Microscopic Traffic Simulation Research Articles

Impact of navigation apps on congestion and spread dynamics on a transportation network

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to known fastest paths. Key traffic metrics, including flow, density, speed, travel time, delay time, and queue lengths, are assessed to evaluate the outcomes. Additionally, we explore congestion propagation at various levels of navigation app adoption. To understand congestion dynamics, we apply a susceptible–infected–recovered (SIR) model, commonly used in disease spread studies. Our findings reveal that traffic system performance improves when 30–60% of users follow dynamic routing. The SIR model supports these findings, highlighting the most efficient congestion propagation-to-dissipation ratio when 40% of users adopt dynamic routing, as indicated by the lowest basic reproductive number. This research provides valuable insights into the intricate relationship between navigation apps and traffic congestion, with implications for transportation planning and management.

Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration

This study analyzes the impact of autonomous-vehicle (AV) market-penetration rates on traffic-flow safety using a genetic algorithm. We set up a microscopic traffic-simulation scenario on a 640 m section of the US I-101 freeway using VISSIM, a microscopic traffic-simulation software. The results of analyzing the number of conflicts according to the introduction rate of autonomous vehicles showed that the number of conflicts increased as the introduction rate increased up to 30%, and then decreased from 40% or more. In this study, it was assumed that autonomous vehicles can avoid dangerous situations, so it is judged that this is the result of an increase in the traffic volume of autonomous vehicles and a decrease in the traffic volume of conventional vehicles. When planning an exclusive lane for autonomous vehicles, it is judged that it is desirable to install two exclusive lanes on the left side until the introduction rate of autonomous vehicles reaches 30%. When the introduction rate of autonomous vehicles is 40–90%, the risk of accidents between autonomous vehicles and conventional vehicles decreases, and the traffic volume of autonomous vehicles is higher than that of conventional vehicles. Therefore, it is judged that it is desirable to operate a mixed road where autonomous vehicles and conventional vehicles can drive together rather than operating an exclusive lane for autonomous vehicles.

CycLight: Learning traffic signal cooperation with a cycle-level strategy

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Traditional RL-based step-by-step traffic signal controllers, while precise and responsive, demand high computational resources for frequent data exchanges and can increase traffic accident risks due to disordered phase switching. Different from the step-by-step control strategy aforementioned, the proposed CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. Moreover, a decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, Simulation of Urban MObility (SUMO). Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

Towards a fundamental understanding on Microscopic Transportation Simulation of Autonomous Delivery Robots

A large number of studies were focused into the adoption of ADRs in the current transportation and logistics, safety in practical applications, and deployment of advanced robotic technology. Only a few contributed to the simulation of microscopic transportation for autonomous delivery robots. Many researchers emphasized the importance of overcoming modelling gaps and covering the scope of simulation as realistically as possible in order to obtain reliable results from the simulation. The versatility of the technology, the nature of the logistics and components of ADR in the real world, and other challenges present challenges to the transportation modeller when it comes to thinking about the possible representation of ADRs in the simulation environment. The gradual development of both computational capacity and underlying behavioural patterns in microscopic traffic simulators opens the door for researchers to develop realistic ADRs model in the microscopic transportation simulation to investigate road interaction and the resulting impacts. This review explored and organized the currently available technologies, operational methods, and simulation components that need to be considered when modelling ADRs in microscopic traffic simulation. Furthermore, this review provides a basic understanding for modellers to address the potential challenges to cope on recent technological developments in ADR modelling and simulation.

Modelling two-dimensional driving behaviours at unsignalised intersection using multi-agent imitation learning

Traffic at unsignalised intersection usually involves complex interactions. It is critical to explicitly model the two-dimensional driving behaviours and capture the interactions among vehicles. However, little effort has been made to develop microscopic traffic simulation models at unsignalised intersections, which can generate realistic vehicle trajectories. To fill this gap, this study aims to develop a two-dimensional data-driven simulation model at an unsignalised intersection based on multi-agent imitation learning for generating realistic trajectories of vehicles crossing the intersection and macroscopic traffic characteristics. We propose a multi-agent adversarial inverse reinforcement learning model with four policies (MA-AIRL-4) to separately learn the driving behaviours with different directions (East-bound, West-bound, South-bound, and North–bound) by capturing the interactions between the vehicles. Using the vehicle trajectories data extracted from an unsignalised intersection of the open-source Interaction Dataset, we evaluate the performance of the proposed model by comparing it with several bench-marking models, i.e., MA-AIRL-2 model with two policies (i.e., one policy for West-East and North-South direction, respectively), MA-AIRL-1 model with one policy for all vehicles, three corresponding multi-agent generative adversarial imitation learning (MA-GAIL) models with four policies, two policies and one policy, respectively, and long short-term memory (LSTM) model. The results demonstrate the superiority of the proposed MA-AIRL-4 model in generating accurate vehicle trajectories and reproducing realistic speed distributions. The interpretation of the recovered reward function also indicates the effective learning of the proposed model and provides insights into the learned behaviours.

Deep Q‐network learning‐based active speed management under autonomous driving environments

AbstractEfficient traffic safety management necessitates real‐time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time‐to‐collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

Traffic simulation-emission modelling to evaluate impact of signal cycle on automobile emissions

Vehicular traffic comprises different types of vehicles with varying static and dynamic characteristics, under heterogeneous traffic conditions in India. The continuous growth of vehicles results in high congestion and becoming the main source of air pollution. The reduction in emissions is directly linked to the reduction in traffic congestion. At urban corridors, higher concentration of pollutants is observed near the traffic signals because of the influence of traffic signal operations on vehicular speed. The present research is carried out with the principal objective as the study of the effect of intersection control on emissions in the urban intersection of a metropolitan city. Microscopic traffic simulation models have been used to simulate real traffic conditions, and effectively represent the extensive performance of vehicles associated with speed and acceleration states. The simulation model is revised with optimum signal cycle time considering the signal phase system shows a substantial reduction in automobile emissions.

N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control

The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Safety Impact of Introducing Automated Road Debris Removal System in South Korea

Road debris should be removed quickly because it can cause road damage and traffic accidents. An automated road debris removal system—called ROBOS—was developed in South Korea to remove road debris automatically and quickly. It can be mounted and used on vehicles over 3.5 tons. The effects of ROBOS on the traffic flow need to be studied before it can be used on actual roads. In this study, these effects were investigated by performing driving and microscopic traffic simulations. The former can be used to analyze drivers’ reactions to new road equipment and driving behavior when detecting road debris. Driving behavior results that met the research purpose were employed in the microscopic traffic simulation to imitate reality. Finally, the number of conflicts and the conflict rate were compared between manual and ROBOS vehicle road debris removal scenarios to analyze traffic safety. The results showed that the overall traffic safety was low when road debris was present in curved sections, where it was generally difficult for the driver to easily detect it, and in the first lane and third lane, where lane changes were limited. Assuming that a ROBOS vehicle was used in these sections, the number of conflicts and the conflict rate can be significantly reduced. This study evaluated traffic safety after the use of a ROBOS vehicle input and obtained basic information needed for decision-making in future road debris removal work, such as the priority of input when choosing between manual removal and removal by a ROBOS vehicle.

Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions.

Traffic flow impact of mixed heterogeneous platoons on highways: an approach combining driving simulation and microscopic traffic simulation

The connected automated vehicle (CAV) platoons are expected to improve the operational performance of the transportation system. Existing platoon testing processes have used the format of mixed heterogeneous platoons (MHPs), where the leader vehicles are connected human-driven vehicles (CHDVs) and the follower vehicles are CAVs. The impact of this type of MHP on existing highway traffic flow is more complex due to the presence of human drivers, and this impact has yet to be analyzed and quantified. Therefore, this paper takes this type of MHP as the research object and conducts experiments by combining driving simulation and microscopic traffic simulation. The driving behavior of the driver of the MHP's leader vehicle is incorporated into the car-following model by means of a car-following model calibration, and an impact analysis is carried out in terms of multiple dimensions of stability, efficiency, and ecological aspects of the mixed traffic flow under different MHP market penetration rates (MPRs). The results show that MHPs weaken the oscillatory effect of mixed traffic flow and improve mixed traffic flow stability. The MPRs of MHPs are proportional to the road capacity and average speed and antipodal to the average travel time. Also, the MHPs help reduce fuel consumption and pollutant emissions (CO2, CO, NOx, HC, and PMx) during traffic operations. This study can provide theoretical support for the future large-scale application and management of MHPs.

Determining Desired Speeds from Vehicle Trajectory Data

Desired speed or free speed distributions are important input parameters for microscopic traffic flow simulations. Whereas driven speeds can be measured, desired speeds are not detectable for all vehicles because of vehicles constraining each other. An established approach to estimating desired speed distributions is the modified Kaplan–Meier approach, which estimates desired speed distributions based on single vehicle data from stationary detectors. We propose a novel approach to determine desired speeds based on vehicle trajectory data. The proposed approach, as well as the modified Kaplan–Meier approach, is applied to a trajectory dataset recorded on a German freeway. With the modified Kaplan–Meier approach, we observed that the resulting desired speed distributions vary by approximately 5 km/h depending on the position of the stationary detectors. The desired speed distributions obtained from the trajectory-based approach are approximately 5 to 10 km/h higher than those estimated by the modified Kaplan–Meier approach. This difference in results is probably because the trajectory-based approach observes each vehicle over a longer distance rather than just at stationary points. Nevertheless, it can be concluded that the estimation of desired speed distributions is subject to a certain degree of inaccuracy. The analysis of the vehicle trajectory data revealed a notable intra-vehicle instability in desired speeds, with a difference of 5 to 7 km/h observed for 40% of the vehicles between different periods of free driving. These findings should be considered in the context of calibrating microscopic traffic flow simulations.

Safety-oriented urban pavement design and evaluation: integrating microscopic simulation and tyre-pavement friction

ABSTRACT Tyre-pavement friction significantly impacts traffic safety performance, yet integrating these topics is often overlooked in pavement engineering. This paper addresses this gap by utilising microscopic traffic simulation and surrogate measures of safety to assess vehicular conflicts severity in diverse urban scenarios and establish friction thresholds for practical implementation. A pre-construction materials selection approach is proposed, employing a predictive model for tyre-pavement friction. Findings reveal that combinations of fine-graded mixtures and low surface texture mineral aggregates can lead to increased conflict severity in urban environments. Furthermore, a post-construction texture assessment using 3D computer vision was applied for verifying compliance with friction thresholds for asphalt pavement surfaces. This study emphasises the potential for cost-effective integration of traffic simulation and safety analysis to an enhanced urban pavement design and evaluation. By incorporating safety-based tyre-pavement friction considerations, decision-making related to materials selection and friction conditions can be improved, leading to safer urban transportation networks.

A methodology of cooperative driving based on microscopic traffic prediction

We present a methodology of cooperative driving in vehicular traffic, in which for short-time traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we follow a qualitative different microscopic traffic prediction approach developed recently (Kerner and Klenov, 2022). In the microscopic traffic prediction approach used for the planning of the subject vehicle trajectory, no learning algorithms of AI are applied; instead, microscopic traffic modeling based on the physics of vehicle motion is used. The presented methodology of cooperative driving is devoted to application cases in which microscopic traffic prediction without cooperative driving cannot lead to a successful vehicle control and trajectory planning. For the understanding of the physical features of the methodology of cooperative driving, a traffic city scenario has been numerically studied, in which a subject vehicle, which requires cooperative driving, is an automated vehicle. Based on microscopic traffic prediction, in the methodology first a cooperating vehicle(s) is found; then, motion requirements for the cooperating vehicle(s) and characteristics of automated vehicle control are predicted and used for vehicle motion; to update predicted characteristics of vehicle motion, calculations of the predictions of motion requirements for the cooperating vehicle and automated vehicle control are repeated for each next time instant at which new measured data for current microscopic traffic situation are available. With the use of microscopic traffic simulations, the evaluation of the applicability of this methodology is illustrated for a simple case of unsignalized city intersection, when the automated vehicle wants to turn right from a secondary road onto the priority road.

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

Enhancing parameter calibration for micro-simulation models: Investigating improvement methods

Calibrating microscopic traffic simulation models is a prerequisite for simulation applications. This study proposes three novel methods to improve the accuracy and interpretability of the calibration model. The proposed approach involves selecting the calibration parameter, refining the model parameter system, and optimizing the calibration results. The first method expands the single-point mean into a multi-point distribution. The cumulative distribution curve of delay was selected as the calibration parameter. The second method divides the parameter system into global and local parameters. Global parameters were calibrated using NGSIM measured data, and local parameters were calibrated through intelligent algorithms. The third method proposes a methodology of parameter clustering recursion based on the genetic algorithm results, with information entropy selected as the analysis index. To evaluate the effectiveness of the proposed optimization methods, this study used NGSIM trajectory data as a case study. Eight simulation schemes based on the three optimization methods were designed, and simulation experiments were conducted using the VISSIM platform. The results show that the accuracy of the multi-point distribution calibration and parameter value optimization method is significantly higher than the default method. Additionally, the optimization method with calibration of both global and local parameters was more consistent with actual driving characteristics. This study provides a theoretical foundation for improving the practical application of traffic simulation technology, which has significant implications for transportation planning and management.

Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach

Human driving behavior significantly affects vehicle fuel economy and emissions on hilly roads. This paper presents an ecological (eco) driving scheme (EDS) on hilly roads using nonlinear model predictive control (NMPC) in a mixed traffic environment. A nonlinear optimization problem with a relevant prediction horizon and a cost function is formulated using variables impacting the fuel economy of vehicles. The EDS minimizes vehicle fuel usage and emissions by generating the optimum velocity trajectory considering the longitudinal motion dynamics, the preceding vehicle’s state, and slope information from the digital road map. Furthermore, the immediate vehicle velocity and angle of the road slope are used to tune the cost function’s weight utilizing fuzzy inference methods for smooth maneuvering on slopes. Microscopic traffic simulations are used to show the effectiveness of the proposed EDS for different penetration rates on a real hilly road in Fukuoka City, Japan, in a mixed traffic environment with the conventional (human-based) driving scheme (CDS). The results show that the fuel consumption and emissions of vehicles are significantly reduced by the proposed NMPC-based EDS compared to the CDS for varying penetration rates. Additionally, the proposed EDS significantly increases the average speed of vehicles on the hilly road. The proposed scheme can be deployed as an advanced driver assistance system (ADAS).

Bicycle as a traffic mode: From microscopic cycling behavior to macroscopic bicycle flow

Cities allocate dedicated road space to bicycles in favor of active-mode road users. For urban environments with a mass bicycle volume, bicycle traffic congestion is likely to occur. Hence, a thorough understanding of bicycle traffic flow is necessary for the assessment of cycling infrastructure and the development of traffic management strategies considering cycling efficiency. This study aims to investigate bicycle flow characteristics using microscopic traffic simulation. As bicycle flow performance is subject to the non-lane-based movement strategy and the behavioral heterogeneity among cyclists, various scenarios with different microsimulation settings are evaluated. Ultimately, we derive the functional form fundamental diagrams and macroscopic fundamental diagrams using a curve-fitting approach and an analytical method, respectively. Important macroscopic traffic flow parameters, such as capacity, critical speed, critical density, backward wave speed, etc., are estimated. The results show that lane width, overtaking incentive, and desired speed distribution are factors that affect bicycle flow performance. The distinct features of bicycle flow under different traffic states are identified by discussing the simulation outcome and comparing the estimated flow parameters. The findings can be utilized by future research regarding large-scale bicycle traffic flow modeling and control.

Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.