**1. Introduction**

Roadway density and traffic congestion substantially increased over the last years across the world, especially near large metropolitan areas, primarily due to rapid industrialization, fast population growth, urban development, and increasing demand for passenger and freight transport [1–4]. The congestion mitigation alternatives (e.g., adding another lane to a given roadway segment, adjust cycles of traffic signals, build an interchange, implement some of the access management approaches, and others) must be implemented in order to alleviate the increasing congestion issues and serve communities. Transportation planners must evaluate various congestion mitigation alternatives, and the most promising alternative should be recommended for implementation. Nowadays, transportation planners often tend to use traffic simulation software packages for comparison of various congestion mitigation alternatives. The increasing application of traffic simulation software packages is supported by numerous advancements in computer and software sciences.

A simulation analysis of traffic flow is based on specific indices and parameters that must be set within a given software package. The major traffic flow parameters within sim-

**Citation:** Rahimi, A.M.; Dulebenets, M.A.; Mazaheri, A. Evaluation of Microsimulation Models for Roadway Segments with Different Functional Classifications in Northern Iran. *Infrastructures* **2021**, *6*, 46. https:// doi.org/10.3390/infrastructures6030046

Academic Editor: Krzysztof Goniewicz

Received: 28 January 2021 Accepted: 10 March 2021 Published: 15 March 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

ulation models must be established based on the data collected as a result of field studies and surveys. Then, the transportation network performance indicators (e.g., travel time, travel speed, travel delay), produced by the traffic simulation model, can be compared to the actual values collected from the field. Based on a comparative analysis against the field data, the required modifications should be applied within the traffic simulation model to ensure that it will replicate realistic travel conditions, which are observed for a given transportation network, with an acceptable degree of error. Once parameters of the given traffic simulation model are calibrated, the model can be executed to estimate the values of transportation network performance indicators for different congestion mitigation alternatives. Upon completion of the simulation analysis, transportation planners will be able to determine the most promising congestion mitigation alternative (i.e., the alternative, which will yield the most favorable impact on the travel conditions). One of the major advantages of using traffic simulation consists in the fact that the traffic simulation models allow visualizing the study area, identification of the roadway sections that experience bottlenecks and require future improvements, and efficient scenario analysis (e.g., evaluation of different congestion mitigation alternatives) [5].

Traffic simulation models can be categorized into three types [5–9]: (1) macroscopic, (2) microscopic, and (3) mesoscopic. Some of the principles used within macroscopic simulation models are adopted from fluid dynamics. Simulation of the traffic flow is performed for a given roadway section of the transportation network without considering interactions among the roadway users. Macroscopic simulation models primarily rely on such parameters as traffic volume, average speed, and density. Transportation planners use macroscopic simulation models for the analysis of the level of service and demand, as well as evaluation of regional plans and comprehensive transport programs.

As for microscopic simulation models, they rely on the car-following theory and the concepts of lane-changing, gap-acceptance and route choice in order to simulate the traffic behavior of each vehicle in a given transportation network. The car-following parameters determine the acceleration of vehicles, their interaction with other roadway users. Lane-changing allows the vehicles to shift from one lane to another based on the driver's objectives and surrounding vehicles. The gap-acceptance parameters determine the synthetic links of vehicles to the traffic flow on the route. The route choice parameters determine the selection of specific routes of a given transportation network for each driver. Microscopic simulation produces more detailed outputs as compared to macroscopic simulation and, therefore, is generally applied for a comprehensive evaluation of a given transportation network. However, microscopic simulation models require more input parameters as opposed to macroscopic simulation models. Moreover, it is quite difficult to determine the accurate values of the microsimulation model parameters due to challenges associated with modeling the driver behavior along the roadways [5,6].

On the other hand, mesoscopic simulation models combine features of macroscopic and microscopic simulation models [8,9]. Mesoscopic simulation models allow a detailed emulation of the vehicle platoon dispersion (e.g., a platoon of vehicles is moving along a roadway segment, and the dispersion occurs due to the differences in vehicle speeds). Furthermore, mesoscopic simulation models allow emulation of the vehicle platoon behavior (e.g., a platoon of vehicles is moving along a roadway segment with similar speeds and a short headway). A detailed platoon modeling allows accurate computation of travel times of vehicles. The total number of vehicles in a platoon, vehicle speeds in a platoon, and distribution of speeds in a platoon are some of the major characteristics required for modeling vehicle platoons in mesoscopic simulation models.

The selection of the appropriate traffic simulation package is critical for roadway improvement projects. In particular, the appropriate traffic simulation model will allow accurate estimation of the major transportation network performance indicators before and after implementation of various roadway improvement projects (therefore, the efficiency of potential roadway improvement projects could be accurately assessed). Generally, microscopic simulation models (e.g., AIMSUN, VISSIM, CORSIM, CUBE, SimTraffic, and

PARAMICS) are used for the analysis of the major roadway segments, which have large traffic volumes and experience significant delays. Considering increasing congestion issues near large metropolitan areas of Iran [6], this study focuses on the application of microsimulation for the analysis of the transportation network located in the northern part of Iran. The AIMSUN and SimTraffic microsimulation models, which have been widely used for the analysis of the transportation networks in Iran [5,10,11], are compared in terms of the accuracy in estimating the major transportation network performance indicators, including travel time, travel speed, vehicle flow, fuel consumption, and total travel distance. The values of performance indicators, suggested by both microsimulation models, are compared to the actual field data. Findings from this research will be valuable for transportation planners and will assist with the selection of the appropriate microsimulation model for the analysis of the transportation networks in Iran.

The remaining sections of the manuscript are organized in the following order. The next section presents a review of the relevant literature with a focus on the implementation of various microsimulation models for the analysis of transportation networks. The third section presents some background information for the AIMSUN and SimTraffic microsimulation models. Furthermore, the third section describes the major transportation network performance indicators, which will be considered in this study and estimated using AIMSUN and SimTraffic. The fourth section discusses the adopted research methodology along with data collection and provides a detailed analysis of the collected data. The fifth section presents the description of numerical experiments, which were conducted to evaluate the AIMSUN and SimTraffic microsimulation models, while the last section summarizes the findings of this research and outlines potential future research extensions.

#### **2. Literature Review**

As mentioned in the introduction section of the manuscript, different traffic simulation models have been widely used for the evaluation of transportation networks. There are many advantages of using traffic simulation; however, there exist some drawbacks associated with traffic simulation as well. The highway capacity manual of the Transportation Research Board [12] provides a detailed discussion of the traffic simulation advantages and disadvantages. The advantages of using traffic simulation include the following [12]: (1) simulated methods are appropriate where analytical studies cannot be administered; (2) simulation models allow comprehensive understanding of the transportation network parameters and their relative interactions; (3) simulation models provide the outputs that can be used for the statistical analysis of the spatial and temporal data; (4) simulation models can be used to evaluate and compare the status of network options; (5) simulation models can be used to analyze modifications in the network efficiency; and (6) simulation models consider the distinctive demands of the network parameters.

The disadvantages of using traffic simulation include the following [12]: (1) simulation models are sophisticated and could provide simpler administrative procedures; (2) simulation models should be analyzed, calibrated, and validated; (3) any shortcoming in the implementation of the latter procedures can make the results unreliable and inefficient; and (4) some users apply simulation models without being aware of its limitations and modalities. This section of the manuscript focuses on a review of the relevant previously conducted studies, which applied traffic simulation models for the analysis of transportation networks, assessed their accuracy in estimating various transportation network performance indicators, and discussed the advantages and disadvantages of using traffic simulation. A more comprehensive review of the state-of-the-art on various traffic simulation models can be found in Pell et al. [7], Azlan and Rohani [8], and Gora et al. [9].

#### *2.1. Detailed Review of the Collected Studies*

Many previous research efforts have aimed to compare different microsimulation models. For example, Bloomberg and Dale [13] focused on the comparison of the VISSIM and CORSIM microsimulation models in terms of the network coding structure, car-

following logic, gap acceptance model, and other attributes. The analysis results indicated that the differences among the considered microsimulation models were minimal, and the selection of the appropriate microsimulation model was primarily affected by the user needs and project requirements. Furthermore, it was found that CORSIM generally provided greater travel time as compared to VISSIM. Shaw and Nam [14] performed a comparative analysis of the VISSIM, PARAMICS, and CORSIM microsimulation models for the Southeast Wisconsin freeway system. The microsimulation models were compared based on the following aspects: (1) model capabilities; (2) ease of use; and (3) freeway system operational assessment application requirements. As a result of a detailed analysis, PARAMICS was found to be the most appropriate microsimulation model.

Tian et al. [15] studied the differences between the VISSIM, SimTraffic, and CORSIM microsimulation models. Based on the conducted numerical experiments, CORSIM produced the lowest variations in vehicle delays and throughput flow rates, while SimTraffic returned the highest variations. Moreover, it was noticed that higher variations were generally recorded for the scenarios where the capacity conditions were reached. Jones et al. [16] performed a comprehensive comparative analysis of the AIMSUN, SimTraffic, and COR-SIM microsimulation models based on different criteria (i.e., software requirements, ease of network coding, data requirements, appropriateness of the default parameter values, etc.). SimTraffic was reported to have the most user-friendly interface, while CORSIM was more efficient for modeling complex transportation networks. Furthermore, the study recommended that the microsimulation model selection should be based on the user needs and project requirements/expectations. In some cases, the synthesis of microsimulation models might be encouraged.

Fang and Elefteriadou [17] assessed the performance of the CORSIM, VISSIM, and AIMSUN microsimulation models for two interchanges in Arizona. The following factors were identified to be the most critical ones in the selection of the appropriate microsimulation model: (1) capability of representation of certain geometric characteristics; (2) capability of emulating certain signal control plans; (3) calibration process and comparison against the field conditions; and (4) extraction of certain performance indicators. Xiao et al. [18] proposed a comprehensive approach for the identification of the appropriate microsimulation model using quantitative and qualitative criteria. The quantitative evaluation criteria included calibration testing, while the qualitative evaluation criteria consisted of functional capabilities, service quality, input/output features, and ease of use. A case study was conducted for the AIMSUN and VISSIM microsimulation models. It was found that preferences to use a specific microsimulation model were primarily determined by the type of user. Shariat and Babaie [19] compared the car-following models adopted within the VISSIM and AIMSUN microsimulation models. Although the Gipps car-following model (used in AIMSUN) was simpler and generally emulated the traffic flow faster, the Whiteman–Ritter car-following model (used in VISSIM) was found to be more logical and typically yielded more accurate results.

Shariat [5] focused on the calibration of the AIMSUN, VISSIM, and SimTraffic microsimulation models for the Tehran metropolitan area. It was found that AIMSUN was superior to VISSIM and SimTraffic in terms of knowledge management, user-friendliness, software cost, and current application by various organizations in Iran. Pourreza et al. [20] evaluated the performance of CORSIM, AIMSUN, INTEGRATION, PARAMICS, and VIS-SIM for the analysis of transportation networks. The following aspects were considered: (1) expected application of the model; (2) model capabilities; (3) previous software implementation; (4) software support; (5) software costs; and (6) user-friendliness, graphics, and interface. CORSIM was found to be the most advantageous microsimulation model based on the considered performance indicators. Da Rocha et al. [21] conducted a study aiming to assess the accuracy of traffic microsimulation models in estimating fuel consumption and emissions. The researchers examined the Gipps and Newell car-following models. It was found that the Gipps car-following model demonstrated higher accuracy in terms

of the simulated vehicle trajectories. The analysis results showed that the selection of the non-optimal parameters substantially increased the variance of the model outputs.

Ibrahim and Far [22] undertook a simulation-based analysis to determine potential benefits from the implementation of pattern recognition in intelligent transportation systems. The AIMSUN microsimulation model was developed using real-life operational data. The numerical experiments demonstrated that AIMSUN was able to reduce the travel time by ~5–30%, while the congestion duration was decreased by ~8–41%. Praticò et al. [23] performed a study aiming to assess the accuracy in estimating vehicle travel speed on roundabouts. The VISSIM microsimulation model was used to emulate the traffic flow. The computational experiments showed that the proposed microsimulation model could provide accurate travel speed estimates if the microsimulation model parameters were carefully calibrated. Shaaban and Kim [24] focused on modeling two-lane and three-lane roundabouts in the VISSIM and SimTraffic environments. The microsimulation models were compared in terms of the estimated traffic delay values. It was found that, for the high-traffic flow scenarios, VISSIM provided higher delay values as compared to SimTraffic. However, no significant differences between the delay values were observed for the low-traffic flow scenarios.

Essa and Sayed [25] performed a comparative analysis of the PARAMICS and VISSIM microsimulation models. The numerical experiments showed that the default model parameters gave poor correlation with the field-measured data. Furthermore, it was found that both microsimulation models could not estimate traffic conflicts accurately without proper calibration. However, a good correlation between the field-measured conflicts and the simulated conflicts was achieved after calibration for both PARAMICS and VISSIM models. Astarita et al. [26] aimed to assess intersection safety by means of different traffic simulation models. The following types of intersections were considered: (1) a roundabout; (2) an intersection regulated with a traffic light; and (3) an unregulated intersection. AIMSUN, VISSIM, and different versions of Tritone were used for simulating the intersection traffic flows. The experiments showed some variations in the simulation outputs. However, the roundabout intersection generally had the largest number of conflicts. Kan et al. [27] studied freeway corridors that had dedicated lanes and periodically experienced congestion. Two driving behavior models were proposed and implemented in AIMSUN and MOTUS. The experiments demonstrated the high accuracy of the developed models and provided some insights into driver behavior on freeways.

Shaaban et al. [28] aimed to evaluate potential impacts from converting roundabouts into traffic signals at one of the urban arterial corridors in Qatar. A microscopic simulation approach based on VISSIM and MOVES (module for estimating emissions) was developed in the study. It was found that the replacement of roundabouts with traffic signals could reduce emissions by 37%−43%. Granà et al. [29] used AIMSUN to determine passenger car equivalent units for two-lane and turbo roundabouts. The results showed that the operational performance of roundabouts could be significantly affected by the percentage of heavy vehicles. Kim et al. [30] proposed a systematic guideline that could be used for calibrating reliable microscale estimates of vehicle emissions. The VISSIM environment was used to simulate the traffic flow. The proposed methodology demonstrated its effectiveness based on the available traffic data.

Song et al. [31] investigated the accuracy of TransModeler and VISSIM for the estimation of nontraffic performance indicators, including emissions, fuel consumption, and safety. The experiments showed that, even after calibration, both microsimulation models had significant errors when comparing to the actual values. Van Beinum et al. [32] examined the VISSIM and MOTUS traffic simulation models in their ability to emulate merging situations in high-traffic scenarios. It was found that the considered simulation packages were not able to accurately emulate turbulent traffic flows in terms of the headway distribution and lane-changing locations. However, the emulated gap acceptance distributions seemed to be appropriate.

A number of studies conducted a detailed review of different traffic simulation models. For example, Pell et al. [7] conducted a detailed analysis of 17 simulation packages, mostly focusing on the adaptability of simulation models to heterogeneous traffic and roadways networks. It was found that many software packages still have a significant number of drawbacks in modeling capabilities. Azlan and Rohani [8] provided a comprehensive overview of microscopic, mesoscopic, and macroscopic traffic simulation models. The models were overviewed in terms of their main purpose and the key parameters used. The study highlighted that the selection of the appropriate traffic simulation software is directly interrelated with the project needs. Gora et al. [9] studied the existing literature on the applications of microscopic traffic simulation for modeling connected and autonomous vehicles. A large variety of different traffic modeling approaches were discussed, including car-following models (e.g., Gipps model, Wiedemann model, Nagel–Schreckenberg model, intelligent driver model), lane-changing models, and software packages (e.g., VIS-SIM, SUMO).
