Search

Cooperative adaptative cruise controls and variable speed limit are gaining notoriety in recent years to fulfill the aim of reducing traffic congestions. Improvements in the management of hazardous materials, combined with these technologies, can reduce damages caused by congestions, mitigate risks to human health, and avoid environmental leakage. Drivers' behaviors are directly influenced by human beings' different reactions. Therefore, the variance in traffic produces shockwaves, rippling traffic waves, and a reduction of the traffic flow capacity. This paper aims to present adaptative optimal control of nonlinear systems simulation as a tool to prove the effects of the predictions according to drivers' behavior. Furthermore, it supports the decision making of hazardous material traffic management. Traffic engineering and queuing theory are also addressed. [ABSTRACT FROM AUTHOR]

Intersection control has an important role in the management of urban traffic to ensure safety, high traffic flow and to prevent congestion. Recently, a growing body of literature has been reported on the theme of non-signalised intersection control in which traffic lights are replaced with intelligent road side units. Data from several studies suggest that non-signalised control could reduce vehicle delays and fuel consumption significantly whilst ensuring safety. However, there is little published data on the impact of the mixed driving behaviour with human-driven vehicles and autonomous vehicles. This paper investigates the emerging role of connectivity and vehicle autonomy in the context of traffic control under the mixed driving behaviour scenario. The concepts of vehicle-to-infrastructure (V2I) communications and multi-agent systems are central to achieving a robust and reliable traffic-light-free intersection control. Comprehensive computer simulation results on a four-way intersection indicate over 96% reduced average vehicle delay and 37% less fuel consumption with the non-signalised control solution compared to the traffic light control. The outcome of this study offers some important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, in order to make more efficient real-time decisions about traffic conditions, whilst ensuring a higher degree of safety. [ABSTRACT FROM AUTHOR]

The use of modeling and simulation aids in deriving many decisions related to transportation planning and traffic operations. Representing the real systems via simulation allows exploring system behavior in an articulated way, which is often impossible in the real world. In this paper, a simulation-based framework is presented to evaluate the impact of congestion charging on daily activity plans of the individuals. Personal decision to accept the congestion charges is evaluated by comparing the value of time with congestion charge. Value of time varies throughout the day depending upon the time pressure at any moment exerted by preceding and succeeding activities. Time pressure during an activity increases if available time for that activity is insufficient to attain the perceived utility. Daily activities in the schedules are modeled using bell-shaped marginal utility that results in sigmoid utility. A model is presented which derives the activity-specific parameters of the marginal utility function for the specific individual. To examine value of time of each person, the congestion charging is applied where personal willingness-to-pay is determined by comparing the ratios of cost to utility for original and adapted schedules. A large-scaled microsimulation of the modeled framework is used to simulate the whole population, which is created by FEATHERS, an operational activity-based model for Flanders, Belgium. The results of the simulation show that the number of individuals who avoid the congestion charges by adapting their schedules is almost three times the number of those who agree to pay it. The proposed framework can be useful to evaluate the tradeoff between value of time and costs where flexibility in selection of time defines the variability in cost. [ABSTRACT FROM AUTHOR]

This paper models the relationship between vehicle miles traveled (VMT) on local and collector roads with an objective to predict local road VMT by using collector road VMT. Through a continuous approximation method typically used for vehicle routing, it first analytically reveals this relationship mainly as a function of roadway density ratios between multiple roadway classifications. This structural relationship suggests regression equations using density ratios or logarithmic values of them as the explanatory variables. The use of regression equations enables to account for varying spatial distributions of roadways and demand through parameter calibration. The proposed regression equations are proved good fits through computer simulation using distinct community road network topologies. In addition, practical data from Hennepin County of Minnesota, U.S.A. that encompasses Minneapolis indicates that our developed regression equations can work well. [ABSTRACT FROM AUTHOR]

Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow characteristics including safety condition on two-lane freeway is of great significance in planning, design, and management of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, density, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simulation model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continuous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient. [ABSTRACT FROM AUTHOR]

The traditional vehicle mobility is restricted to the driver habits and the line-of-sight. The new-emerging connected vehicles enable information exchange with each other at vicinity, which will undoubtedly bring a greatly positive effect on the vehicle mobility pattern. In this paper, we propose to modeling and analyzing the vehicle mobility patterns respectively driven by traditional drivers and connected drivers. We perform the extensive simulations to explore the statistical differences between two mobility patterns and the underlying reasons through comparing to the real vehicle trace datasets. The results show that they behave quite diversely at the concerned aspects, e.g. degree distribution, clustering coefficient, topological shortest path, topological coefficient, and vehicle density, and also we find that the connected vehicles are well distributed and contribute a relieved traffic congestion situation. [ABSTRACT FROM AUTHOR]

In recent years, the topic of car-following has experimented an increased importance in traffic engineering and safety research. This has become a very interesting topic because of the development of driverless cars (Google driverless cars, ). Driving models which describe the interaction between adjacent vehicles in the same lane have a big interest in simulation modeling, such as the Quick-Thinking-Driver model. A non-linear version of it can be given using the logistic map, and then chaos appears. We show that an infinite-dimensional version of the linear model presents a chaotic behaviour using the same approach as for studying chaos of death models of cell growth. [ABSTRACT FROM AUTHOR]

Existing traffic simulations often consider normative driver behavior. Drivers do not always use physically delineated lanes: sometimes drivers use the entire road surface. Thus, current traffic simulations do not reproduce all observed urban and suburban traffic phenomena. To improve the validity of urban and suburban traffic simulations, we propose to consider driving context and driver behavior in terms of occupied space. We endow driver agents with an ego-centered representation of the environment based on the concept of affordances and virtual lanes. Affordances thus identify the possible space occupation actions afforded by the environment and by other agents. The proposed model was implemented using our ArchiSim tool. We show that this model is more efficient and realistic than existing models. The experiments also reproduce real traffic situations and compare simulated data to real data. [ABSTRACT FROM AUTHOR]

To understand how the jamming on real communication networks depends on node capacity, we study the traffic model with heterogeneous node capacity. In this model, each movable packet takes a biased random walk and the capacity of a node with degree k is given as C( k) ∼ k with a tunable parameter x. Each packet disappears when it arrives at the preassigned target node. We analytically and numerically show that the transition from the free-flow phase to the jammed phase occurs when the balance between the packet generations and removals is broken. The balance breaking condition for the jamming is analytically determined by the competition between C( k) and the average number of packets on a node of degree k, m( k), in the free-flow phase. Based on the analytic arguments, we find that there exist three different jamming patterns depending on C( k). The analytic conjectures for jamming patterns are verified by numerical simulations. [ABSTRACT FROM AUTHOR]

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work. [ABSTRACT FROM AUTHOR]

Vehicular sensor networks (VSNs) have emerged as a new technology, which combines wireless communication offered by vehicular ad hoc NETworks with sensing devices installed in vehicles or in roadsides. VSN creates a huge opportunity to extend the roadside sensor infrastructure of existing traffic control systems. The main issues in VSNs are inherent in the data collection and its dissemination in such a large-scale network, characterized by frequent topology changes and network partitioning. This paper proposes a new multi-hop data collection and dissemination protocol based on data replication approach. The aim is an efficient collection and dissemination of data from a defined geographic area. Differently from other data collection protocols for VSNs, which have for purpose to collect data for traffic control applications or monitoring applications, our proposed protocol called Collaborative Data Collection Protocol (CDCP for VSNs) focuses on collecting data to be used in non-delay tolerant applications, so CDCP aims to collect the requested data within an optimized delay, but also, focuses in making this data accessible for other potential requests. Simulation results show the effectiveness of our proposed protocol. [ABSTRACT FROM AUTHOR]

Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area. [ABSTRACT FROM AUTHOR]

In this paper, dynamic traffic load is considered to determine optimal location of base station (BS) using evolutionary optimization algorithms. The various parameters such as site coordinates (x, y), transmitting power, height and tilt are taken as design parameters for BS placement. Coverage maximization and cost minimization are considered as two conflicting objectives with inequality constraints such as handover, traffic demand and overlap. RGA and MNSGA-II algorithms are used to solve single objective and multiobjective BS placement problem respectively. A $$2 \times 2\, \text{ km}^{2}$$ synthetic test system is discretized as hexagonal cell structure for simulation purposes. Receiving field strength for all service testing points is calculated using simulations and path loss is calculated using Hata model. In dynamic traffic model, both vehicle and pedestrian movements in up and side directions are considered. Dynamic movement is achieved by randomly moving vehicles and pedestrians for a fixed speed in each sample time. The results show that the RGA is able to determine the optimal BS location after considering the dynamic traffic load and satisfying inequality constraints for both coverage maximization and cost objectives. MNSGA-II algorithm gives well distributed pareto-front for the multiobjective BS placement in single simulation run. The simulation results reveal that the proposed dynamic traffic model is suitable for the real world BS placement problem. [ABSTRACT FROM AUTHOR]

Traffic is highly influenced by network structure and human behaviour. Small changes in the human behaviour can lead to huge changes in the load of a traffic network. Current transportation models do not, and most of them cannot, research such random behaviour but always calculate a steady state. In our multi-agent transport simulation, we frequently observe seemingly random “network breakdowns”, huge traffic jams that spread over a big part of the network, making a normal traffic flow impossible. This paper describes the investigations that were performed on the results of our large-scale multi-agent transport simulations in an attempt to contribute to the better understanding of the dynamic processes in such simulations and, hopefully, better understanding and modelling of the real-world. [ABSTRACT FROM AUTHOR]

This paper describes a multi-agent coordination mechanism applied to intersection simulation situations. In a goal of urban traffic simulation, we must consider the dynamic interactions between autonomous vehicles. The field of multi-agent systems provides us some studies for such systems, in particular on the coordination mechanisms. Conflicts between vehicles (i.e. agents) are very frequent in such applications, and they may cause deadlocks, particularly at intersections such as crossroads. Our approach is based on the solving of two player games/decision matrices which characterize three basic situations. An aggregation method generalizes to n-player games for complex crossroads. The objective of this approach consists in searching basic two-player matrices for solving n-agent problems. To explain the principle, we describe our approach for a particular case of crossroad with three agents. Finally, the obtained results have been examined via a tool of road traffic simulation. ARCHISIM. We assume also that the global traffic replicates the behavior of agents in different situations. [ABSTRACT FROM AUTHOR]

Using computer simulations, we show that metastable states still occur in two-lane traffic models with slow to start rules. However, these metastable states no longer exist in systems where aggressive drivers ( which do not look back before changing lanes) are present. Indeed, the presence of only one aggressive driver in the circuit, triggers the breakdown of the high flow states. In these systems, the steady state is unique and its relaxation dynamics should depend on the lane changing probability pch and the number of aggressive drivers present in the circuit. It is found also that the relaxation time τ diverges as the form of a power-law: τ∝pch -β, β=1. 89.40.+k [ABSTRACT FROM AUTHOR]

Background: Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin-destination data and have difficulties when phenomena should be investigated that have an effect on the origin-destination matrix.Methods: A macroscopic traffic model is introduced that is novel in the sense that no origin-destination data are required as an input. This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points. The simulated road utilization and resulting congestion is compared to traffic data to quantitatively evaluate the results. Traffic jam avoidance behavior is included in the model in several variants, which are then all evaluated quantitatively.Results: The described model is applied to the City of Graz, a typical European city with about 320,000 inhabitants. Calculated results correspond well with reality.Conclusions: The introduced traffic model, which uses mobility data instead of origin-destination data as input, was successfully applied and offers unique advantages compared to traditional models: Mobility behavior data are valid for different systems, while origin-destination data are very specific to the region in question and more difficult to obtain. In addition, different scenarios (increased population, more use of public transport, etc.) can be evaluated and compared quickly. [ABSTRACT FROM AUTHOR]