Traffic Flow Simulation Research Articles

A computer simulation of traffic flow with on-street parking and drivers’ behaviour based on cellular automata and a multi-agent system

Urban traffic is becoming more and more intensive, but the amount of space available for parking along city streets is not increasing at a rate commensurate with the rising number of new vehicles. This study looks at on-street/curb car parks, i.e., parking spaces located along streets or in one lane of a street. This article describes a simulation of the behaviour of various drivers in the process of on-street parking, and the impact of such parking on traffic flow, speed and travel time in the studied street fragment. This study is the first among other research studies on curb and on-street parking to consider driver behaviour such as patience, perceptivity and the use of indicators (turn signals). It also considers the impact of vacant parking-space marking. The study was performed on the basis of field research data. Drivers’ behaviour was simulated in the form of agents’ logic in a multi-agent system. The structure of the road, car park and vehicle traffic were reproduced on the basis of cellular automata, extending the known and proven Nagel-Schreckenberg, STCA, F-STCA and other models. Research results are helpful in teaching motorists how to park on-street or on the curb. Paying attention to the aspects of effective and streamlined parking may significantly improve traffic flow efficiency.

Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models

Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies.

Effects of vehicle gap changes on fuel economy and emission performance of the traffic flow in the ACC strategy.

This paper focuses on the effects of vehicle gap changes on fuel and emission performance of the simulated traffic flow in the adaptive cruise control (ACC) strategy. Firstly, the close correlation of vehicle gap changes and the host car's behaviors was explored with the measured car-following data. Secondly, the correlation between the host car's velocity and vehicle gap changes with different memory steps was also explored to develop the nth car’s optimal velocity function. Thirdly, a microscopic traffic simulation program was created for analyzing the traffic flow evolution process and approximately estimating the fuel consumptions and exhaust emissions. As a result, it was seen that vehicle gap changes with memory significantly affect fuel economy and emission performance of the simulated traffic flow in the ACC strategy, which can result in low fuel consumptions and exhaust emissions. This study is an incremental step forward for designing the control strategy of the ACC system.

Traffic state evaluation and intersection-movement-based incidents detection of expressway network

ABSTRACTCurrently, the national expressway has more than 120,000 kilometers, ranking first in the world. Unfortunately, the attendant problems such as the traffic congestion, traffic accidents, environmental pollution, and other negative effects also increased rapidly almost day by day. In this article, the mesoscopic traffic flow simulation model has been improved by inputting the expressway data. The estimated method of the expressway network running state is proposed, which takes approach proportion, or choice probability of intersection movements, as input information. Upon the designed mesoscopic traffic simulation process, the traffic accidents detection method is put forward under the condition of lacking the real-time data on road. The real expressway network of Shandong province and the toll data are used to test the validity of the proposed method. The results show that the proposed model and method can be more accurate in estimating the real-time network running state and the relevant macroparameters, such as the traffic flow speed, the averaged density, and flow. It is also feasible and effective to detect traffic accidents on road sections of the network. The proposed model and method can help expressway management department publish information, lay detectors, and conceal accident cover-up by the subordinate departments.

Highway Charging Station Plan Based on Dynamic Traffic Flow Simulation

The reasonable arrangement of the charging station, the infrastructure used to charge the electric vehicle, is vital for the promotion of electric vehicles. As for highways, a highway charging station planning method based on the dynamic traffic flow simulation is proposed in this paper. First, the running and charging demands of electric vehicles on the highways are analyzed; second, considering the interests of investors and vehicle owners, a mathematical model is constructed to specify the objective function; thirdly, the dynamic traffic flow simulation method suitable for the highway condition is proposed; finally, a method is provided, the validity and practicability of this method are verified with examples, and its effects on the photovoltaic consumption are analyzed further in this paper as for the planning result.

The difference of drivers' anticipation behaviors in a new macro model of traffic flow and numerical simulation

A valuable consideration is inserted into car-following theory to investigate the difference of drivers' anticipation. Furthermore, a fresh macro traffic model involving the difference of the drivers' anticipation is deduced from the car-following model by applying transformation relationship from micro to macro variables. And this effect resulted from the difference of the drivers' anticipation is observed via numerical simulation. The results show that the difference of drivers' anticipation has a negligible impact on traffic flow.

A German Passenger Car and Heavy Vehicle Stock Model: Towards an Autonomous Vehicle Fleet

Automated vehicles are becoming a reality. Many pilot projects have already begun demonstrating the technological capabilities, as public authorities now allow the testing of automated vehicles in real traffic. To smooth the transition from a conventional to an automated fleet, effective fiscal and regulatory policies must be developed by governmental agencies. But at what rate will automated vehicles actually be adopted, and what automation technology will be available for use in new cars joining the national fleet? A national vehicle stock model can be used to answer these questions and to observe the aggregate impact of governmental policies on individual vehicle purchase decisions. In this paper, we present a passenger car and heavy vehicle stock cohort model that forecasts the diffusion of automation technology in Germany. The model uses national data on vehicle stock and vehicle utilization patterns on German freeways and predicts market shares of generic automation levels in predefined instances of a trend scenario. Results point toward market saturation of automated vehicles beyond 2050, with almost 90% of the passenger car fleet being classified as at least partially automatized by this date. The results also suggest that technology diffusion will be faster in the heavy vehicle fleet than in the passenger car fleet. This implies a positive correlation between emission-linked road user charges for heavy vehicles on the freeway network and the renewal rate of the heavy vehicle fleet. The forecast shares of automated vehicles can be used as an input for traffic flow simulations or as a basis for those infrastructure measures and traffic policies that are sensitive to the share of automated vehicles.

Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

This paper shows how the recent breakthroughs in reinforcement learning (RL) that have enabled robots to learn to play arcade video games, walk, or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems. We present the first use of RL for the control of systems modeled by discretized non-linear partial differential equations (PDEs) and devise a novel algorithm to use non-parametric control techniques for large multi-agent systems. Cyberphysical systems (e.g., hydraulic channels, transportation systems, the energy grid, and electromagnetic systems) are commonly modeled by PDEs, which historically have been a reliable way to enable engineering applications in these domains. However, it is known that the control of these PDE models is notoriously difficult. We show how neural network-based RL enables the control of discretized PDEs whose parameters are unknown, random, and time-varying. We introduce an algorithm of mutual weight regularization (MWR), which alleviates the curse of dimensionality of multi-agent control schemes by sharing experience between agents while giving each agent the opportunity to specialize its action policy so as to tailor it to the local parameters of the part of the system it is located in. A discretized PDE, such as the scalar Lighthill–Whitham–Richards PDE can indeed be considered as a macroscopic freeway traffic simulator and which presents the most salient challenges for learning to control large cyberphysical system with multiple agents. We consider two different discretization procedures and show the opportunities offered by applying deep reinforcement for continuous control on both. Using a neural RL PDE controller on a traffic flow simulation based on a Godunov discretization of the San Francisco Bay Bridge, we are able to achieve precise adaptive metering without model calibration thereby beating the state of the art in traffic metering. Furthermore, with the more accurate BeATS simulator, we manage to achieve a control performance on par with ALINEA, a state-of-the-art parametric control scheme, and show how using MWR improves the learning procedure.

Simulation of Traffic Flow to Analyze Lane Changes on Multi-lane Highways under Non-lane Discipline

The present study uses the microscopic traffic simulation flow model VISSIM for generating traffic flow data to obtain the essential parameters. Study calibrated and validated the VISSIM model based on the field data. Further, lane change behaviour is analyzed with homogeneous vehicle type traffic on four-lane, six-lane and eight-lane divided highways sections through VISSIM simulation model. The study finds the number of lane changes depends on traffic volume as well as on a number of lanes provided for a direction of travel. Lane change data was correlated with traffic volume, and the third-degree polynomial trend was found to be fitted on each type of simulated highway sections. A maximum number of lane changes and lane change at the capacity level of the volume are also quantified on simulated sections of four-lane, six-lane and eight-lane divided highways.

Design of Traffic Flow Simulation System to Minimize Intersection Waiting Time

This paper designs a traffic simulation system for minimizing intersection waiting time. We use SUMO simulator which is widely used as a traffic flow simulation tool for traffic flow simulation. Using the SUMO simulator to set the route from the source to the destination and measuring the time required when using the existing intersection signal system. Through this simulation, we want to measure how much the proposed system can minimize the waiting time. In order to minimize the intersection waiting time, it is assumed that there is a loop sensor that can recognize whether there is a waiting vehicle in each direction of the intersection. Using this information, a signal lamp is used as a waiting signal in the case of a direction in which there is no waiting vehicle, and a driving signal is given in the case of a waiting vehicle or an entering vehicle. In this paper, we try to reduce the time required for vehicles to arrive at their destination by making the traffic flow smoothly without any expense such as road expansion through the limited system.

Simulation of traffic flow in unsignalization intersection using computer software SIDRA in Baghdad city

Computer simulation is a vital means for the examination of expressways and urban lanes and streets. Transportation experts concentrate on the arrangement and dissemination of traffic jams on roadways. The objective of this study is to calculate and analyses the travel time, delay time, degree of saturation, and level of service and travel speed in Jordan intersection in Baghdad, Iraq using Sidra software. The number of vehicles passing Jordan intersection was recorded by the author of this work from 7am to 3pm for four days. A simulation model has been used to assess the performance of the current intersection. Results demonstrated that the level of service (LOS) for Jordan intersection is D with (35 sec/veh) average delay and degree of saturation (0.996 v/c). It was concluded that Jordan intersection needs further developments like using intelligent transportation system application (ITS) to regulate traffic signals, whereby reducing traffic jam, using closed circuit TV (CCTV) might assist traffic office in identifying jam point to reduce traffic jam.

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

By widely introducing information technology tools in the field of traffic control, it is possible to increase the capacity of hubs and reduce vehicle delays. Adaptive traffic light control is one of such tools. Its effectiveness can be assessed through traffic flow simulation. The aim of this study is to create a simulation model of a signal-controlled intersection that can be used to assess the effectiveness of adaptive control in various traffic situations, including the presence or absence of pedestrian traffic through an intersection. The model is based on a numerical experiment conducted using the Monte Carlo method. As a result of the study, vehicle delays, queue length and duration of traffic light cycles are calculated subject to different intensities of incoming traffic flows, and the presence or absence of pedestrian traffic.

Traffic Flow on Single-Lane Road Networks: Multiscale Modelling and Simulation

This paper addresses the problem of modelling and simulation of the vehicular traffic flow in a road network by adopting a measure-theoretic framework. This allows to describe the system from both, a microscopic and a macroscopic point-of-view simultaneously, in the sense that two scales co-exist and are coupled, namely a set of moving agents and an average density. The resulting mathematical model consists of a strongly coupled PDE-ODE system with non-local interactions. Within this work we specify this multiscale model for a single-lane roundabout, that is, an example of a road network. Then, we focus on the computational aspects in order to develop a setup for numerical simulations and to analyse the results of some experiments. This multiscale simulator can reproduce phenomena that characterize the real physical system, e.g. self-organization effects, and thus it can be adopted as an environment for control algorithms benchmarking or as a tool for simulation-based traffic flow control and optimization.

Analysis on Sag Bottleneck Phenomena Based on Multiclass Traffic State Estimation

Abstract This study proposes a novel technique for analyzing traffic dynamics based on multiclass traffic state estimation. One of the most common causes of bottlenecks on Japan’s roads are sags. Considerable scientific attention has been paid to the bottleneck phenomena at sags in terms of both their microscopic and macroscopic aspects. However, the mechanisms of traffic breakdown at sags are not understood in detail yet. This paper presents a data assimilation system using a particle filter in which online observations from fixed detectors and probe vehicles are combined with multiclass traffic flow simulations to analyze the traffic dynamics that contribute to congestion at sags. The application of a particle filter enables the monitoring of the hidden traffic state described by the unobservable parameters of traffic flow models. On the application of this method to an existing sag bottleneck section, we found that i) the estimation results are a good fit to observation data from both fixed detectors and probes, ii) the integrated use of multiple data source enables estimation accuracy to be improved, iii) the traffic capacity of the upgrade section is lower than that of the other sections and this tendency is more marked for heavy vehicles than for regular vehicles.

Effects of traffic emission reduction on urban air quality episode using WRF/Chem

Traffic emission control strategies have been tested in order to reduce the effects of traffic on pollution concentrations in Madrid (Spain) during an air quality episode with very large NOx concentrations. The meteorology-chemistry model WRF/Chem allows forecasting these effects with high spatial resolution (1 km). It was necessary to develop very detailed emission inventories with a bottom-up methodology. For traffic emissions, the traffic flow simulation model SUMO has been applied, using the real time traffic counters data, Madrid vehicle fleet distribution, and emission factors from EMEP-CORINAIR Tier 3 methodology. The base or control simulation has been compared with data from the Madrid air quality monitoring network. The control simulation reproduces satisfactorily the high NO2 concentration values. The traffic reduction strategies which were taken on 28 and 29 December 2016, did not contribute substantially to improve the air quality in Madrid.

A multi-criteria analysis framework including environmental and health impacts for evaluating traffic calming measures at the road network level

ABSTRACTRecent interests in both vehicle emissions and public health have facilitated the development of more eco-friendly transportation systems. This study developed a multi-criteria evaluation framework to evaluate the effectiveness of traffic calming measures (TCMs) from the various perspectives at the road network level; operational efficiency, traffic safety, environmental and health impacts. The proposed methodology employs four-step sequential simulation experiments, including driving, traffic flow, emissions, and air dispersion simulations. The results obtained from these four simulations are used to evaluate the effectiveness in terms of safety and operational efficiency in addition to environmental and health impacts. A multi-criteria value function based on the weights estimated from the analysis of an analytical hierarchy process (AHP) is applied in the evaluation framework. As an application, chicanes and speed humps widely implemented in Korean school zones were evaluated at the road network level. The proposed simulation-based approach is expected to be effectively used for the decision-making process in selecting better alternatives for TCM.

Forecasting of Congestion in Traffic Neural Network Modelling Using Duffing Holmes Oscillator

Forecasting of congestion in traffic with Neural Network is an innovative and new process of identification and detection of chaotic features in time series analysis. With the use of Duffing Holmes Oscillator, we estimate the emergence of traffic flow congestion when the traffic load on a specific section of the road and in a specific time period is close to exceeding the capacity of the road infrastructure. The orientated model is validated in six locations with a specific requirement. The paper points out the issue of importance of traffic flow forecasting and simulations for preventing or rerouting possible short term traffic flow congestions.

Wpływ odległości między pojazdami na czas i prędkość przejazdu w symulowanym ruchu

The paper presents an analytical derivation of the dependence between vehicle speed and traffic density in urban traffic conditions. It was shown that the speed function is hyperbolic and depends on the density of traffic flow, the average vehicle length and the value of expected distances between vehicles E(T) expressed in time units. A number of computer simulations were performed on the basis of a defined relationship in order to study the effect of the E(T) value on the parameters of simulated traffic flow. The average speed and time of traveling the route from the starting point to the destination were tested in the conducted simulations. It was proved that the change in the E(T) parameter in a range between 1 s to 4 s results in a change in the average travel time from 6.6% to 35.6% and a change in average speed from 3.0% to 18.0%, depending on the number of vehicles in the simulation.

Unifiable multi-commodity kinematic wave model

In the literature, many kinematic wave models have been proposed for multi-class vehicles on multi-lane roads; however, there lacks an explicit model of unifiable multi-commodity traffic, in which different commodity flows can have different speeds and violate the first-in-first-out (FIFO) principle, but there exists a speed-density relation for the total traffic. In this study, we attempt to fill the gap by constructing and solving a unifiable multi-commodity kinematic wave model. We first construct commodity speed-density relations based on generic generating functions. Then for two commodities we discuss the properties of the unifiable kinematic wave model and analytically solve the Riemann problem with a combination of total and commodity kinematic waves. We propose a unifiable multi-commodity Cell Transmission Model (CTM) with a general junction model for numerical simulations of network traffic flows, which are unifiable but may violate the FIFO principle. We prove that the CTM is well-defined under an extended CFL (Courant et al., 1928) condition. With examples we verify the consistency between the analytical and numerical solutions and demonstrate the convergence of the CTM. We conclude with several follow-up research directions for unifiable multi-commodity kinematic wave models.

避航を考慮した海上交通シミュレーション(海上交通流シミュレーションモデル, 海上交通工学)

避航を考慮した海上交通シミュレーション(海上交通流シミュレーションモデル, 海上交通工学)