*3.7. Travel Distance Models*

Both AIMSUN and SimTraffic estimate the total travel distance (*d tot*, m) as a summation of the distances traveled by each vehicle in the network, based on the following formula:

$$d^{tot} = \sum\_{i=1}^{N\_{sys}} D\_i \tag{10}$$

#### *3.8. Fuel Consumption Estimation Models*

AIMSUN estimates the fuel consumption of a vehicle based on the vehicle state (i.e., idling, cruising at a constant speed, deceleration, or acceleration). For the vehicles in a decelerating or idling state, the fuel consumption rate is assumed to be constant. The default fuel consumption rate is set to *F dec AIMSUN* = 0.530 mL/sec and *F idle AIMSUN* = 0.330 mL/sec for decelerating and idling states, respectively [35]. However, the aforementioned values can be modified by the user as needed. For the vehicles in an accelerating state, the fuel consumption rate (*F acc AIMSUN*, mL/sec) can be estimated as follows [35]:

$$F\_{AIMSLIN}^{acc} = c\_1 + c\_2 \cdot a \cdot V \tag{11}$$

where:

*c*1, *c*2—the model constants specified by the user;

*a*—the acceleration rate of a vehicle (m/sec<sup>2</sup> );

*V*—the speed of a vehicle (m/sec).

For the vehicles traveling at a cruising speed, the fuel consumption rate (*F cru AIMSUN*, mL/sec) can be estimated as follows [35]:

$$F\_{AIMSIN}^{\rm cru} = k\_1^a \cdot \left[ 1 + \left( \frac{V}{2 \cdot V\_m} \right)^3 \right] + k\_2^a \cdot V \tag{12}$$

where:

*k a* 1 , *k a* <sup>2</sup>—the model constants empirically determined for the considered vehicles; *Vm*—the speed of a vehicle at which the fuel consumption is minimal (m/sec); *V*—the speed of a vehicle (m/sec).

SimTraffic, on the other hand, estimates the fuel consumption as follows [36]:

$$F\_{SimTraffic} = k\_1^s \cdot Total + k\_2^s \cdot Total + k\_3^s \cdot Stops \tag{13}$$

where:

*FSimTra f f ic*—the fuel consumption of a vehicle estimated in gallons (should be multiplied by 3.785 in order to convert to liters);

*k s* <sup>1</sup> = 0.075283 − 0.0015892·*V* + 0.000015066·*V* 2 ; *k s* <sup>2</sup> = 0.7329; *k s* 2

*V*—the speed of a vehicle provided in mph (1 m/sec is 2.237 mph); *TotT*—the travel distance provided in miles (1 mile has 1609.34 meters); *TotD*—the total signal delay provided in hours; *Stops*—the total number of vehicle stops per hour.

#### **4. Research Methodology and Data Collection**

In order to assess the performance of the considered microsimulation software packages, the field data were collected for the selected roadway sections located in the northern part of Iran. The available field data were refined, and the travel time-flow functions were calibrated using the SPSS statistical software for each one of the considered roadway sections. After processing the collected field data, the major transportation network performance indicators were estimated. Then, the considered roadway sections were modeled within the AIMSUN environment and the SimTraffic environment using the same geometric and physical characteristics. The network performance indicators, estimated using the microsimulation models, were compared to the ones, which were computed based on the collected field data. This section provides details on the field data collection and processing.

#### *4.1. Roadway Sections Selected for the Field Survey*

The entire map of Rasht (one of the largest cities in the northern part of Iran and the capital city of Gilan province, located near the Caspian Sea, with a population of approximately 1.2 million, including students, workers, and other commuters [37]) was studied in order to select the roadway sections for further evaluation. A total of four roadway sections with different functional classifications were selected for a detailed analysis, including one major arterial roadway, two minor arterial roadways, and one collector-distributer (C–D) roadway. Note that the classification of roadway sections was adopted based on the Iran urban roadway design code [38]. In particular, the major arterial roadway is classified as a two-lane 2-way suburban roadway, generally passing through the small- and medium-sized cities. On the other hand, the minor arterial roadway is designed to facilitate mobility and accessibility of vehicles. The pedestrian traffic is controlled at intersections using the traffic control signals. The minor arterial roadways generally pass through the large-sized cities. Furthermore, the C-D roadways establish connections between the local and minor arterial streets. The C-D roadways typically have at least two lanes in each direction and an allowable travel speed of 40 km/h.

As stated earlier, a total of four roadway sections were selected for a detailed evaluation, including the following: (1) Beheshti Street; (2) Saadi Street; (3) Azadegan Street; and (4) Esteghamat Street. More information (i.e., classification and basic geometric characteristics) regarding the considered roadway sections is presented in Table 2. Furthermore, the satellite images of the selected roadway sections are presented in Figure 1. All the investigated roadway sections have 2 lanes in each direction. The lane width varies from 3.25 m (Azadegan Street) to 3.75 m (Saadi Street). Moreover, the surveyed section on the Beheshti Street was the longest (i.e., 1300 m), while the surveyed section on the Saadi Street was the shortest (i.e., 490 m). Note that none of the considered roadway sections had any junctions (i.e., the traffic flow along the considered roadway sections was not interrupted due to the presence of junctions).


**Table 2.** Geometric and physical characteristics of the selected roadway sections.

**Figure 1.** Satellite images of the selected roadway sections.

There exist different approaches for collecting the traffic data (e.g., counts, video recording). In this study, the field data were collected using the traffic counts. The total number of passing vehicles was recorded over 5-min time intervals for each one of the selected roadway sections. The data were collected from 8:00 am until 1:00 pm in five days during weekdays (from Saturday to Wednesday). Note that weekdays in Iran are from Saturday to Thursday. The Thursday data were not considered in the analysis, as the Thursday traffic flow patterns substantially differ from other weekdays for the considered study areas. Throughout the data collection, the weather was clear. Two vehicle plate registration stations were located at each one of the considered roadway sections (one station was located at the beginning of each roadway section, while the other station was located at the end of each roadway section). The following data were collected at the vehicle plate registration stations by the observers: (a) the vehicle entrance time; (b) the last three digits of the vehicle plate; and (c) vehicle type.

The data collected from the vehicle plate registration stations were stored on specific worksheets. Based on the vehicle entrance time at each vehicle plate registration station, the research team was able to determine the time when each vehicle entered and exited a given roadway section. The travel time along a given roadway section was estimated as a difference between the exit and entrance times for each vehicle. The last three digits of the vehicle plate were used as the vehicle's unique identifiers throughout this study. During the data collection, it was noticed that the travel time was relatively large for certain vehicles on some of the considered roadway sections. The latter can be explained by the fact that those vehicles could make stops along a given roadway section (e.g., to pick or drop-off passengers or cargo), which significantly increased the travel time. However, the number of vehicles with the abnormal travel time can be considered as insignificant as compared to the total number of vehicles, which were passing a given roadway section. In particular, over 17,000 records were collected for the considered roadway sections throughout this study, and less than 2% were eliminated from the analysis due to abnormal travel times.

Once the field data were collected, all registered vehicles were converted to the passenger car units (PCUs) using the standard PCU coefficients, which are presented in Table 3. Note that the adopted PCU coefficients have been widely used in the transportation planning of different networks in Iran [39–41]. Bikes and motorcycles are generally assumed to have the same PCU value (i.e., PCU = 0.3) since bikes are not very popular in Iran (i.e., installation of bike lanes is not desirable by the city authorities, as these bike lanes may occupy a substantial portion of urban streets). As for other types of vehicles, mid-size trucks and large-size trucks fall under the category "other types of vehicles". Mid-size trucks and large-size trucks may substantially impact the traffic flows during the day; however, their percentage was insignificant for the considered roadway sections.



#### *4.2. Data Processing*

Once the data collection was completed, the research team started the analysis of the worksheets, which contained the information gathered from the vehicle plate registration stations. The first step in processing the collected data was to identify the timestamps on the entry vehicle plate registration station and the exit vehicle plate registration station for each vehicle. The corresponding time stamps were retrieved using the vehicle plate information. The timestamp values were further used in estimating the total vehicle travel time for each one of the considered roadway sections. In the second step, the estimated travel time observations were analyzed, and the observations with abnormal travel time values were removed from the dataset (as discussed earlier, certain vehicles could make additional stops at a given roadway segment, which substantially increased the total travel time, as compared to the total travel time of the vehicles that did not make any stops). Elimination of the observations with abnormal travel time values was critical in order to ensure that the transportation network performance indicators would be calculated accurately. In the third step, the hourly vehicle flows were estimated for the time periods between 8:00 am and 1:00 pm. Note that the hourly vehicle flows were calculated using the PCU coefficients, which were applied to different types of vehicles. In the fourth step, the hourly travel time values were estimated for the time periods between 8:00 am and 1:00 pm in order to develop the functions, describing the relationship between the travel time and the hourly flow for each one of the considered roadway sections. The travel time and vehicle flow values were entered in the SPSS statistical software.

The SPSS statistical software was further used for the calibration of the travel timeflow functions for each roadway section. The Bureau of Public Roads (BPR) formula was adopted as a foundation throughout the analysis. The BPR formula can be expressed using the following relationship [42]:

$$t\_i = t\_i^0 \cdot \left[ 1 + 0.15 \cdot \left( \frac{v\_i}{c\_i} \right)^4 \right] \forall i \in I \tag{14}$$

where:

*I*—the set of links in the transportation network;

*ti*—the congested travel time on link *i* (min);

*t* 0 *<sup>i</sup>* —the free-flow travel time on link *i* (min);

*vi*—the vehicle flow on link *i* (vehicles/h);

*ci*—the capacity of link *i* (vehicle/h).

Based on the BPR formula, the congested travel time on a given link is defined based on the free-flow travel time on that link, the vehicle flow, and the link capacity. Increasing the flow of vehicles on a given link causes an increase in travel time. Once the link capacity is reached, the travel time will oscillate. The SPSS statistical software was used to estimate the free-flow travel time and capacity for each one of the considered roadway sections based on the collected data. The basic statistical information for the collected travel time data that were used for the development of BPR functions is presented in Table 4 (including the number of observations, minimum travel time, maximum travel time, average travel time, travel time standard deviation, and median travel time). Note that the collected number of observations used in developing the BPR function for each roadway section was found to be sufficient in order to obtain an acceptable degree of accuracy (i.e., the errors did not exceed 10−<sup>8</sup> for the considered roadway sections).

**Table 4.** Statistical information for the collected travel time data.


#### *4.3. BPR Functions*

Based on the results obtained from the SPSS statistical software, the following BPR functions were obtained for the Beheshti (*t beh*), Saadi (*t saad*), Azadegan (*t azad*), and Esteghamat (*t est*) roadway sections:

$$t^{beh} = 0.80 \cdot \left[ 1 + 0.15 \cdot \left( \frac{v}{260} \right)^4 \right] \tag{15}$$

$$t^{sad} = 0.96 \cdot \left[ 1 + 0.15 \cdot \left( \frac{v}{215} \right)^4 \right] \tag{16}$$

$$t^{azad} = 1.00 \cdot \left[1 + 0.15 \cdot \left(\frac{v}{232}\right)^4\right] \tag{17}$$

$$t^{\varepsilon st} = 1.37 \cdot \left[ 1 + 0.15 \cdot \left( \frac{v}{170} \right)^4 \right] \tag{18}$$

The calibrated BPR functions are illustrated in Figure 2 for all the considered roadway sections. Since the length of the considered roadway sections is different, the absolute travel time values were converted into the relative travel time values (i.e., travel time per kilometer) in Table 4 and Figure 2, as this ratio would provide more insights into the travel conditions at the considered roadway sections. It can be observed that the travel time at the Esteghamat roadway section increases much faster with the increasing flow as compared to the other roadway sections.

**Figure 2.** Calibrated Bureau of Public Roads (BPR) functions for the selected roadway sections.

### *4.4. Peak Hour Indicators*

Based on the analysis of the collected data, the peak hour for the selected roadway sections was found to be 8 am—9 am. The average travel time for the peak hour was calculated based on the available travel time observations collected over the 8 am—9 am peak period. Furthermore, based on the roadway section length and the timestamps recorded at the vehicle plate registration stations, the average speed of vehicles was calculated for each direction of a given roadway section. Details regarding the peak hour indicators are presented in Table 5 for each one of the considered roadway sections, including the following: (1) section name; (2) section main direction; (3) vehicle flow in the main direction—*v main*; (4) vehicle flow in the opposite direction—*v opp*; (5) average travel time—*t ave*; (6) average travel speed—*s ave*; and (7) total distance traveled by all the vehicles—*d tot*. Note that the main direction was determined based on the police reports and confirmed during the field survey that was conducted as a part of this study for each one of the considered roadway sections.

**Table 5.** The peak hour indicators for the selected roadway sections.


The highest vehicle volume was recorded for the Beheshti roadway section, while the lowest vehicle flow was observed on the Esteghamat roadway section. The greatest travel time (≈1.9 min) was estimated for the Saadi roadway section, where the vehicles were traveling with an average travel speed of less than 20 km/h. On the other hand, the greatest average travel speed (≈43.32 km/h) was recorded for the Beheshti roadway section. In addition, based on the analysis of the collected data, the greatest vehicle travel distance was calculated for the Beheshti roadway section.

#### **5. Numerical Experiments**

Based on the existing physical and traffic characteristics, the selected roadway segments were simulated within the AIMSUN and SimTraffic environments. The major model parameters, such as vehicle specifications (e.g., length, width, maximum speed, acceleration, and others), driver behavior parameters (e.g., reaction time), lane-changing distance in ramp and weaving areas, and others, were calibrated for the travel conditions, observed in the northern part of Iran. The calibration of the major microsimulation model parameters (which are primarily used by the car-following models) was performed based on the field surveys, which were conducted by Shariat [5]. Specifically, Shariat [5] gathered the data for the representative roadway sections, passing through the Tehran metropolitan area. A number of professional Z series SONY cameras were installed along the roadway sections in order to collect the data. The speed and acceleration of passing vehicles were recorded with a time interval of less than 1.0 sec. Each one of the installed cameras could cover an area of up to 100 m in length. The videos created by each camera were overlapped. Then, the collected data were analyzed, and the required car-following model parameters were calculated. Although the study was conducted by Shariat [5] for the Tehran metropolitan area, the obtained results can be applied to this study due to similarities in the traffic flow patterns observed in the Tehran metropolitan area and the northern part of Iran (where the four roadway sections, selected for a detailed analysis in this study, are located).

Furthermore, some additional procedures were performed before adopting the calibration results from the previously conducted study. In particular, the validity of calibrated results (obtained for the Tehran metropolitan area) were verified using the field data that

were collected for the considered roadway sections, located in the City of Rasht, based on the GEH formula, proposed by Geoffrey E. Havers [43]:

$$GEH = \sqrt{\frac{2 \cdot (v - \overline{v})^2}{v + \overline{v}}} \tag{19}$$

where:

*v*—the traffic volume obtained from the microsimulation model (vehicles);

*v*—the actual traffic volume obtained from the field observations (vehicles).

As a result of the conducted analysis, it was found that the GEH values did not exceed 4.3 for the considered roadway sections, which shows a high accuracy level of the calibrated data for the car-following models that were adopted in this study. Additional field surveys were conducted in order to calibrate the physical and technical characteristics of a PCU in Iran [39–41]. More than 8800 vehicles of different types were analyzed in terms of the following parameters: (1) length; (2) width; (3) weight; (4) maximum speed (i.e., the maximum speed that a vehicle can achieve in a free flow traffic condition on a straight roadway section, assuming no speed limits and obstacles); and (5) maximum acceleration. The analysis results are summarized in Table 6. Based on the estimated PCU specifications and the report published by the Iran standard and quality inspection company [44], the PCU fuel consumption was set equal to 12.1 liters of fuel per 100 kilometers for the urban travel conditions. Using the data collected as a result of the field survey, the estimated driver reaction time comprised approximately 0.90 sec.

**Table 6.** Calibrated physical characteristics of a standard passenger car unit.


Certain microsimulation software packages (e.g., AIMSUN) require setting additional parameters for the lane-changing model. The latter set of parameters were estimated based on the available field observations and is presented in Table 7. In zone 1, the lane-changing decisions are primarily affected by the travel conditions on the lanes involved. The following factors are considered when assessing the improvement in driving conditions from changing lanes [35]: travel speed, desired speed, distance to the preceding vehicle, speed of the preceding vehicle, and others. The lane-changing model is typically implemented in zone 1 for overtaking maneuvers. As for zone 2, it is generally occupied by vehicles, which are not driving in the desirable lanes (i.e., the vehicles aim to move to alternative lanes in order to make turning maneuvers). Once the gap becomes acceptable, the vehicles within zone 2 will be moving closer to the desired lane. Note that the distances to zone 1 and zone 2 are given in seconds (Table 7) but can be converted to meters based on the vehicle travel speed. The on-ramp distance is used by the lane-changing model in the vicinity of ramps (e.g., certain vehicles will be switching lanes in order to get closer to the ramp).

**Table 7.** Calibrated lane-changing parameters.


The aforementioned calibrated parameters were assigned within both AIMSUN and SimTraffic microsimulation models in order to conduct the numerical experiments. The next sections of the manuscript elaborate on the evaluation of the considered microsimulation models for the selected roadway sections in terms of the major transportation network performance indicators.

#### *5.1. Evaluation of the Microsimulation Models*

Both AIMSUN and SimTraffic microsimulation models use statistical distributions in modeling the traffic flow, which causes differences in terms of the values of transportation network performance indicators from one replication to another. Therefore, multiple replications are required in order to obtain the average values of the performance indicators. A total of ten replications were used to calculate the average values of the performance indicators within the AIMSUN and SimTraffic microsimulation models in this study. Ten replications were found to be sufficient, as the coefficient of variation did not exceed 2.0% for the considered transportation network performance indicators (which will be presented in the following sections of the manuscript). Furthermore, the developed microsimulation models start each replication with an empty transportation network. In order to avoid significant variations in the performance indicators throughout the simulation run, a warm-up time of 15 min was assigned for both AIMSUN and SimTraffic. The peak hour volume was used in modeling the traffic flow for each one of the selected roadway sections. Based on the existing speed limits, the maximum allowable speed was set to 55 km/h for each roadway section.

#### *5.2. Transportation Network Performance Indicators*

The major transportation network performance indicators, estimated using the AIM-SUN and SimTraffic microsimulation models, are presented in Tables 8 and 9. Tables 8 and 9 provide the following information: (1) section name; (2) input vehicle flow—*v*; (3) total travel time by all vehicles—*t tot*; (4) average travel speed—*s ave*; (5) average total travel time per vehicle—*t veh*; (6) total fuel consumption by all the vehicles—*f tot*; and (7) total distance traveled by all the vehicles—*d tot*. The transportation network performance indicators, calculated using the AIMSUN and SimTraffic microsimulation models, were compared to the actual ones, which were calculated based on the collected field data. The actual input vehicle flow, average travel speed, average total travel time per vehicle, total fuel consumption by all the vehicles, and total distance traveled by all the vehicles, which were computed based on the collected field data, are presented in Table 10. Figure 3 presents the values of all the considered transportation network performance indicators obtained by different approaches (actual vs. AIMSUN vs. SimTraffic) for the selected roadway sections.



**Table 9.** SimTraffic performance indicators for the selected roadway sections.



**Table 10.** Actual values of the performance indicators for the selected roadway sections.

**Figure 3.** Transportation network performance indicators: actual vs. AIMSUN vs. SimTraffic.

The numerical experiments indicate that AIMSUN overestimated the vehicle flow on average by 0.13%, while SimTraffic overestimated the vehicle flow on average by 2.22% over the selected roadway sections. As for the average travel speed, both microsimulation models also overestimated the average travel speed as compared to the actual values, estimated based on the collected field data. Specifically, the average travel speed, suggested by the AIMSUN and SimTraffic microsimulation models, was greater on average by 11.59% and 17.75%, respectively, as compared to the actual average travel speed. It was found that AIMSUN underestimated the actual average total travel time per vehicle on average by 6.91%, while SimTraffic overestimated the average total travel time per vehicle on average by 0.42%.

As for the fuel consumption, both AIMSUN and SimTraffic microsimulation models underestimated the total fuel consumption by vehicles on average by 64.83% and 16.33%, respectively, as compared to the actual fuel consumption, calculated based on the Iran standard and quality inspection company guidelines (i.e., 12.1 liters of fuel per 100 kilometers). Such a significant difference in the fuel consumption, suggested by the microsimulation

models, and the actual fuel consumption can be explained by the fact that both AIM-SUN and SimTraffic deploy specific fuel consumption models, which are not just simply based on the total travel distance. Specifically, the AIMSUN fuel consumption model uses different equations for estimating the fuel consumption depending on the vehicle state (e.g., "idle" vs. "deceleration" vs. "traveling at the cruising speed" vs. "acceleration") and takes into consideration the vehicle speed, acceleration/deceleration rates, and different fuel consumption rates (which vary depending on the vehicle state) [35].

On the other hand, the SimTraffic fuel consumption model calculates the fuel consumption based on a nonlinear function, which includes the vehicle cruising speed, total travel distance, total delay caused by traffic signals, and total number of stops [36]. Therefore, based on the analysis results, it can be concluded that the current Iran standard and quality inspection company guidelines require some revisions in order to more accurately estimate fuel consumption. Other variables should be considered (e.g., vehicle state, vehicle speed, acceleration/deceleration rates, total delay caused by traffic signals, total number of stops)—not just the total travel distance. The numerical experiments also indicate that the total distance traveled by all the vehicles, suggested by the AIMSUN and SimTraffic microsimulation models, was greater on average by 3.58% and 8.34%, respectively, as compared to the actual total distance traveled by all the vehicles.

#### *5.3. Discussion*

The conducted numerical experiments provided some insights regarding the performance of the AIMSUN and SimTraffic microsimulation models for the selected roadway sections in the northern part of Iran. AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance, while SimTraffic provided more accurate values of the travel time. The errors of the microsimulation models in estimating various transportation network performance indicators can be justified by different issues that include, but are not limited to, the following: (1) capability of the adopted car-following models to replicate realistic traffic flow behavior; (2) capability of the adopted lane-changing models to replicate realistic lane-changing maneuvers; (3) network traffic generation accuracy; (4) errors that are associated with the calibration of BPR functions for the considered roadway sections; (5) errors that are associated with the calibration of physical and technical characteristics of a standard passenger car unit; and (6) errors that are associated with the field data collection and estimation of the actual values of the transportation network performance indicators. Addressing the aforementioned challenges is expected to improve the accuracy of both AIMSUN and SimTraffic microsimulation models.

The fuel consumption, suggested by both microsimulation models, was significantly different from the fuel consumption values, calculated based on the Iran standard and quality inspection company guidelines (where the fuel consumption is proportional to the total travel distance only). The latter finding can be justified by the fact that both AIMSUN and SimTraffic microsimulation models deploy more advanced fuel consumption models, which account not only for the travel distance but also for the other important factors (e.g., acceleration/deceleration rates, travel speed, vehicle state, number of stops, etc.). Despite the difference in terms of the computed fuel consumption values, both AIMSUN and SimTraffic microsimulation models were able to replicate the existing travel conditions on the considered roadway sections with a high degree of accuracy and identify bottlenecks for certain roadway sections. For example, both AIMSUN and SimTraffic were able to identify congestion on the Saadi roadway section, which is in line with the existing travel conditions (Figure 4). In particular, the AIMSUN and SimTraffic microsimulation models suggested the average travel speeds of 20.58 km/h and 32.00 km/h, respectively (Tables 8 and 9). Such values are significantly lower than the actual speed limit on the Saadi roadway section (55 km/h) and indicate moderate traffic congestion.

**Figure 4.** Congestion on the Saadi roadway section.

The entire section of the Saadi Street (i.e., 490-m roadway segment) experienced traffic congestion during peak hours primarily due to lack of capacity. The existing traffic demand in the area substantially exceeds the available capacity of the Saadi roadway section. Furthermore, throughout the field survey, it was noticed that many vehicles could make stops along the Saadi roadway section (e.g., to pick or drop-off passengers or cargo), which is another reason for the congestion and low travel speeds. Note that the travel speed estimation accuracy could be improved even further by enhancing the quality and quantity of the data that were used for the calibration of the AIMSUN and SimTraffic microsimulation models.

Despite the effectiveness of both microsimulation models in terms of the identification of bottlenecks, AIMSUN is recommended to be further used by transportation planners in northern Iran, as it outperformed SimTraffic in terms of the major transportation network performance indicators (i.e., vehicle flow, travel speed, and total travel distance).

#### **6. Concluding Remarks and Future Research**

The demand for passenger and freight transport has significantly increased over the last decades due to a number of reasons, including urban development, industrialization, and population growth. Some of the existing transportation networks are not able to serve the growing demand, which causes severe congestion. Different traffic simulation software packages have been widely used by transportation planners across the world, aiming to identify the appropriate congestion mitigation alternatives and eliminate recurring bottlenecks. Microsimulation models (e.g., VISSIM, CORSIM, PARAMICS, AIMSUN, and SimTraffic) have been commonly used for a detailed evaluation of transportation networks. A number of studies conducted in the past aimed to evaluate certain microsimulation packages. Most of those studies concluded that the selection of the appropriate microsimulation software package could be affected by the software capabilities, ease of use, user interface/graphics, accuracy in estimating various transportation network performance indicators, user needs, objectives of the project and other factors. Furthermore, the selection of the appropriate microsimulation model directly depends on the study area characteristics.

Taking into consideration the existing congestion issues in metropolitan areas of Iran, this study aimed to estimate the major transportation network performance indicators for the four roadway sections with different functional classifications located in the northern part of Iran. The AIMSUN and SimTraffic microsimulation models were developed for the selected roadway selections. The collected field data and the data from a previously conducted study for the Tehran metropolitan area were used for the calibration of microsimulation model parameters. The calibration of car-following models was performed using the previously conducted study for the Tehran metropolitan area, as it has similarities in the traffic flow patterns with the considered study areas. The AIMSUN and SimTraffic microsimulation models with calibrated parameters were used to estimate the major transportation network performance indicators, including travel time, travel speed, vehicle flow, fuel consumption, and total travel distance.

The numerical experiments indicated that AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance. On the other hand, SimTraffic provided more accurate values of the travel time. Significant variations were observed for the fuel consumption estimates, which could be explained by the fact that both microsimulation models had their own approaches for the calculation of fuel consumption. However, both AIMSUN and SimTraffic were able to accurately replicate the existing travel conditions and effectively identify congestion on the selected roadway sections. Despite the effectiveness of both microsimulation models in terms of the identification of bottlenecks, AIMSUN is recommended to be further used by transportation planners in northern Iran, as it outperformed SimTraffic in terms of the major transportation network performance indicators (i.e., vehicle flow, travel speed, and total travel distance). Moreover, findings from this study will be useful for the researchers and practitioners, who heavily rely on microsimulation models in transportation planning.

The scope of future research for this study may focus on the following extensions: (1) compare the AIMSUN and SimTraffic microsimulation models against other microsimulation models (e.g., VISSIM, CORSIM, and PARAMICS), which are widely used in transportation planning: (2) evaluate performance of the AIMSUN and SimTraffic microsimulation software packages for other congested roadway sections in Iran; (3) compare different congestion mitigation alternatives using the developed microsimulation models; (4) assess the effects from deployment of intelligent transportation systems for the considered roadway sections using the developed microsimulation models; (5) conduct an additional field survey and collect the data not only for the morning peak hour, but also for the evening peak hour (the developed microsimulation models could be evaluated using a larger data sample to improve the accuracy of results); (6) apply alternative methods for improving the transportation process (e.g., exact optimization, heuristic algorithms, and metaheuristic algorithms [45–47]); (7) compare vehicle trajectories proposed by AIMSUN and SimTraffic throughout the safety analysis (e.g., estimation of crash angles); and (8) collect additional field data to calibrate the parameters of car-following models for the considered roadway sections.

**Author Contributions:** The authors confirm contribution to the paper as follows: study conception and design: A.M.R., M.A.D., A.M.; data collection: A.M.R., A.M.; analysis and interpretation of results: A.M.R., M.A.D., A.M.; draft manuscript preparation: A.M.R., M.A.D., A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not Applicable.

**Informed Consent Statement:** Not Applicable.

**Data Availability Statement:** The data that were used in this study can be requested by contacting the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
