INTEGRATED MODELING FRAMEWORK FOR DYNAMIC INFORMATION FLOW AND TRAFFIC FLOW UNDER VEHICLE-TO-VEHICLE COMMUNICATIONS: THEORETICAL ANALYSIS AND APPLICATION

Modeling the spatiotemporal propagation characteristics of information under vehicle-to-vehicle (V2V) communications is critical for developing information-enabled applications to improve traffic safety and mobility. Existing analytical approaches assume instantaneous information flow propagation to simplify the communication constraints arising from the traffic flow dynamics. Consequently, information flow propagation characteristics such as the information flow propagation wave have not been analyzed. They are necessary to describe the interactions with the underlying traffic flow dynamics. An analytical model, which integrates an epidemic model with a traffic flow model, is developed to account for such interactions. The proposed model is able to capture the dynamics of information flow and traffic flow in an integrated formulation that circumvents key analytical and numerical challenges. Results from computational experiments demonstrate the effectiveness of the proposed model and its ability to describe the dynamic characteristics of information flow propagation along with the traffic flow dynamics.

Modeling the spatiotemporal propagation characteristics of information under vehicle-to-vehicle communications is critical for developing information-enabled applications to improve traffic safety and mobility. Existing analytical approaches assume instantaneous information flow propagation to simplify the communication constraints arising from the traffic flow dynamics. Consequently, information flow propagation characteristics such as the information flow propagation wave have not been analyzed. They are necessary to describe the interactions with the underlying traffic flow dynamics. An analytical model, which integrates an epidemic model with a traffic flow model, is developed to account for such interactions. The proposed model is able to capture the dynamics of information flow and traffic flow in an integrated formulation that circumvents key analytical and numerical challenges. Results from computational experiments demonstrate the effectiveness of the proposed model and its ability to describe the dynamic characteristics of information flow propagation along with the traffic flow dynamics.

ABSTRACTIn a vehicle-to-vehicle (V2V) communications-based advanced traveler information system (ATIS), the dynamic flow propagation of multiple units of information depends on the interactions between the traffic and inter-vehicle communication characteristics and constraints, and the consequent dynamics. This study models the dynamic flow propagation of multiple units of information using a multi-layer framework that captures the dynamics of the three interacting layers: physical traffic flow, inter-vehicle communication, and information flow. The traffic flow dynamics are captured using a cell transmission model in the physical traffic flow layer. The inter-vehicle communication layer uses the time-dependent locations of vehicles in the traffic flow layer and inter-vehicle communication-related constraints to determine the occurrences of inter-vehicle communication. The information flow layer depicts the flow of multiple units of information as a network. Thereby, the proposed framework describes how the dynamic flow propagation of multiple units of information can be mapped from the traffic flow dynamics and the inter-vehicle communication constraints. Synthetic experiments analyze the interactions between the traffic flow dynamics and inter-vehicle communication constraints, and the flow propagation characteristics of multiple units of information. They illustrate that the proposed multi-layer framework enables the integration of the traffic flow dynamics and inter-vehicle communication constraints to generate insights into the flow propagation of multiple units of information. Also, they indicate that it can be extended to incorporate the dynamic flow propagation characteristics of multiple units of information into the design of robust V2V-based ATIS architectures.

Graph‐Based Modeling of Information Flow Evolution and Propagation under V2V Communications‐Based Advanced Traveler Information Systems

Multiclass information flow propagation control under vehicle-to-vehicle communication environments

8-channel multiplexer

Analytical model for information flow propagation wave under an information relay control strategy in a congested vehicle-to-vehicle communication environment

Analytical model for information flow propagation wave under an information relay control strategy in a congested vehicle-to-vehicle communication environment

To analyze the influence of passenger transport on traffic flow, we develop a microscopic traffic flow model that incorporates various factors such as vehicle speed, acceleration, deceleration, lane-changing behavior, and interaction between different types of vehicles. The model takes into account the specific characteristics of passenger transport vehicles, their behavior in mixed traffic, and their impact on the overall traffic flow. We conducted extensive simulations using the developed microscopic traffic flow model to evaluate the influence of passenger transport on the traffic flow characteristics. The simulations were based on real-world scenarios and considered different traffic conditions, including varying traffic volumes. Our results demonstrate that the presence of passenger transport vehicles has a significant impact on the microscopic characteristics of traffic flow on country roads. We observed that the introduction of passenger transport vehicles affects the overall traffic flow dynamics, including vehicle speeds, acceleration patterns, and lane-changing behavior of both passenger transport and other vehicles in traffic flow. Furthermore, we found that the interaction between passenger transport and other vehicles plays a crucial role in determining the traffic flow characteristics. Additionally, our study highlights the importance of considering passenger transport in traffic flow models and transportation planning. The presence of passenger transport vehicles can significantly impact the overall performance of the road network, including travel time, congestion, and safety. Therefore, incorporating the characteristics and behavior of passenger transport vehicles into traffic flow models can provide more accurate predictions and assist in developing effective traffic management strategies. In conclusion, this study contributes to a better understanding of the influence of passenger transport on the microscopic characteristics of traffic flow on roads. The developed microscopic traffic flow model provides valuable insights into the behavior of passenger transport vehicles and their interaction with other vehicles, leading to a comprehensive understanding of traffic flow dynamics. The findings of this study can aid transportation planners and policymakers in making informed decisions for improving the efficiency, safety, and sustainability of road networks.

Research in understanding traffic flow is conducted since 1930s in mathematics, physics, and engineering fields. The main interest is to reveal the characteristics of traffic dynamics and consequently propose ways to reduce undesirable impacts of traffic flow to social and economical life. This can be achieved only if rigorous and reliable mathematical models are constructed. The first part of this work covers the classification of such models as well as empirical and software tools used to study and predict traffic flow. The second part is devoted to a critical parameter in the traffic dynamics: time delay, which is recognized in this particular area as early as 1958. Delay originates from the time needed by human drivers to become conscious, make decision, and perform control actions in traffic. Such a definition states that human beings actively control the time evolution of traffic by their time-delayed behaviors (human as a controller/plant), and thus traffic dynamics becomes inherently time delayed. This dynamical structure, in a global sense, can also be seen as an interconnection of dynamics that transfer information/energy/momentum among each other, but under the presence of communication/transportation delays. For the specific problem considered, we first discuss the source of time delay, its physical interpretations, and mathematical nature, and next present a survey on mathematical models that explicitly account for delays. We conclude with interesting research topics at the intersection of control theory and time-delay systems. In this context, an example traffic flow scenario is covered to both demonstrate this intersection and show the consequences of delay presence in traffic flow dynamics, especially from the stability point of view.KeywordsTime DelayFlow ModelTraffic FlowHuman DriverReaction DelayThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The speed of the information flow propagation wave is a fundamental characteristic to analyze vehicle-to-vehicle (V2V) communications based traffic systems. It depends on the density of vehicles equipped with a V2V communication capability, the V2V communication constraints, and the traffic flow dynamics. This study proposes an integrated model consisting of integro-differential equations to describe the information flow propagation mechanism and a partial differential equation to describe the traffic flow dynamics. It can directly incorporate the success rate of communication with distance and interference as a probability density function, and provides a closed-form solution for the speed of the information flow propagation wave. Numerical simulation experiments are used to analyze the performance of the analytical solution. The results show that the analytical solutions are consistent with the average speed of the information flow propagation wave provided by the numerical experiments.

Short-term traffic flow predictions are an essential part of intelligent transportation systems. Previous research underlines the difficulty in systematically assessing the predictability of traffic flow near capacity or during congested conditions. In this article a neural network prediction scheme is proposed that is consistent with the pattern-based evolution of traffic flow and has the capability of exploiting past information to acquire knowledge on the traffic dynamics in order to enhance predictability. Findings indicate that pattern-based predictions are more accurate—in the traffic flow regimes considered—when compared to other local and global prediction techniques that operate under the time-series consideration. The pattern-based prediction scheme was also found to outperform the other methods tested in the knowledge of the anticipated traffic flow state in all traffic flow conditions considered.

In road traffic systems with Vehicle-to-Vehicle (V2V) communications, the information flow propagation is determined by (V2V) communication events and traffic flow dynamics. Green wave traffic light system has several benefits, such as allowing for higher traffic loads, reducing traffic jams, and facilitating pedestrian traffic. When green wave traffic light works properly, all vehicles within the wave can drive through a sequence of green traffic lights at a certain speed without having to stop at the signals. In this paper, we study the impact of green wave traffic light system on the performance of (V2V) communications. We study the evaluation performance of network metrics such as packet delivery ratio, end-to-end delay and packet loss using NS-2 simulator. We found that the green wave strategy outperforms synchronized traffic lights strategy.

In recent years, more attention has been paid to the study of modelling heterogeneous traffic flow dynamics consisting of regular vehicles (RVs) and connected vehicles (CVs). However, the establishment of current mixed traffic flow models mostly lacks the support of field experimental data analysis results. In this study, a new heterogeneous car‐following model is presented on the basis of data mining results to study the impact of vehicle‐to‐vehicle (V2V) safety messages on traffic flow stability. The analytically critical stability criterion of this proposed heterogeneous model is obtained by linear stability analysis. Then, the effect of the market penetration rate (MPR) of CVs on heterogeneous traffic stability is investigated. Finally, simulation experiments are carried out under periodic boundary conditions to test the critical stability condition. Both the theoretical derivation and simulation experiment results show that the traffic flow stability can decline despite improvements in traffic safety in the case of considering only the safety information of the nearest‐neighbour leading vehicle. However, both the safety and stability of traffic flow can be enhanced by considering the V2V messages of more leading vehicles. Moreover, the heterogeneous traffic flow stability is improved with an increase in the MPR of CVs.

Connected vehicles at single lane roundabouts and its performance evaluation