Microscopic Traffic Simulation Model Research Articles

Comparison of the Methodologies for Calculating Expressway Space Mean Speed Using Vehicular Trajectory Information from a Radar Detector

This study was initiated to evaluate the performance of methodologies to estimate the space mean speed(SMS) using the time mean speed(TMS) which was collected from the vehicle detection system(VDS) in expressways. To this end, the methodologies presented in prior studies were firstly summarized. It is very hard to achieve exact SMSs and TMSs due to mechanical and communication errors in the field. Thus, a microscopic traffic simulation model was utilized to evaluated the performance. As a result, the harmonic mean and volume-distance weighted harmonic mean were close to the SMS in the case in which the TMSs of individual vehicles were used. However, when the 30-second-interval aggregated TMS were used, the volume-distance weighted harmonic mean was outstanding. In this study, a radar detector was installed in the Joongbu expressway to collect the SMS. The trajectory of individual vehicles collected from the detector were used to calculate the SMS, which was compared with the estimates using other methodologies selected in this study. As a result, the volume-distance weighted mean was turned out to be close to the SMS. However, as the congestion becomes severe. the deviation between the two speed becomes bigger.

Traffic micro-simulation model for design and operational analysis of barrier toll stations

Abstract The objective of this paper was the development of a microscopic traffic simulation model for design, assessment, and operational analysis of toll stations. A simulation software using VB.NET was created to simulate the stochastic nature of traffic arrival, toll collection time, and driver decision making. The developed simulation model was used to analyze different scenarios of traffic volumes, toll booth capacity, driver types, and configuration of toll station. Results showed that volume per toll lane and method of payment significantly affect the average delay and maximum queue lengths of a toll station. Recommendations on number of toll booths are presented in order to process peak traffic hours without excessive delay times or long queues.

Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions

To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car–car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These models were implemented in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated based on different Measure of Effectiveness (MoE) using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. Capacity is higher when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane (Ex: exclusion of auto-rickshaws, heavy vehicles, auto-rickshaws + heavy vehicles, etc.). The results showed the promise of some measures based on vehicle class, namely, the exclusion of auto rickshaws or auto rickshaws and heavy vehicles. The findings have interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis.

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

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.

Comparison Study of O/D Estimation Methods for Building a Large-Sized Microscopic Traffic Simulation Network: Cases of Gravity Model and QUEEENSOD Method

PURPOSES : The aim of this study was to compare the performance of the QUEENSOD method and the gravity model in estimating Origin-Destination (O/D) tables for a large-sized microscopic traffic simulation network. METHODS : In this study, an expressway network was simulated using the microscopic traffic simulation model, VISSIM. The gravity model and QUEENSOD method were used to estimate the O/D pairs between internal and between external zones. RESULTS: After obtaining estimations of the O/D table by using both the gravity model and the QUEENSOD method, the value of the root mean square error (RMSE) for O/D pairs between internal zones were compared. For the gravity model and the QUEENSOD method, the RMSE obtained were 386.0 and 241.2, respectively. The O/D tables estimated using both methods were then entered into the VISSIM networks and calibrated with measured travel time. The resulting estimated travel times were then compared. For the gravity model and the QUEENSOD method, the estimated travel times showed 1.16% and 0.45% deviation from the surveyed travel time, respectively. CONCLUSIONS : In building a large-sized microscopic traffic simulation network, an O/D matrix is essential in order to produce reliable analysis results. When link counts from diverse ITS facilities are available, the QUEENSOD method outperforms the gravity model.

Online calibration for microscopic traffic simulation and dynamic multi-step prediction of traffic speed

Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.

Exploring emergency areas for medical service using microscopic traffic simulation model

The aim of this study is to set the emergency medical service areas with the emergency medical facilities in Anyang City as a study area, and to analyze vulnerable areas by using microscopic traffic simulation model based on geographic information system (GIS). This study analysed the changes of service areas of four emergency medical facilities, and as a result, it was identified that there is a great difference in the service areas of emergency medical service between the new town and the old town. In view of the simulation results, it was concluded that the difference in the emergency medical service areas due to the differences in the shape of the urban structure and road facilities. On the basis of these results, the vulnerable areas in view of emergency medical service in Anyang City were identified, and the policies to increase accessibility to emergency medical facilities in the vulnerable areas were presented by considering the spatial characteristics of the vulnerable areas.

Calibration protocol for PARAMICS microscopic traffic simulation model: application of neuro-fuzzy approach

This study investigated the challenges of calibration of the PARAMICS microscopic simulation model for the local traffic conditions in the Kingdom of Saudi Arabia. It proposed an adaptive neuro-fuzzy inference system (ANFIS) based calibration protocol for the PARAMICS model. The developed ANFIS model performs adequately in modeling the queue length as a function of two key calibration parameters, namely mean headway time and mean reaction time. The selected values of the calibration parameters obtained through the ANFIS modeling approach were used as the input parameters for the PARAMICS model. The error indices such as mean absolute errors and mean absolute percentage errors of the developed ANFIS model in predicting the queue lengths varied between 1.11 and 1.24, and between 3.44 and 4.06, respectively. The conformance of the PARAMICS output and the measured queue length indicates the validity of the proposed calibration protocol.

Towards Behavioral Modeling of Drivers in Mixed Traffic Conditions

Mixed traffic conditions characterized by the presence of different vehicle types and weak lane discipline are common in the developing world. Different maneuvering capabilities of different vehicle types lead to vehicle-type dependent longitudinal and lateral movement driving behaviors. Weak lane discipline allows drivers to simultaneously look for possible lateral movements while progressing longitudinally. This integrated driving behavior gives rise to various driving phenomena like tailgating, multiple-leader following, swerving and filtering. However, very few studies exist in the literature that attempt to model these typical driving behaviors of mixed traffic streams. Moreover, most of these studies are not often readily available. Thus, a comprehensive review of the studies that consider modeling driver behavior in mixed traffic conditions is presented in this paper. Often, models developed for mixed traffic conditions stemmed out of the basic principles employed in the development of homogeneous traffic models. Hence, this paper first reviews the lane-based macro-, micro- and meso-scopic models in tandem and identifies their strengths and weaknesses in describing the mixed traffic system. A thorough understanding and conceptualization of any system is possible by analyzing large sets of empirical data. Therefore, a brief review on traffic data requirements to understand and model the driver behavior and, data extraction tools to obtain the trajectories of vehicles is presented next. Driver behavioral models form the core of any microscopic traffic simulation model. Core behavioral models such as car-following and lane-changing models which describe the longitudinal and lateral movements of drivers respectively are reviewed next. However, these two behavioral models may not be able to describe the integrated driving behavior independently. Therefore, various integrated driver behavioral models are briefed next. A simulation framework is essential to implement the driver behavioral models and evaluate their capabilities in representing larger scale traffic systems. Hence, a brief review of various simulation frameworks is presented subsequently. Finally, the challenges that are involved in modeling driver behavior in mixed traffic conditions are discussed and some useful research directions are proposed.

Calibrating the Wiedemann’s vehicle-following model using mixed vehicle-pair interactions

Microscopic traffic simulation models require the calibration of car-following (or vehicle-following) models. The parameters of vehicle-following models control individual driver’s spacing, time gap, speed variation and acceleration during different driving conditions. In recent studies, these parameters have been determined for different vehicle classes separately since heavy vehicles generally keep longer spacing and time gap than light vehicles. These parameters have been commonly estimated based on the observed macroscopic traffic flow data such as average volume and speed. However, these data cannot reflect actual vehicle-following behavior of individual vehicles. Also, the effect of the lead vehicle class on the following vehicle’s behavior has been neglected in the parameter estimation. Thus, this study estimates the driving behavior parameters for cars and heavy vehicles in the Wiedemann 99 vehicle-following model. For the estimation, 2169 vehicle trajectories were obtained from a 640-m segment of US-101 in Los Angeles, California during the morning peak hours. Separate parameters were estimated for three vehicle classes (cars, heavy vehicles, and motorcycles) and three vehicle-following cases (car following car, car following heavy vehicle, and heavy vehicle following car). From the comparison of the driving behavior parameters between cars and heavy vehicles, it was found that heavy vehicles keep longer spacing and time gap with the lead vehicle, are less sensitive to the lead car behavior, and apply smaller acceleration when they start from stationary position compared to cars. It was also found that the parameters significantly varied across different vehicle pairs even for the same vehicle class and the same vehicle-following case. The estimated parameters were also validated as the VISSIM simulation with the estimated parameters better reflected the observed cumulative average speed and acceleration distributions than the simulation with the default parameters. The results indicate that differences in the parameters among different vehicle-following cases and the variability of parameters for different vehicle pairs must be considered in the fixed parameters for each vehicle class currently used in the Wiedemann’s model.

Congestion pricing strategies to investigate the potential of route diversion on toll facilities using en-route guidance

The application of intelligent transportation systems (ITS) technologies to facilitate the traffic mobility requires dynamic routing decisions. This study examines the effectiveness of Paramics, a microscopic traffic simulation model that uses a link-to-link shortest path algorithm to consider both updated link travel times and incident conditions detected through different traffic assignment techniques. This paper describes modeling of an urban highway network's traffic conditions to investigate potential route diversion through congestion pricing strategies on toll facilities in Orlando, Florida using Paramics. The experimental design included a multi-level factorial design with three qualitative variables and four response quantitative variables. The experiment's objective was to investigate different scenarios for reducing tolls on less congested roads (SR528 and SR417) and increasing tolls on more congested roads (SR408) to determine the impact on travelers' route choices and overall congestion in the network. The simulation results demonstrate that the Dynamic Feedback Assignment (DFB) led to a reduction in the average queuing delay and average travel time when compared to results from the Stochastic Assignment (SA). DFB significantly affected the percentage of diversion in the network. Drivers saved 10%–16% of travel time when DFB information was provided. Results also show that percentages of route diversion vary from one route to another and depending on the travel cost between specific origin-destination pairs. While drivers incorporate real time guidance information to maximize their own utility, not all drivers gain the same benefit. This was attributed to the limited extra capacity of the alternative routes and the longer travel distance. Combining congestion pricing strategies with traffic information maximize travel time benefits.

Effective Installation of an Auxiliary Lane at Sag Sections to Mitigate Motorway Traffic Congestion

1. Introduction & Objectives Traffic congestion has been an important social issue in all over the world. It will result in great economic loss due to time delay and environment deterioration. In Japan, traffic congestion on intercity expressways on weekends and holidays is not an unusual scene. Looking at the percentage of congestion occurring on Japanese intercity motorways by bottleneck type, nearly 80% occurs in uninterrupted flow sections of sags or uphill slopes (60%) and tunnel entrances (20%). Congestion at merge areas accounts for approximately 13%. At a high level of traffic flow before occurrence of congestion, there exists a difference of travel speed between outer lane and inner lane, eventually causing an increase in use of the inner lane. The resultant inequality of lane utilization results in a breakdown of traffic flow in the inner lane of multilane motorway while flow of the outer lane remains below its capacity, thus decreasing the directional capacity. Once congestion occurs at a bottleneck, capacity would drop from pre-queue breakdown flow rate to lower queue discharge flow rate during congestion. As a measure against congestion, queue discharge flow from a bottleneck could be increased by providing drivers with information on the location of the head of the queue using roadside VMS. It is also possible to correct the overuse of the median lane by adding an auxiliary lane around the bottleneck at a sag. Besides, correction of unbalanced lane use could also be achieved by the provision of traveller information, such as “Please keep left” or “Please use left hand lane” to drivers through the VMS before occurrence of the congestion, (care should be taken here that vehicles drive on the left in Japan). However, there is no guideline on effective addition of an auxiliary lane in sag section for mitigation of motorway traffic congestion. This paper uses a microscopic traffic simulation model to study the effective way of adding an auxiliary lane in sag section of a dual 2-lane motorway for mitigation of traffic congestion. 2. Results The study considers 2 types of auxiliary lanes in sag section, i.e. one diverging and merging from outside, and one diverging from inside and merging from outside. The length of auxiliary lane varies every 500m from 500m to 2,000m. The auxiliary lane is added at various locations ending at or starting from the bottom of a sag, or starting from the start of a vertical curve, or starting from around the midpoint of upstream half of a vertical curve. Altogether 15 cases are considered for the simulation study. The microscopic traffic simulation model is used in the study. Prior to the simulation runs, some parameters are calibrated to check if the 5-min average speed and median lane utilization agree with the observed data. The spatial distribution of lane utilization before, inside and after an auxiliary lane is also examined. The output of the simulation runs in the study is breakdown flow rate for each case of auxiliary lane. It is seen from the simulation results that for an auxiliary lane diverging and merging from outside, the optimum length should be 1,000m – 1,500m and be added from around the start of vertical curve to several hundred meters beyond the end of vertical curve. This is in accordance with the observation that shock waves take place in the length of auxiliary lane of a sag section. The auxiliary lane longer than the optimum length does not seem to result in higher breakdown flow rate. With the same length of auxiliary lane, the one diverging from inside and merging from outside would yield higher breakdown flow rate than the one diverging and merging from outside from the viewpoint of increasing the breakdown flow rate.

Methodology for Microscopic Traffic Simulation Modelling of Land Port of Entries along the U.S.-Mexican Border: Ysleta – Zaragoza Land Port of Entry Case Study

Land Ports of Entry (LPOEs) are facilities that control access into the U.S., as well as departures of persons and freight. While traffic simulation modeling techniques are not typically used to evaluate LPOE operational processes, they should be considered more often, because LPOEs have a significant impact on surrounding traffic conditions and vice versa. This paper proposes a methodology for developing microscopic traffic simulation models of LPOEs along the U.S.-Mexico border. The methodology consists of seven steps: (i) collect data; (ii) develop traffic or roadway network; (iii) model inspection and toll booths; (iv) develop traffic management strategies; (v) set up traffic demand; (vi) calibrate model; and (vii) validate model. The paper also presents a case study in which this methodology was followed to develop the Ysleta – Zaragoza LPOE traffic simulation model, which can be controlled from a web-based interface to help LPOE personnel without previous transportation modeling experience.

Traffic Models for Self-driving Connected Cars

Self-driving and connected vehicles, communicating with one another (V2V technology) and with the road infrastructure (V2I technology), are a subject of extensive research nowadays and are expected to revolutionize the automotive industry in the near future. The major goal of our work is to design a microscopic traffic simulation model for such vehicles, including a robust protocol for exchanging information. The question arises as to whether such communication system may efficiently improve travel quality while reducing the risk of collisions. For the purpose of our research we created and developed a simulation software. Our tool visualizes traffic flow for custom but simplified road maps. The transport infrastructure includes multiple junctions, optionally equipped with traffic lights, and roads with varying number of travel lanes. Each vehicle is assigned a fixed route leading to a randomly chosen destination point. Any decisions made by autonomous cars (regarding acceleration or turning maneuvers) are preceded by communication stages (retrieving necessary data, negotiations). In the paper we present fundamental concepts, assumptions and design of our model and simulation software, we also discuss potential issues relevant to our approach. As for the future work, we plan to implement our model in a large-scale agent-based traffic simulation software, Traffic Simulation Framework, so that further examination will be carried out for realistic road networks taken from the OpenStreetMap project. We also plan to apply machine learning techniques, so that self-driving vehicles, as well as traffic light controllers, will be able to learn how to develop the best strategy and by this way improve traffic safety and efficiency in atypical cases.

Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles (AV) are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about the driving environment. Studies have proved that automated driving have the potential to decrease traffic congestion by reducing the time headway (THW), enhancing the traffic capacity and improving the safety margins in car following. Despite different encouraging factors, automated driving raise some concerns such as possible loss of situation awareness, overreliance on automation and system failure. This paper aims to investigate the effects of AV on driver’s behavior and traffic performance. A literature review was conducted to examine the AV effects on driver’s behavior. Findings from the literature survey reveal that conventional vehicles (CV), i.e. human driven, which are driving close to a platoon of AV with short THW, tend to reduce their THW and spend more time under their critical THW. Additionally, driving highly AV reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of AV on traffic performance, a simulation case study consisting of a 100% AV scenario and a 100% CV scenario was performed using microscopic traffic simulation. Outputs of this simulation study reveal that the positive effects of AV on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably improved by 8.09% during p.m. peak hours in the AV scenario, while the average travel speed enhanced relatively by 8.48%. As a consequent, the average travel time improved by 9.00% in the AV scenario. The outcome of this study jointly with the previous driving simulator studies illustrates a successful practice of microscopic traffic simulation to investigate the effects of AV. However, further development of the microscopic traffic simulation models are required and further investigations of mixed traffic situation with AV and CV need to be conducted.

A Learning-based Adaptive Group-based Signal Control System under Oversaturated Conditions

The operation of traffic signal control is of significant importance in traffic management and operation practice, especially under oversaturated condition during the morning and afternoon peak hours. However, the conventional signal control systems showed the limitations in signal timing and phasing under oversaturated situations. This paper proposes a multi-agent adaptive signal control system in the context of group-based phasing techniques. The adaptive signal control system is able to acquire knowledge on-line based on the perceived traffic states and the feedback from the traffic environment. Reinforcement learning with eligibility trace is applied as the learning algorithm in the multi-agent system. As a result, the signal controller makes an intelligent timing decision. Feature-based function approximation method is incorporated into reinforcement learning framework to improve the learning efficiency as well as the quality of signal timing decisions. The learning process of the learning-based signal control is carried out with the aid of a microscopic traffic simulation model. A benchmarking system, an optimized group-based vehicle actuated signal control system, is compared with the proposed adaptive signal control systems. The simulation results show that the proposed adaptive group-based signal control system has the potential to improve the mobility efficiency under different congested situations.

A copula-based approach to accommodate the dependence among microscopic traffic variables

Developing microscopic traffic simulation models requires the knowledge of probability distributions of microscopic traffic variables. Although previous studies have proposed extensive mathematical distributions for describing traffic variables (e.g., speed, headway, vehicle length, etc.), these studies usually consider microscopic traffic observations to be independent variables and distributions for these variables are investigated separately. As a result, some traditional approaches consider microscopic traffic variables as independent inputs to the traffic simulation process and these methods may ignore the possible dependence among different traffic variables.The objectives of this paper are to investigate the dependence structure among microscopic traffic variables and to examine the applicability of the copula approach to the joint modeling of these variables. Copulas are functions that relate multivariate distribution functions of random variables to their one-dimensional marginal distribution functions. The concept of copulas has been well recognized in the statistics field and recently has been introduced in transportation studies. The proposed copula approach is applied to the 24-h traffic data collected on IH-35 in Austin, Texas. The preliminary data analysis indicates that there exists dependence among microscopic traffic variables. Moreover, the modeling and simulation results suggest that copula models can adequately accommodate and accurately reproduce the dependence structure revealed by the traffic observations. Overall, the findings in this paper provide a framework for generating multiple microscopic traffic variables simultaneously by considering their dependence.

Exploring Traffic Conflict-Based Surrogate Approach for Safety Assessment of Highway Facilities

This study explored potential use of a traffic conflict-based surrogate safety assessment as an alternative way of evaluating the safety performance of roads and identifying potential sites for safety improvement. The study compared two major safety assessment streams (a statistical modeling method and a traffic conflict-based method) in their evaluation of crash risk to investigate the performance of the traffic conflict-based method as an alternative to the crash-based method in identification of hot spots. The empirical Bayes (EB) method coupled with the safety performance function (SPF), called the EB-SPF method, was used as a benchmark, and the conventional crash frequency method was used as a comparison supplement. These two methods were viewed as the crash-based methods because they relied on crash data. Traffic conflicts were estimated through the microscopic traffic simulation model VISSIM. The safety evaluation was performed separately for 24 signalized intersections and 86 segments in the Tyson...

Assessment of Queue Length and Delay at Toll Plaza Using Microscopic Traffic Simulation

Highways and freeways usually experience severe traffic congestion due to the presence of toll plazas which is considered as a bottleneck. In recent years, traffic engineers around the world have utilised microscopic traffic simulation models as tools to evaluate the performance of selected freeway facilities based on known traffic pattern data. This study attempts to analyse the performance of toll plaza by utilizing the microscopic traffic simulation software VISSIM 6.0, based on two different output measures, namely: average queue length and average delay time. At the selected toll plaza, data are collected at toll booths for all three modes of payments, which are: cash mode, Touch n Go, and Smart TAG. Therefore, this study aims to build a microscopic traffic simulation model that is capable of analysing the behaviour of vehicles at toll plaza based on the mentioned output measures for each mode of payment and also to determine the type of payment mode that has the most significant impact on the overall performance of toll plazas and ultimately, to understand the causes of congestion at toll plaza.

Traffic simulation models calibration using speed–density relationship: An automated procedure based on genetic algorithm

This paper presents the first results of a research which applied a genetic algorithm to calibrate a microscopic traffic simulation model based on speed–density relationships. A large set of traffic data collected from the A22 Freeway, Italy, was used and a comparison was performed between the field measurements and the simulation outputs obtained for a test freeway segment by using the Aimsun microscopic simulator.The calibration was formulated as an optimization problem to be solved based on a genetic algorithm; the objective function was defined in order to minimize the differences between the simulated and real data sets in the speed–density graphs. For this purpose, the genetic algorithm tool in MATLAB®was applied.Keeping in mind the objective to automatize this process, the optimization technique was attached to Aimsun via a subroutine, so that the data transfer between the two programs could automatically happen. An external script written in Python allowed the MATLAB®software to interact with Aimsun software.A better match to the field data was reached with the optimization parameters set with the genetic algorithm. In order to check to what extent the model replicated reality, model validation was also addressed. Results showed that a genetic algorithm is usefully applicable in the calibration process of the microscopic traffic simulation model. Beneficial effects are expected by applying the suggested optimization technique since it searches for an optimum set of parameters through an efficient search method.