Traffic Theory Research Articles

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.

Estimating Markov Chain Mixing Times: Convergence Rate Towards Equilibrium of a Stochastic Process Traffic Assignment Model

Network equilibrium models have been extensively used for decades. The rationale for using equilibrium as a predictor is essentially that (i) a unique and globally stable equilibrium point is guaranteed to exist and (ii) the transient period over which a system adapts to a change is sufficiently short in time that it can be neglected. However, we find transport problems without a unique and stable equilibrium in the literature. Even if it exists, it is not certain how long it takes for the system to reach an equilibrium point after an external shock onto the transport system, such as infrastructure improvement and damage by a disaster. The day-to-day adjustment process must be analysed to answer these questions. Among several models, the Markov chain approach has been claimed to be the most general and flexible. It is also advantageous as a unique stationary distribution is guaranteed in mild conditions, even when a unique and stable equilibrium does not exist. In the present paper, we first aim to develop a methodology for estimating the Markov chain mixing time (MCMT), a worst-case assessment of the convergence time of a Markov chain to its stationary distribution. The main tools are coupling and aggregation, which enable us to analyse MCMTs in large-scale transport systems. Our second aim is to conduct a preliminary examination of the relationships between MCMTs and critical properties of the system, such as travellers’ sensitivity to differences in travel cost and the frequency of travellers’ revisions of their choices. Through analytical and numerical analyses, we found key relationships in a few transport problems, including those without a unique and stable equilibrium. We also showed that the proposed method, combined with coupling and aggregation, can be applied to larger transport models. History: This paper has been accepted for the Transportation Science Special Issue on the 25th International Symposium on Transportation and Traffic Theory. Funding: This study was financially supported by the Japan Society for the Promotion of Science [Grant-in-Aid 20H00265]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0523 .

Evaluating and enhancing the safety performance of automated longitudinal control at on-ramp merging bottleneck: A simulation study in the framework of Kerner’s three-phase traffic theory

The adaptive cruise control (ACC) system, an essential component of commercial autonomous driving that functions in longitudinal control, has attracted significant research interest because of its potential to reduce accident rates. By introducing a non-fixed headway concept, Kerner recently proposed the Three-traffic-Phase ACC (TPACC) model, which integrates a speed adaptation module derived from the three-phase traffic theory. However, the efficacy of such headway flexibility in safety performance remains underexplored. To fill this gap, this study aims to evaluate the longitudinal crash risk of TPACC at on-ramp merging bottleneck, a site where complicated traffic dynamics often constitutes concern, and to further bolster its safety performance. Specifically, simulations are conducted to evaluate the longitudinal crash risk of pure TPACC traffic as well as human-machine mixed traffic, by comparing it with a classical ACC model across various flow rates and market penetration rates. Enhancement of TPACC's safety performance is achieved through the multi-objective optimization in regard to a parameter set of the speed adaptation module. The results indicate that TPACC has the ability to weaken disturbances caused by merging vehicles and leads to a markedly decline in the longitudinal crash risk. Mixed traffic incorporating with TPACC vehicles also exhibits superior safety performance over that with the classical ACC vehicles. Furthermore, the proposed optimization measure effectively enhances TPACC’s longitudinal safety performance across a vast spectrum of penetration rates and flow rates at on-ramp merging area, all the while preserving throughput efficiency. On this basis, a guideline for directional tuning of the parameter set under conditions of limited information accuracy is provided, thereby heightening its practicality.

Scheduling in the High-Uncertainty Heavy Traffic Regime

We propose a model uncertainty approach to heavy traffic asymptotics that allows for a high level of uncertainty. That is, the uncertainty classes of underlying distributions accommodate disturbances that are of order 1 at the usual diffusion scale as opposed to asymptotically vanishing disturbances studied previously in relation to heavy traffic. A main advantage of the approach is that the invariance principle underlying diffusion limits makes it possible to define uncertainty classes in terms of the first two moments only. The model we consider is a single-server queue with multiple job types. The problem is formulated as a zero sum stochastic game played between the system controller, who determines scheduling and attempts to minimize an expected linear holding cost, and an adversary, who dynamically controls the service time distributions of arriving jobs and attempts to maximize the cost. The heavy traffic asymptotics of the game are fully solved. It is shown that an asymptotically optimal policy for the system controller is to prioritize according to an index rule, and for the adversary, it is to select distributions based on the system’s current workload. The workload-to-distribution feedback mapping is determined by a Hamilton–Jacobi–Bellman equation, which also characterizes the game’s limit value. Unlike in the vast majority of results in the heavy traffic theory and as a direct consequence of the diffusive size disturbances, the limiting dynamics under asymptotically optimal play are captured by a stochastic differential equation where both the drift and the diffusion coefficients may be discontinuous. Funding: R. Atar is supported by the Israeli Science Foundation [Grant 1035/20].

Insights into vehicle conflicts based on traffic flow dynamics

The utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds. Different traffic states and corresponding transitions are identified with the three-phase traffic theory using high-resolution trajectory data. Meanwhile, the SSMs are mapped to the corresponding traffic states from the perspectives of time, space, and deceleration. Three models, including k-means, GMM, and Mclust, are investigated and compared to optimize the identification of traffic conflicts. It is observed that Mclust outperforms the others based on the evaluation metrics. According to the results, there is a variation in the distribution of traffic conflicts among different traffic states, wide moving jam (phase J) has the highest conflict risk, followed by synchronous flow (phase S), and free flow (phase F). Meanwhile, the thresholds of traffic conflicts cannot be fully represented by the same value through different traffic states. It reveals that the heterogeneity of thresholds is exhibited across traffic state transitions, which justifies the necessity of dynamic thresholds for traffic conflict analysis.

Evaluation of Car-Following Model for Mixed Autonomous and Human Driven Vehicles on Road Facilities.

Driverless cars are emerging slowly but bear the opportunity to improve the traffic system efficiency and user comfort. For the near future, a mix of human-driven and driver-less vehicle co- existence is inevitable. The quest to integrate autonomous and human-driven vehicles has created numerous critical questions in the road traffic system: How can autonomous and human- driven vehicles co-exist efficiently? What are the prospects of breaching the existing car-following model widely adopted to control the longitudinal movement of vehicles and control the driver’s maintenance of a safe distance? There is no doubt, that the emergence of autonomous vehicles and their integration is altering the existing traffic theories and the idea behind the car-following model. Currently, researchers are working hard to efficiently describe the physics behind the traffic flow theory of car-following models in mixed traffic. This work investigated a homogeneous car-following model sensitivity with varying human drivers’ reaction time in mixed traffic. The ability to predict the optimal vehicle response time in a platoon to the behaviour of its predecessor is vital in predicting the effect a change in the driving environment of mixed traffic has on traffic flow. The major complexities surrounding the car- following model in a mixed traffic flow environment are how the traffic participants share the road intersection space and the car-following behavior of human-driven vehicles. Investigation in microscopic traffic flow describes the way a single car be- haves and then introduces the impact another car has on a car’s behavior to address the mixed traffic flow. The traffic speed performance under the different traffic conditions suggests that the proposed mixed traffic car-following model outperforms the existing car-following model for human-driven vehicles with an average of 22.59mph. There is a significant throughput improvement in a mixed traffic environment when the distance headway of autonomous vehicles is adjusted to support the research hypothesis.

Audio signal transmission method in network-based audio analytics system

The subject matter of the article is аudio signal transmission method in network-based audio analytics system. The creation of a network-based audio analytics system leads to the emergence of new classes of load sources that transmit packetized sound data. Therefore, without constructing adequate mathematical models, it is impossible to build a well-functioning network-based audio analytics system. A fundamental question in traffic theory is the question of load source models. The development of an method for transmitting audio signals in a network-based audio analytics system becomes necessary. Based on this, the goal of the work is to create methods an method for transmitting audio signals in a network-based audio analytics system to ensure efficiency and accuracy in audio analytics. The following tasks were solved in the article: the formation of a model for the system's load sources, investigation of connection and traffic management, implementation of control and traffic monitoring functions in the network, research of methods to ensure the quality of audio signal transmission and the development of a method of transmitting an audio signal by virtual routes switching. To achieve these goals, the following methods are used: mathematical signal processing, data compression algorithms, optimization of network protocols, and the use of high-speed network connections. The obtained results include modeling of the system's load sources, examination of connection and traffic management, investigation of methods to ensure the quality of audio signal transmission and a method of transmitting an audio signal by virtual routes switching was proposed. In conclusion, the possibilities of using simulation modeling of nodes in the network-based audio analytics system are highly limited. This is explained by the fact that the acceptable level of information loss in data centers is very low. The use of the developed method enables effective control and processing of sound information in real-time. This method can find broad applications in various fields, including security, healthcare, management systems, and other industries where the analysis of audio signals is a crucial element.

Physics of automated-driving vehicular traffic.

We have found that phase transitions occurring between three traffic phases [free flow (F), synchronized flow (S), and wide moving jam (J)] determine the spatiotemporal dynamics of traffic consisting of 100% automated-driving vehicles moving on a two-lane road with an on-ramp bottleneck. This means that three-phase traffic theory is a common framework for the description of traffic states independent of whether human-driving or automated-driving vehicles move in vehicular traffic. To prove this, we have studied automated-driving vehicular traffic with the use of classical Helly's model [Proceedings of the Symposium on Theory of Traffic Flow (Elsevier, Amsterdam, 1959), pp. 207-238] widely applied for automated vehicle motion. Although dynamic rules of the motion of automated-driving vehicles in a road lane are qualitatively different from those of human-driving vehicles, we have revealed that traffic breakdown (F→S transition) at the bottleneck exhibits the nucleation nature, which was observed in empirical field data measured in traffic consisting of 100% human-driving vehicles. The physics of the nucleation nature of the F→S transition in automated-driving traffic is associated with a discontinuity in the rate of lane-changing that causes the discontinuity in the rate of over-acceleration. This discontinuous character of over-acceleration leads to both the existence and self-maintaining of synchronized flow at the bottleneck in automated-driving vehicular traffic as well as to the existence at any time instant of a range of highway capacities between some minimum and maximum capacities. Within the capacity range, an F→S transition can be induced; however, when the maximum capacity is exceeded, then after some time-delay a spontaneous F→S transition occurs at the bottleneck. The phases F, S, and J can coexist each other in space and time.

The Onset of a Blocking Event as a “Traffic Jam”: Characterization with Ensemble Sensitivity Analysis

Abstract Recently, Nakamura and Huang proposed a theory of blocking onset based on the budget of finite-amplitude local wave activity on the midlatitude waveguide. Blocks form in their idealized model due to a mechanism that also describes the emergence of traffic jams in traffic theory. The current work investigates the development of a winter European block in terms of finite-amplitude local wave activity to evaluate the possible relevance of the “traffic jam” mechanism for the flow transition. Two hundred members of a medium-range ensemble forecast of the blocking onset period are analyzed with correlation- and cluster-based sensitivity techniques. Diagnostic evidence points to a traffic jam onset on 17 December 2016. Block development is sensitive to upstream Rossby wave activity up to 1.5 days prior to its initiation and consistent with expectations from the idealized theory. Eastward transport of finite-amplitude local wave activity in the southern part of the block is suppressed by nonlinear flux modification from the large-amplitude blocking pattern, consistent with the expected obstruction in the traffic jam model. The relationship of finite-amplitude local wave activity and its zonal flux as mapped by the ensemble exhibits established characteristics of a traffic jam. This study suggests that the traffic jam mechanism may play an important role in some cases of blocking onset and more generally that applying finite-amplitude local wave activity diagnostics to ensemble data is a promising approach for the further examination of individual onset events in light of the Nakamura and Huang theory. Significance Statement Blocking is an occasional phenomenon in the mid- and high-latitude atmosphere characterized by the stalling of weather systems. Episodes of blocking are linked to extreme weather but their occurrence is not completely understood. A recent theory suggests that blocks may form in the jet stream like traffic jams on a highway. The onset mechanism contained in the theory could explain why forecasts of blocking are sometimes poor. In this work, we investigate the formation of a 2016 European winter block in the context of the traffic jam theory. Though questions remain regarding the implications for forecast uncertainty, our findings strongly support the notion of a traffic jam onset.

The Action Point Angle of Sight: A Traffic Generation Method for Driving Simulation, as a Small Step to Safe, Sustainable and Smart Cities

Computer simulations of traffic and driving provide essential solutions to reduce risk and cost in traffic-related studies and research. Through nearly 90 years of simulation development, many research projects have attempted to improve the various aspects of realism through the use of traffic theory, cameras, eye-tracking devices, sensors, etc. However, the previous studies still present limitations, such as not being able to simulate mixed and chaotic traffic flows, as well as limited integration/interoperability with 3D driving simulators. Thus, instead of reusing previous traffic simulators, in this paper, we define relevant concepts and describe the development and testing of a novel traffic generator. First, we introduce realistic aspects to improve traffic generation, including interactive physics (i.e., interactions based on physics among the vehicles, infrastructure, and weather) and natural traffic behaviors (e.g., road user behaviors and traffic rules), allowing the self-driving vehicle behaviors to mimic human behaviors under stochastic factors such as random vehicles and speed. Second, we gain experiences from the technical deficiencies of existing systems. Third, we propose methods for traffic generation based on the action point angle of sight (APAS) formula, which adheres to these constraints and is interoperable with modern driving simulators. We also conducted quantitative evaluations in two experiments (comprising 250 trials), in order to prove that the proposed solution can effectively simulate mixed traffic flows. Moreover, the approaches presented in this study can help self-driving cars to find their way at an intersection/T-junction, as well as allowing them to steer automatically after an accident occurs. The results indicate that traffic generation algorithms based on these new traffic theories can be effectively implemented and used in modern driving simulators and multi-driving simulators, outperforming previous traffic generators based on repurposed technologies.

A cellular automaton traffic model by mechanical restriction and the slow-to-start rules in the framework of Kerner’s three-phase traffic theory

Most of the conventional cellular automaton (CA) traffic flow models have two shortcomings: unlimited deceleration capabilities and incapability of reproducing the synchronized flow in the three-phase traffic flow theory. Based on an original deceleration CA model that emphasizes limited mechanical capabilities and human overreaction as the origin of congested traffic states, this paper proposes a new deceleration CA model where the slow-to-start rules are incorporated. For periodic boundary conditions, one also finds that the present model can reproduce well the three different phases of traffic flow (free flow, synchronized traffic flow, wide moving jam) as well as two first-order phase transitions (the transitions from free flow to synchronized flow and from synchronized flow to wide moving jam) among them. Compared to the original deceleration CA model, one notes that the phase transition from the synchronized flow to wide moving jam becomes distinguishable. Furthermore, the present model can reliably reproduce most empirical findings including synchronized flow with different slopes, the so-called pinch effect, and the time-headway distribution of free flow and so on. Importantly, the synchronized flow with different slopes is supported by spatiotemporal diagrams and the statistical distribution of velocity and so on. For open boundary conditions, the present model can reproduce the spatiotemporal diagrams of well-known five patterns including moving synchronized flow pattern, localized synchronized flow pattern, widening synchronized flow pattern, dissolving general pattern and general pattern.

Statistical physics of the development of Kerner's synchronized-to-free-flow instability at a moving bottleneck in vehicular traffic.

With the use of simulations of a stochastic microscopic traffic model in the framework of the three-phase traffic theory, we have revealed the statistical physics of a traffic flow instability with respect to a transition from synchronized flow (S) to free flow (F) (Kerner's S→F instability) at a moving bottleneck (MB) occurring through a slow-moving vehicle in vehicular traffic. We have found that the S→F instability can occur at the MB more frequently than at an on-ramp bottleneck. From a comparison of the occurrence of the S→F instability at the MB and on-ramp bottleneck at the same probability of traffic breakdown and the same flow rate it has been found that, whereas the frequency of the S→F instability at the on-ramp bottleneck barely changes, the larger the velocity of the MB, the more frequently the S→F instability occurs at the MB. Contrarily, when the MB velocity decreases considerably, then rather than the S→F instability, in synchronized flow at the MB the classical traffic flow instability leading to the emergence of wide-moving jams (S→J instability) occurs. It has been found that the physics of the intensification of the S→F instability at the MB with the increase in the MB velocity is associated with the increase in the mean space gap (mean time headway) between vehicles in synchronized flow. For this reason, when the MB velocity increases, there is an MB velocity at which the S→F instability dominates the S→J instability: The MB velocity influences considerably on the competition between the S→F and classical traffic flow instabilities in synchronized flow.

Link-Based Traffic Estimation and Simulation for Road Networks Using Electronic Registration Identification Data

Traffic estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation.

Research on the impact of entry points on urban arterial roads in the framework of Kerner’s three-phase traffic theory

Urban arterial roads, characterized by high traffic volumes and driveway densities, are prone to congestion and accidents due to external disturbances. The disturbance from the driveways (entry points) between arterials and adjacent properties has aroused wide concern in recent years. In this paper, based on the well-known KKSW (Kerner–Klenov–Schreckenberg–Wolf) CA (cellular automaton) model, an adapted KKSW-EP (entry points) CA model is proposed to study the influence of entry points on urban arterial roads. Traffic flow patterns under several entrance inflow rates and different locations of entry points are found by simulation. Besides, explanations for these patterns are provided in the framework of Kerner’s three-phase traffic theory. Moreover, the impact of entry points on the safety of urban arterial roads is also assessed. The results show that the congested patterns on the two lanes upstream of the entry point are not the same because they are caused by different types of bottlenecks and the right lane suffers from greater bottleneck strength. The safety assessment shows that severe conflicts are associated with the speed difference between vehicles caused by entry points and wide moving jams.

Signal performance optimization in the local area network trafic management in the DRC : Models for transmission networks

The high availability of computer networks is a prerequisite in large companies and service providers. Thus, computer network administrators are called upon to face the various growing challenges related to unscheduled downtime of services; lack of expertise; lack of tools; the complexity of technologies; market consolidation and competition to provide better quality services. It should be remembered that the organization of the networks of telecommunication aims at giving control of the phenomena which occur there, during the treatment of the communications, and more generally of the services which they offer. These phenomena are governed by the randomness of the appearance of the requests, and are studied independently of the choices of technology implemented. They are amenable to the formalism of the calculation of probabilities, and give birth to the notion of traffic, which will play a central role in their apprehension, given that they condition for a large part the effective structure of modern networks. This research specifically examines the problems of optimization of signal performance in traffic management of local area networks of companies in the Democratic Republic of Congo, by presenting the different elements of network traffic theory and quality. Thus demonstrating their impact on the architectures of networks and computer systems, and at the same time introducing the major problems faced by IT network administrators on a daily basis; The main objective of this research is to evaluate the development performance of computer and telecommunications networks according to their applications in the areas of network design, equipment dimensioning, and characterization and quality measurement. Service, and network planning techniques.

A Heavy Traffic Theory of Two-Sided Queues

Motivated by emerging applications in online matching platforms and marketplaces, we study a two-sided queue. Customers and servers that arrive into a two-sided queue depart as soon as they are matched.

Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner’s three-phase traffic theory

As the important spatiotemporal state of traffic flow discovered by Kerner’s three-phase traffic theory, the synchronized traffic flow describes a new traffic phase in congested traffic. However, until now most models within the standard traffic theories cannot reproduce it. The average space gap model (ASGM) is a simple cellular automaton model aimed to reproduce various empirical findings discovered by Kerner’s three-phase traffic theory by incorporating the influence of the multi-anticipative effect on vehicle’s deceleration. However, this paper shows that the simulated synchronized flow by ASGM is not consistent with the reality. To this end, the Multi-Anticipative Model (MAM) based on ASGM is proposed, which describes the influence of the multi-anticipative effect on both the accelerations and the decelerations. Simulations indicate that the empirical consistent synchronized flow and related congested patterns can be well reproduced by MAM. Moreover, MAM can reproduce the speed drop in the car following vehicle platoons reported by the empirical observations. Generally, MAM indicates that the multi-anticipative effect can shed light on the understanding and capturing the complex characteristics of traffic flow especially reported by Kerner’s three-phase traffic theory.

A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algorithms

In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource-sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.

A systematic approach for microscopic models based on cellular automata for road traffic

The transportation infrastructure is one of the most important resources for a country's economic and social well-being. The effectiveness of a country's street network will decide whether it develops further or stagnates. With the increasing number of vehicles on the road and the effects of urbanization, traffic roads are being subjected to a variety of requests and uses for which they were not designed, sized, or predicted. Because of the critical relevance of traffic roads, research must begin to lessen the effects of traffic jams in the streets, determine the appropriate number of traffic lanes, and integrate real-time traffic information into GPS systems. The goal of modeling a traffic-road system is to either build new traffic systems or gain a better knowledge of existing traffic systems so that they can be optimized. The accuracy, performance, stochastic and dynamic behavior of the model produced will be evaluated using a simulation of a genuine traffic system. This paper provides microscopic models based on cellular automation to replicate the behavior of various automobiles on a set of urban streets in Cluj Napoca city downtown. This model includes streets with multiple traffic lanes, various types of vehicles such as automobiles, buses, and trams, intersections with multiple possible upcoming streets controlled by traffic lights, bus stops inside and outside the traffic lane, tram stops inside the traffic lane, pedestrian crosswalks, and parking areas alongside and transversely with the right traffic lane of a street. TCA (Traffic Cellular Automata) is a proposed model that produces adequate findings in urban traffic theory. The results were obtained in both free-flow and traffic-jam conditions.

Evolution of behaviors in heterogeneous traffic models as driven annealed disorders and its relation to the n-vector model

In one-dimensional heterogeneous models of hard-particle flux (like vehicular traffic), the system dynamics depend strongly on the behavior of the leading particle. In models satisfying the following properties: the interactions are unidirectional; the dynamics of the particles maximize the velocity or reduces the gap between particles; the particles are hard, and; there is no exchanging of particles with the exterior, simple heuristic arguments suggests a link between traffic theory and graph theory that considerably simplifies the analysis of the spreading of driving styles through social contagion or random fluctuations. The evolutionary dynamics transforms the quenched disorders characterizing the inhomogeneities of heterogeneous systems in dynamical (annealed) disorders which are driven toward specific regions in the space of parameters. De fining vectors on the space of parameters which entries are the parameters controlling the behavior of the individuals (parameters of the model), the arguments show a connection between the evolutionary dynamics of these systems and asymptotic behaviors of the n-vector model. When the time-scale ratio of selection to the local imitation to the mutation processes, τi/τm, is small an organized state where “orientation” corresponding to the set of parameters of the slowest strategies is favored, and if this ratio is big an unorganized state without a preferential orientation is favored.