Traffic Flow Simulation Research Articles

Generating and Calibrating a Microscopic Traffic Flow Simulation Network of Kyoto

Microscopic traffic flow simulations as tools for enabling detailed insights on traffic efficiency and safety gained numerous popularity among transportation researchers, planners and engineers in the first to decades of the 21st century. By implementing a test bed for simulation scenarios of complex urban transportation infrastructure it is possible to inspect specific effects of introducing small infrastructural changes related to the built environment and to the introduction of advanced traffic control strategies. The possibility of reproducing present problems or the transportation services, such as the ones of public bus services is a key motivation of this work. In this research, we reproduce the road network of the city of Kyoto for observing specific travel patterns of public buses such as the bus bunching phenomena. Therefore, a selection of currently available data sets is used for calibrating a cutout of the Kyoto road network of a relatively large extent. After introducing a method for geodata extraction and conversion, we approach the calibration by introducing virtual detectors representing present inductive loops and make use of historical traffic count records. Additionally, we introduce bus routes partially contributed by volunteer mappers (OSM project). First simulation outcomes show numerous familiar (local knowledge) flow patterns.

Evaluation of a Dynamic Lane Assignment on Urban Freeways by Using a Microscopic Traffic Flow Simulation

At a merging section of freeways, the mainline traffic flow conflicts with the traffic flow from an onramp, which often causes traffic congestion. To mitigate this conflict, it is important to dynamically manage the lane utilization on the mainline at bottleneck points. Recently, connected and automated vehicles (CAVs) are expected to be widely used on freeways. Guiding CAVs to change lane to avoid conflicts with merging vehicles may alleviate traffic congestion. In this paper, we propose a novel dynamic lane guidance system that can dynamically manage the lane changes in the merging sections with urban freeway ramps. In the proposed system, CAVs are guided to change lane at the lanes upstream of bottlenecks according to traffic conditions, which are detected by the following two methods: 1) cross-sectional vehicle speed information obtained from traffic detectors and 2) vehicle speed information obtained from driving trajectories based on probe data. Then, the effectiveness of the proposed dynamic lane guidance system is evaluated by a microscopic traffic simulator, Vissim, which is calibrated to reproduce the observed traffic states based on full vehicle trajectory data collected at the section near the Tsukamoto merge area on the Hanshin Expressway Route 11 Ikeda Line in Japan. As a result, the obtained results showed that dynamic lane guidance is effective in reducing overall travel time, thus demonstrating the usefulness of dynamic lane guidance. It was also found that traffic control based on partial vehicle information, such as probe data, is sufficiently effective.

Bidirectional Green Waves for Road Arterials: Optimization and Simulation

Consider a bidirectional arterial road along which several junctions are controlled by fixed-time traffic signals. The driving times between the junctions are far from showing the ideal condition of integer multiples of half the cycle time. At the junctions, separate left-turn phases have to be scheduled. In particular, the two opposite directions of the arterial can but do not need to be scheduled at the same time. This paper provides an integrated mathematical optimization model that maximizes the widths and lengths of the green bands of both directions of the arterial. Within this, the model selects among three predefined signal programs with different phase sequences, and computes optimum offsets for the selected signal programs at each junction. Arranging the left-turn phases before or after the arterial’s through-traffic in a dedicated way provides much better results in the optimization model than always scheduling them at the same time. To validate the optimization result, we implemented a microscopic traffic flow simulation model (using the SUMO simulation package) inspired by certain arterials in the city of Berlin, Germany. With 11 crossings, the result was an average of less than 1.7 stops per car—already including one possible stop at the two entry points of the coordinated arterial. This compares favorably with the average number of at least 3.4 stops per car when the straight traffic in both directions is always scheduled at the same time, that is, in parallel.

Focusing on Driving Modes Rather than Drivers: Toward More Precise and Efficient Car-Following Behavior Modeling

Car-following (CF) behavior is one of the most important driving behaviors. Accurately understanding and modeling CF behavior is essential for traffic flow simulation and user-acceptable advanced driving assistance systems (ADASs). In previous decades, CF models were calibrated based on drivers or trajectories, with short-term changes ignored. Recent studies have indicated that these changes could be caused by occasional irritations or regular switches of driving modes, but there is still a lack of specific understanding of driving modes and how these modes affect simulation accuracy in the reproduction of CF behavior. This paper explored the existence of driving modes and the quantified modeling influence of driving modes. Specifically, we first extracted 4000 high-resolution CF events of 40 drivers from large-scale naturalistic driving data for the discovery of underlying driving modes. Then, we introduced a novel multivariate time series method, Toeplitz Inverse Covariance-based Clustering (TICC), to achieve the segmentation and classification extraction of different driving modes. Finally, calibrated by the CF dataset, the proper cluster number of the driving mode was determined, and a comparison of driving-mode-based modeling (DMBM) and driver-based modeling (DBM) was conducted. The results showed that the driving process could be viewed as five core driving modes, and the DMBM has the potential to bring upwards of a 13% accuracy improvement with fewer parameters.

Dynamic management network system of automobile detection applying edge computing

AbstractDynamic vehicle detection requires the transmission of large amounts of data collected by different types of sensors to the edge computing nodes. This is likely to cause network delays and congestion, affecting the computation of the edge computing nodes and thus posing serious security risks. Therefore, optimizing data transmission between vehicles and edge computing nodes is a new challenge to be addressed in the practical application of edge computing‐based vehicle dynamic detection architectures. The data requirements of VDT for vehicle detection dynamic detection in different environments are considered, the optimization objectives and constraints are analysed, and a deviation detection and greedy algorithm is proposed in this paper to address the problems of long mixed‐integer linear programme solution time and insufficient practical applications, and the performance of the algorithm is evaluated through simulation experiments conducted by simulation of urban mobility, a traffic flow simulation tool, and PreScan, a vehicle simulation test software. The results show that compared with the deviation detection algorithm, the greedy algorithm can reduce the communication overhead by 82.6%–86.2% in all cases and improve the performance by 13.6%–19.5%, which is more suitable for practical applications. The results of this paper contribute to the automation and modernization of vehicle technology management and information transfer.

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers.

HetroTraffSim: A Macroscopic Heterogeneous Traffic Flow Simulator for Road Bottlenecks

Smart mobility is crucial for future smart cities. Traffic simulation software (TSS) is an important tool for efficient planning and management of road networks to achieve this goal. Many TSS tools have been developed for both microscopic and macroscopic homogenous traffic flow. However, only two (SUMO and HetroSim) are applicable to heterogeneous traffic. In this paper, HetroTraffSim is proposed to simulate macroscopic heterogeneous traffic flows at road bottlenecks. It is developed using the Unity3D engine and is based on a second-order traffic flow model. It is evaluated for a 360 m road segment on University Road, Peshawar, Pakistan. This segment contains a 78.5 m bottleneck which causes traffic congestion. This bottleneck is due to the construction of a pedestrian overhead bridge which reduces the three-lane road to two lanes. HetroTraffSim provides normalized traffic velocity, average traffic density, traffic flow, and time, as well as the temporal and spatial evolution of traffic. The results obtained show that a change in the distance headway affects the traffic flow, velocity, and density. Further, HetroTraffSim can be extended to automated traffic flows using raycasting. It can easily be used to create realistic traffic scenarios and the computational complexity is low due to the small-degree polynomials employed. HetroTraffSim can be used by traffic planners to improve traffic flow and public safety.

Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling Method

The rationality of heavy vehicle models is crucial to the structural safety assessment of bridges. To establish a realistic heavy vehicle traffic flow model, this study proposes a heavy vehicle random traffic flow simulation method that fully considers the vehicle weight correlation based on the measured weigh-in-motion data. First, a probability model of the key parameters in the actual traffic flow is established. Then, a random traffic flow simulation of heavy vehicles is realized using the R-vine Copula model and improved Latin hypercube sampling (LHS) method. Finally, the load effect is calculated using a calculation example to explore the necessity of considering the vehicle weight correlation. The results indicate that the vehicle weight of each model is significantly correlated. Compared to the Monte Carlo method, the improved LHS method better considers the correlation between high-dimensional variables. Furthermore, considering the vehicle weight correlation using the R-vine Copula model, the random traffic flow generated by the Monte Carlo sampling method ignores the correlation between parameters, leading to a weaker load effect. Therefore, the improved LHS method is preferred.

Modeling and simulation of traffic flow based on memory effect and driver characteristics

In this paper, an improvement of the full velocity difference model is presented, which comprehensively considers driver characteristics which include aggressive and timid behaviors and memory effect. The stability condition of the improved model is obtained by making use of the small perturbation method. The Burgers, Korteweg–de Vries and modified Korteweg–de Vries equations are derived by means of the reductive perturbation method. Finally, through numerical simulation, the traffic flow evolution process of the improved model under two typical conditions is observed. The first is the starting process, and the second is the propagation process of disturbance in traffic flow. The results of numerical simulation and theoretical analysis demonstrate that the multiple driver characteristics considered in the improved model have important influences on traffic flow stability and the ease of traffic jams, and the model can quickly recover to its original state in case of the disturbance. • A new car following model is presented, which considers memory effect and driver characteristics including aggressive and timid behaviors. • The stability conditions of the extended model are obtained with the use of the small perturbation method. • The Burgers, Korteweg–de Vries and modified Korteweg–de Vries equations are derived by means of the reduced perturbation method. • The starting process and the propagation process of disturbance are carried out, the results are much closer to actual traffic.

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics

The growing food demand, the tendency for organic food, and the adaptation of the e-commerce business model require new food supply chain management approaches. On the one hand, 30% of the world’s produced food is wasted, and CO2 emissions are rapidly growing due to transport. On the other hand, the increasingly complex and dynamic environment is decreasing the effectiveness of food supply chains. Because of these trends, sustainability and resilience are becoming more relevant to food supply chains. Therefore, the objective of this paper is to propose a strategy based on information exchange to improve food quality and decrease the level of CO2 emission in last-mile deliveries of food products. To achieve this goal, an agent-based model of last-mile deliveries was developed. The model simulated traffic flow and traffic accidents as disturbances in the system while measuring the level of CO2 emission and food quality of the network. The simulation compares information sharing between all vehicles in the urban area and without information sharing in four scenarios of the food industry. In practice, information sharing is achieved by using connected vehicle technology. The use of information sharing between vehicles in last-mile delivery processes allows the development of a self-organizing system, which would adapt to disturbances and lead to the development of sustainability in the long run.

Traffic information interpolation method based on traffic flow emergence using swarm intelligence

Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results.

Development of Estimation and Improvement Method for Real World Fuel Economy by Using a Traffic Flow Simulation

Development of Estimation and Improvement Method for Real World Fuel Economy by Using a Traffic Flow Simulation

3-step calibration process of a microscopic traffic flow simulation platform for mixed traffic scenarios

Microscopic traffic flow simulation is used in multiple fields nowadays, resulting in varying objectives and requirements regarding model calibration. Traffic engineers analyse the impact on traffic flow using simulation tools and in automobile industry simulation is used for developing and analysing automated driving functions and their functionalities. We developed a simulation platform with pre-defined applications, allowing operators to test the impact of automated driving on traffic flow by varying mixed traffic scenarios. As calibration of the base case for different domains is a time-consuming process, we present in this paper a 3-step calibration process, which allows efficient model calibration by focusing on macroscopic traffic flow dynamics as well as microscopic driving behaviour.

Simulation Model for Traffic Flow of Projected Recreational Area by River Desna in the City of Bryansk

The paper presents the results of simulation modelling of a transport hub connecting Bezhitsky and Sovetsky districts of Bryansk (Russia). A project for sustainable development of the territory has been designed for the area formed at the intersection of Bezhitskaya, Flotskaya and Ulyanova streets, aimed at creation of a comfortable environment. This project envisages the creation of a special “green” recreation zone that includes access to River Desna. To assess possible changes connected with the prospective commissioning of a new urban facility, evaluation of public accessibility within the given area is required. For this purpose, structuring and analysis of all qualitative and quantitative characteristics of the objects adjacent to the site was carried out with a view to create a traffic flow simulation model. The development of such a model will enable the profile experts to evaluate transport accessibility of the new facility by private and public transport and the expected traffic changes in the specified area.

Simulation model of traffic flow based on agent-based modeling

The work is devoted to traffic flow simulation using agent-based modeling using distributed computing. The role of modeling traffic flows in the modern world is considered. The types of traffic flow modeling are considered. The developed software makes it possible to simulate the movement and interaction of vehicles.

Partial differential grey model based on control matrix and its application in short-term traffic flow prediction

Short-term traffic flow analysis is the core part of the intelligent transportation system, and also the critical basis for traffic management and control system to guide traffic flow. Real-time and accurate short-term traffic flow prediction can be effectively applied to traffic control and guidance, providing reliable and effective information for traffic managers and travelers, and improving travel efficiency. Based on the spatiotemporal characteristics of traffic flow and periodicity of traffic flow, this paper uses partial differential equation variables to reflect the spatiotemporal characteristics of traffic flow, combines the modelling mechanism of partial differential and the grey prediction model organically, and establishes a partial grey prediction model with control matrix by using the partial derivatives of the mean sequence. At the same time, the parameters estimation and modelling steps of the novel model are obtained by matrix analysis and recursion. A set of data is used from two perspectives to verify the proposed model's effectiveness. Experimental results show that the proposed model is stable and effective. Finally, the novel model is applied to the short-time traffic flow simulation and prediction of the highway. The research results show that the novel model is better than other comparison models. The novel model mainly aims at the matrix data form of traffic flow and is applied to general short-term traffic flow prediction. On the one hand, the prediction results of the novel model can provide theoretical basis and basic methods for the traffic management system, traffic data for traffic information system, and new ideas for the traffic flow prediction research. On the other hand, it can help traffic managers solve accurate real-time traffic flow information, provide real-time traffic information for travelers, and help them plan and adjust routes in advance.

The Scanner of Heterogeneous Traffic Flow in Smart Cities by an Updating Model of Connected and Automated Vehicles

The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.

A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.

Development of a cyber-physical-system perspective based simulation platform for optimizing connected automated vehicles dedicated lanes

Recently, many studies have shown that during the transition period of the coexistence of autonomous vehicles (AVs)/connected automated vehicles (CAVs) and human-driven vehicles (HDVs), a dedicated lane (DL) for AVs/CAVs can improve traffic throughput and lower collision risk, which has become a hot topic in the field of intelligent transportation systems (ITS). This study presents an integrated simulation platform for DL optimization from cyber-physical-system (CPS) perspective. The developed simulation platform can be used to qualitatively and quantitatively evaluate the design parameters, operation modes, contingency plan, etc. of DL in a “What You See Is What You Get (Wysiwyg)” style. First, we introduce the architecture and features of the proposed platform, which comprises of SUMO, OMNeT++, and Unity3D. We implemented all experimental scripts for DL optimization in SUMO, including the configuration of the DLs, car-following models, lane-changing models, vehicle departure models, origin–destination demands, and 2D visualization of traffic flow simulation. We used OMNeT++ for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Unity3D is a 3D visualization platform for simulations synchronized with SUMO. Connected with the driving simulator and paired with a virtual reality (VR) helmet, Unity3D can offer a high-fidelity immersive environment for all types of users. We developed a specific communication protocol to achieve data sharing and synchronization of all components of the proposed simulation platform. Second, this paper depicts the functions of the proposed simulation platform in detail, including the modeling of road networks, vehicles, traffic flow, optimal design, and evaluation indicators of DL. Finally, by using the developed simulation platform, we demonstrated and analyzed three cases: (1) without DL vs with DL, (2) inner lane vs outer lane, and (3) without V2I vs with V2I. The experimental results confirm the feasibility of the developed simulation platform, and yield some interesting findings, such as (1) market penetration rates (MPRs) are an important factor in deciding whether to deploy DLs, (2) the number of lane changes when the vehicle enters/leaves the designated lane is the key criterion for determining the placement of the DLs, and (3) the cooperative vehicle-infrastructure system can offer AVs/CAVs with better over-the-horizon perception capabilities, helping to improve the smoothness of traffic flow in the case of abnormal events. The developed simulation platform is expected to offer a new tool for optimal design and evaluation of DLs for AVs/CAvs.