This paper introduces DivinEye, a novel method for unknown network traffic detection based on open-set semi-supervised learning. DivinEye utilizes a small quantity of labeled data and a large amount of unlabeled data to train the model, achieving the ability to identify known classes accurately while also detecting unknown ones. To leverage incoming unlabeled traffic, DivinEye employs open-set semi-supervised techniques to select known-class data from unlabeled data to optimize the known traffic detection model. To detect unknown traffic, DivinEye combines the known traffic classifier and multi-binary classifier to construct an unknown traffic detection model, generating auxiliary targets for all unlabeled data as supervision to train the model. Trace-driven experiments demonstrate that DivinEye outperforms state-of-the-art methods by a large margin.

Fusion of multi-resolution data for estimating speed-density relationships

Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) data. This paper shows that ignoring the variability of HR data within the LR interval during the averaging process could lead to systematic data point distortions, resulting in biased model estimations. The average absolute biases of models estimated from the average data increase with the lost variability of HR data within the LR intervals. Subsequently, it proves that for any given complete average data dataset, there must exist an optimal dataset that minimizes the average absolute bias in model estimations introduced by the averaging process. A novel procedure for determining the practical optimal dataset is proposed. To test the proposed method, real-world HR data from four sites in Hong Kong and Nanjing, China were collected to mimic situations with multi-resolution data. Results demonstrated that the proposed method can significantly reduce the average absolute biases of models estimated from the determined practical optimal dataset, as compared to models estimated from the complete average dataset.

Urban Traffic Mobility Optimization Model: A Novel Mathematical Approach for Predictive Urban Traffic Analysis

This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM’s precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments.

The asymptotic behavior of Riemann solutions for the Aw-Rascle-Zhang traffic flow model with the polytropic and logarithmic combined pressure term

The exact Riemann solutions for a specially designated Aw-Rascle-Zhang traffic flow model are explicitly obtained by using the combination of 1-rarefaction or 1-shock wave with 2-contact discontinuity. The vacuum and delta shock formation is carefully identified and analyzed in the Riemann solutions when both the two perturbed parameters in the combined pressure term drop to zero simultaneously, where the intrinsic cavitation and concentration phenomena are concretely surveyed and explored. Additionally, our theoretical analysis results are also validated through several representative numerical simulations.

Using traffic assignment models to assist Bayesian inference for origin–destination matrices

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

Evaluating the Performance of Right Turn Lanes at Signalized Intersection Using Traffic Simulation Model

The issue of traffic congestion at signalized intersections is a concern in transportation systems due to the growth of urban areas and increased vehicular transportation. To study the evolution of congestion and evaluate the traffic performance operation of signalized intersections under problematic congested and improved conditions, the microscopic simulation VISSIM software is utilized. The objectives of this paper are to evaluate operational techniques, build a simulation model, and produce a well-calibrated and validated model. The methodology procedure to evaluate the signalized intersection involves the application of a traffic simulation model to observe real-time delays and stopped vehicles. Using the VISSIM software Version 9 to create an intersection model and redesign geometry with an exclusive right turn to enhance the intersection functionality and reduce delay. Our research focused on the Al-Nakhala signalized intersection located in the southern part of Palestine urban street in Baghdad city. This intersection is one of the busiest along the corridor due to significant land-use changes in the study area, including residential, educational, or commercial areas generating daily pressure from additional trips and saturating the absorptive capacity of the intersections. The proposed scenario of an exclusive right–turn could reduce the queue length and vehicle delay at the signalized intersection, resulting in a more efficient traffic operation. As a result of the reduction in vehicle delay, the Level of service (LOS) for the north, west, and east approaches improved from F to D. However, there was only a slight improvement for the south approach, with the LOS changing from E to F. Nonetheless, there was a noticeable reduction in queue length and vehicle delay ranging from 25% to 50%. Doi: 10.28991/CEJ-2024-010-07-010 Full Text: PDF

An extended intelligent driving model for autonomous and manually driven vehicles in a mixed traffic environment with consideration to roadside crossing

While the advantages of AVs and their impact on manually driven vehicles (MVs) have been widely discussed in continuous flow conditions, their performance under mixed traffic, intermitted flow conditions has yet to be properly studied. One of the representative scenarios is that vehicular flow is interrupted by roadside crossing obstacles such as pedestrians or cyclists. Since such interruption makes vehicles stop and go more frequently and creates random and complex traffic conflict, it has become a critical factor that can affect the driving performance of AVs. Therefore, this paper proposes a uniform traffic model (Pre_IDM+) to include roadside crossing impact in traffic flow analysis. The classical intelligent driving model (IDM) is extended into an obstacle-avoiding case, in which a novel pre-reaction workflow is introduced to describe yielding behavior and generate a reasonable braking trajectory. A real mixed traffic data near an un-signalized mid-block crosswalk is used to calibrate Pre_IDM+ and an accordingly microscope mixed traffic simulation platform is constructed. The simulation results show that discreet AVs can greatly avoid hard braking (−83.61%) and slightly improve passing speed (+5.11%) compared with MVs, while competitive AVs can maximize traffic efficiency (+7.03%) but will also deteriorate driving smoothness and comfort (−31.66%). Maintaining a sparse distribution of crossing sites along the road may contribute more to traffic stability and driving continuity compared with gathering all obstacles crossing at one location. This paper may help better understand the impact of AVs on general intermitted flow and give a reference to mixed traffic modeling towards a complex road condition.

Adaptive reconstruction of ship trajectory considering motion states

Abstract In response to the presence of noise and missing data in the records of ship trajectories from the Automatic Identification System (AIS), which affects the accuracy of ship traffic flow modeling and knowledge discovery, an adaptive reconstruction method for ship trajectories is proposed, taking into account the state of motion of the ship. This method comprehensively considers latitude, longitude, speed, course, and turn rate in the ship trajectory data and enables the identification and removal of position, speed, and course noise. In addition, the ship’s trajectory is divided into straight segments and turning segments based on the rate of change of the ship’s course. Various interpolation methods are adaptively applied to repair the trajectory. The validation with real ship trajectories shows that the proposed method has higher accuracy than other benchmark methods and provides a solid basis for evaluating ship trajectories.

Discrete superior dynamics of a generalized chaotic system

In the past few decades, the discrete dynamics of difference maps have attained the remarkable attention of researchers owing to their incredible applications in different domains, like cryptography, secure communications, weather forecasting, traffic flow models, neural network models, and population biology. In this article, a generalized chaotic system is proposed, and superior dynamics is disclosed through fixed point analysis, time-series evolution, cobweb representation, period-doubling, period-3 window, and Lyapunov exponent properties. The comparative bifurcation and Lyapunov plots report the superior stability and chaos performance of the generalized system. It is interesting to notice that the generalized system exhibits superior dynamics due to an additional control parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}. Analytical and numerical simulations are used to explore the superior dynamical characteristics of the generalized system for some specific values of parameter β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}. Further, it is inferred that the superiority in dynamics of the generalized system may be efficiently used for better future applications.

Multi‐graph representation spatio‐temporal attention networks for traffic forecasting in the cinematic metaverse

AbstractThe cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.