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The main result of this paper is a mathematical model that describes the dynamics of the motion of several cars in areas with different speed limits. As such areas, we can consider speed limit zones and speed bumps or uneven road surfaces. The model is a system of differential equations with a delayed argument. The dynamical properties of the model are studied by numerical methods. A computer program has been developed that uses the model to describe the motion of traffic flows in various road situations. The simulation results coincide with the observation data of real traffic flows. [ABSTRACT FROM AUTHOR]

This paper studies the estimation of parameters estimation in a traffic flow model. The model is represented by a system of differential equations with a time delay. The main result of this paper is the calculation of the range of values for the parameters describing the intensity of acceleration and braking, as well as a coefficient that describes how smoothly the pursuing vehicle adjusts its speed to the one in front. The parameters of the model are estimated with analytical calculations and numerical experiments. A special computer program is developed for the numerical experiment. [ABSTRACT FROM AUTHOR]

The main result of this study is a new mathematical model that describes the traffic flow movement dynamic of several vehicles. The model is represented by a system of delay differential equations. Units of measurement and parameter values are determined for the model. Using numerical methods, the basic dynamic properties of the model and the stability of the uniform movement of vehicles are analytically investigated. A computer program is also developed. The program based on the resulting model simulates the movement of traffic flows in various road situations. The simulation results observed in the program correspond to the observation data of real traffic flows and traffic lights. [ABSTRACT FROM AUTHOR]

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. • A novel mathematical model for traffic flow is presented, which incorporates the prediction of the leading vehicle's dynamics. • The novel model is numerically investigated using specially developed software, demonstrating its applicability in various traffic scenarios. • The novel model shows significant improvements in traffic dynamics compared to the original model and closely aligns with real traffic flow observations. • The novel model reduces delay times, resulting in increased traffic throughput and efficiency. • The novel model aligns well with observed data, indicating its potential use in automated driving systems. [ABSTRACT FROM AUTHOR]

This paper investigates the vulnerability of urban traffic networks to cyber attacks on traffic lights. We model traffic signal tampering as a bi-objective optimization problem that simultaneously seeks to reduce vehicular throughput in the network over time (maximize impact) while introducing minimal changes to network signal timings (minimize noticeability). We represent the Spatio-temporal traffic dynamics as a static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels.

The objective of this study was to determine the influence of vehicular traffic on the environmental noise levels of the Santa Marta City tourist route on the Colombian coast. An analysis of vehicle types and frequencies at various times of the day over nearly a year helped to track the main sources of environmental noise pollution. Five sampling points were selected, which were distributed over 12 km, with three classified as peripheral urban and two as suburban. The average traffic flow was 966 vehicles/h and was mainly composed of automobiles, with higher values in the peripheral urban area. The noise level was 103.3 dBA, with background and peak levels of 87.2 and 107.3 dBA, respectively. The noise level was higher during the day; however, there were no differences between weekdays and weekends. The results from the analysis of variance showed that the number of vehicles and the noise levels varied greatly according to the time of day and sampling point location. The peak and mean noise levels were correlated with the number of automobiles, buses and heavy vehicles. The mean noise levels were similar at all sample points despite the traffic flow varying, and the background noise was only correlated for automobiles (which varied much more than the heavy vehicles between day and night). [ABSTRACT FROM AUTHOR]

Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called “g-estimator method” using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion – AIC and Bayesian Information Criterion – BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively.

Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called "g-estimator method" using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion - AIC and Bayesian Information Criterion - BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine the influence of vehicular traffic on the environmental noise levels of the Santa Marta City tourist route on the Colombian coast. An analysis of vehicle types and frequencies at various times of the day over nearly a year helped to track the main sources of environmental noise pollution. Five sampling points were selected, which were distributed over 12 km, with three classified as peripheral urban and two as suburban. The average traffic flow was 966 vehicles/h and was mainly composed of automobiles, with higher values in the peripheral urban area. The noise level was 103.3 dBA, with background and peak levels of 87.2 and 107.3 dBA, respectively. The noise level was higher during the day; however, there were no differences between weekdays and weekends. The results from the analysis of variance showed that the number of vehicles and the noise levels varied greatly according to the time of day and sampling point location. The peak and mean noise levels were correlated with the number of automobiles, buses and heavy vehicles. The mean noise levels were similar at all sample points despite the traffic flow varying, and the background noise was only correlated for automobiles (which varied much more than the heavy vehicles between day and night).

Traffic flow dynamics and energy consumption differs under dissimilar weather conditions, while seldom investigations have been conducted with a cellular automata model. In this paper, the friction coefficient between ground and tire is considered as the quantitative label of weather, a dynamic safe gap based on friction coefficient to avoid rear-end crash is introduced. We developed a safer one-dimensional model to examine the kinetic energy consumption under different weathers. Numerical results show that previous models overestimated the kinetic energy consumption in medium density flow (density < 0.5). In medium flow, speed limit will not reduce energy consumption on rainy and snowy days in most cases, but is necessary for prevention of accidents. Inversely, the effect of speed control on energy consumption is obvious under extreme weather. Our work can promote a better understanding of traffic dynamics, reduce energy dissipation and be applied to real traffic management. [ABSTRACT FROM AUTHOR]

This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

Effective treatment of acute coronary syndrome (ACS) patients depends on the transportation time to a hospital. But selection of an optimal route and target hospital for an ambulance within a large city is a complex problem. It requires taking into account the dynamical nature of an urban environment. Such dynamic factors as traffic flow, changing road graph, population mobility, and hospital capabilities are sources of uncertainty in decision making on hospitalization, and eventually they influence the functioning quality of emergency medical services (EMS) in a city. This work is devoted to the analysis of this problem for the city of Saint-Petersburg (Russia) with the use of statistical data, public geographic information services (OpenStreetMap), and real-time data on traffic flow (Yandex.Maps). It is shown that dynamic traffic conditions influence selection of a hospital and have to be considered within the task of ambulance routing. The results may be applied in order to design a more efficient EMS decision support system for ambulance personnel and dispatchers. [ABSTRACT FROM AUTHOR]

The incorporation of two macroscopic approaches reflecting Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) traffic dynamics in a gas-kinetic (GKT) traffic flow model is presented. The first approach was recently analyzed in the literature aiming to describe the effects induced by the ACC and CACC systems due to changes of the speed of the leading car(s) by the introduction of an acceleration/deceleration term. The second approach is a novel one and is based on the introduction of a relaxation term that satisfies the time/space-gap principle of ACC or CACC systems. In both approaches, the relaxation time is assigned on multiple leading vehicles in the CACC case; whereas in the ACC case this relaxation time is only assigned to the direct leading vehicle. We numerically approximate the resulting models by an accurate and robust high-resolution finite volume relaxation scheme, where the nonlinear system of partial differential equations is first recast to a diagonalizable semi-linear system and is then discretized by a higher-order WENO scheme. Numerical simulations investigate the effect of the different ACC and CACC approaches to traffic flow macroscopic stability with respect to perturbations introduced in a ring road and to flow characteristics in open freeways with merging flows at an on-ramp. Following from the numerical results, it can be concluded that CACC vehicles increase the stabilization of traffic flow, with respect to both small and large perturbations, compared to ACC ones. Further, the proposed CACC approach can better improve the dynamic equilibrium capacity and traffic dynamics, especially at the on-ramp bottleneck. [ABSTRACT FROM AUTHOR]

In this article, we propose an extended multiclass gas-kinetic theory applicable to mixed traffic flow of manual and adaptive cruise control (ACC) vehicles. In the model, the acceleration/deceleration of ACC vehicles is specified explicitly using microscopic models. The macroscopic multiclass traffic equations are obtained from the proposed gas-kinetic model using the well-known method of moments. The influencing conditions to traffic flow stability with respect to a small perturbation are derived by the linear stability method. The analytical results show that ACC vehicles contribute to the improved stabilisation of traffic flow. The numerical simulations of our developed multiclass macroscopic model on a circular freeway support our analytical findings and indicate that increasing the penetration of ACC vehicles results in more stable traffic flow. Simulations of merging flows at an on-ramp in an open freeway are carried out to describe the effects of the penetration of ACC vehicles on the bottleneck capacity. It is found that around 30% ACC vehicles in traffic flow leads to significantly increased capacity and reduced travel time. We argue that, together with other microscopic models, our model provides a wider picture of the effects of ACC vehicles on traffic flow characteristics. [ABSTRACT FROM PUBLISHER]

Abstract: The need for traffic noise prediction models that take traffic dynamics into account has been recently shown for urban areas. Such models couple a dynamic representation of traffic with noise emission laws. The contribution of the paper is to test different traffic and noise source representations for L Aeq and statistical levels estimation. Tests on four scenarios that reflect urban traffic conditions are carried on. They show that an individualized representation of vehicles with a macroscopic behavior rule is sufficient for noise descriptors estimation. Noise sources have to be aggregated on cells to reduce the calculation time of noise emission propagation. To this end a grid of line source representation appears to be more relevant than a grid of point source representation. Furthermore, large cells do not affect substantially the noise descriptors estimation. [Copyright &y& Elsevier]

Abstract: The development and validation of a model for dynamic traffic noise prediction is presented. The model is composed of a GIS-based traffic microsimulation part coupled with an emission model, and a beamtrace-based 2.5D propagation part, which takes into account multiple reflections and diffractions. The model can be used to analyze the influence of real urban traffic situations (e.g., traffic flow management, road saturation) in the usual equivalent sound level maps. However, it also allows to calculate and visualize statistical noise levels and indicators derived from them. Novel descriptors based on the power spectrum of noise level fluctuations can be obtained. A part of Gentbrugge, Belgium, is taken as a validation area; different traffic demand scenarios are simulated. [Copyright &y& Elsevier]