*2.2. Literature Summary and Contribution*

A summary of the conducted literature review is presented in Table 1, including the following data: (a) author(s); (b) year; (c) software used; and (d) key findings and important notes. A review of the literature indicates that different microsimulation models have been widely used by researchers in the past. The selection of the appropriate microsimulation software package is generally dependent on a number of factors, which may include, but are not limited to [5,14,16,17,20]: (1) software capabilities; (2) ease of use; (3) user interface/graphics; (4) software cost; (5) hardware/software requirements; (6) capability of emulating certain operations features; (7) previous software implementation; (8) accuracy in estimating various transportation network performance indicators (e.g., travel speed, travel time, vehicle delay, vehicle flow, and others); (8) user needs; (9) objectives of the project; and others. This study extends the work conducted by Shariat [5] and Shariat and Babaie [19] and focuses on the selection of the appropriate microsimulation software package for modeling the traffic movements in the northern part of Iran. The AIMSUN and SimTraffic microsimulation models are evaluated for the roadway sections with different functional classifications in terms of various performance indicators, including travel time, travel speed, vehicle flow, fuel consumption, and total travel distance.


#### **Table 1.** Literature review summary.


#### **Table 1.** *Cont.*


**Table 1.** *Cont.*

AIMSUN and SimTraffic have been widely used for the analysis of the transportation networks in Iran [5,10,11], and such a tendency can be explained by several reasons. First, both AIMSUN and SimTraffic are user-friendly in simulating traffic flow as compared to other microsimulation software packages (e.g., VISSIM). Second, AIMSUN and Sim-Traffic are quite popular microsimulation software packages and have been adopted by many consulting companies in Iran. Third, the cost of AIMSUN and SimTraffic is more affordable as compared to other microsimulation software packages (e.g., VISSIM). Fourth, the calibration process for AIMSUN and SimTraffic is less complicated when comparing to other microsimulation software packages. Last, but not least, AIMSUN and SimTraffic were found to be efficient in terms of replicating typical traffic conditions in Iran [5]. Findings from the present study are expected to provide more insights regarding the performance of AIMSUN and SimTraffic in terms of the modeling accuracy of the traffic movements in the northern part of Iran. These insights 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.

#### **3. Basic Background Information for AIMSUN and SimTraffic**

This section of the manuscript focuses on the description of the background information for the AIMSUN and SimTraffic microsimulation models. Furthermore, this section of the manuscript provides a detailed description of how the key transportation network performance indicators are calculated within the AIMSUN and SimTraffic microsimulation models.

## *3.1. AIMSUN*

The advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN2), the AIMSUN's prototype, was developed by the members of the former Simulation and Operations Research Laboratory (LIOS), located at the Polytechnic University of Catalonia [33] in 1989. In 1997, the Transport Simulation Systems (TSS) company was founded. Technical developments continued at the Polytechnic University of Catalonia, while TSS was commercializing the AIMSUN microsimulation software package. AIMSUN includes two components that enable a dynamic simulation, including the microscopic simulator and the mesoscopic simulator. AIMSUN can be applied for modeling roadways of different classifications, including urban networks, highways, freeways, arterials, ring roads, and their combinations. Its comprehensive graphic environment allows modeling different levels of travel demand. Furthermore, AIMSUN allows efficient correspondence with monitoring and signal mechanisms. The AIMSUN microsimulation software package can be used to administer maintenance mechanisms of the transportation corridors, facilitate transport security, and evaluate intelligent transport systems, toll mechanisms, and pricing procedures.

The AIMSUN microscopic simulator is a combined discrete/continuous simulator, where for certain elements of the system (e.g., detectors, vehicles), states alter continuously over the given simulated time, which is separated into fairly short, fixed-time intervals that are called simulation steps or cycles. AIMSUN contains some other important elements (e.g., entrance points, traffic signals), for which states alter discretely at specific points over the given simulation time. AIMSUN has many modeling capabilities, including detailed modeling of the traffic network, different types of drivers and vehicles, a wide range of the network geometric layouts, traffic incidents, conflicting maneuvers, and others. Along with traffic lights and traffic detectors, AIMSUN allows emulating variable message signs (VMS) and ramp metering devices. In order to design a simulation scenario, AIMSUN requires certain input data, which can be categorized into four classes: (1) network description; (2) traffic control plans; (3) traffic demand data; and (4) public transport plans. Some of the input parameters are primarily related to the simulation scenario features (e.g., warm-up time, simulation time), while some parameters characterize the nature of the traffic flow and transportation network and must be calibrated (e.g., reaction times, lane-changing zones). The AIMSUN microsimulation software package allows producing a graphical representation of the transportation network in both 2D and 3D formats, statistical data output (journey times, flow, delays, speed, stops), and the data, which were gathered by the simulated detectors (occupancy, counts, speed).

AIMSUN relies on the car-following, lane-changing, and gap-acceptance models. The car-following model determines changes in the velocity of a given vehicle, depending on its position and the positions of the surrounding vehicles. AIMSUN relies on the Gipps car-following model, which is based on the physical probability of lane-changing patterns, location of permanent traffic barriers, express routes, the future driver turns, and the existence of heavy vehicles. The lane-changing model triggers the vehicle movement from one lane to another. Generally, lane changes occur due to alterations in the traffic flow, connecting the origin and the destination, and driver routes. The vehicle lane changes are classified into discretionary and urgent lane changes. The gap-acceptance model allows defining whether the available gap will be accepted by a given driver to maneuver.

#### *3.2. SimTraffic*

SimTraffic is a microsimulation module, which is available within the Synchro Studio software. The Synchro Studio was developed by Trafficware, Inc., which was acquired by Naztec in 2005 [34]. Along with SimTraffic, the Synchro Studio has another module (Synchro), which is primarily used for optimizing the timing schemes at signalized intersections and for traffic signal coordination. The Synchro Studio is widely used for different traffic projects and studies on public transport. Synchro optimizes the cycle length, offsets, split times, and phase sequences, aiming to minimize the driver stops and delay. SimTraffic utilizes the information regarding the optimized signal timing provided by Synchro in order to execute microsimulation and emulate the traffic flows. Although the Synchro Studio is heavily used for improving the efficiency of traffic signals, the availability of the SimTraffic module extends its application for the analysis of congested transportation networks. SimTraffic allows modeling individual vehicles traveling along the predefined transportation network. Different types of vehicles can be modeled using SimTraffic, including trucks, passenger cars, and busses.

Unlike a number of other microsimulation software packages, SimTraffic displays animation while the simulation is being executed. The input data, assigned within Synchro (e.g., traffic flows, intersection cycle length, network geometric characteristics), are transferred automatically in the SimTraffic module. The driver and vehicle parameters, including yellow reaction time, green reaction time, gap-acceptance factor, vehicle acceleration, vehicle length, vehicle width, and occupancy, are adopted based on the values that are recommended by the Federal Highway Administration (FHWA). The trip generation and the route assignment are determined based on the traffic flows, which are assigned to each roadway segment. The traffic flows can be adjusted using growth factors, peak hour factor (PHF), or percentile adjustments. SimTraffic assumes that each vehicle will travel at its cruise speed if there are no impediments (i.e., in case there are no obstacles on a given roadway segment, each vehicle will travel at its cruise speed). The cruise speed is estimated based on the assigned link speed and the speed factor, which is dependent on the driver type. The speed factors may range from 0.85 to 1.15 based on the driver type. Similar to the AIMSUN microsimulation software package, SimTraffic allows changing the driver characteristics within the simulation environment.

#### *3.3. Network Traffic Generation*

In AIMSUN, the user is able to select one of the following headway models for generating the network traffic [35]: (1) exponential; (2) uniform; (3) normal; (4) constant; (5) "ASAP"; and (6) external. The exponential headway model is the default, where vehicles are assumed to enter the network, following an exponentially distributed vehicle arrival pattern. As for the uniform headway model, the mean time headway values are sampled from the uniform distribution. The normal headway model generates the vehicles, entering the network based on the truncated normal distribution. The constant headway model assumes the time interval between two consecutive vehicles to be constant (*t* = 1/*λ*, where *t*—the headway (sec), and *λ*—the mean input flow (vehicles/sec)). The "ASAP" headway model allows the vehicle to enter the network "as soon as possible" (i.e., once the space becomes available). The ASAP model allows increasing utilization of the available transportation network space. The external headway model generates the entering network traffic using an external user-defined program.

In SimTraffic, the flows are generated at the network entry points based on the volume counts at the downstream intersection [36]. Trips can also be added to the midblock traffic if the midblock traffic is specified or a volume source is required to balance the traffic. If both balancing and midblock sources exist, the midblock traffic will be computed as the maximum of these two sources. The vehicle arrivals generally follow the Poisson distribution. The link flows are computed independently for heavy vehicles and passenger cars. The heavy vehicle volume is estimated as a product of the adjusted vehicle volume and the percentage of heavy vehicles, while the passenger car volume will be equal to

the remaining vehicle volume. The user is able to assign two types of heavy vehicles, including: (a) trucks and (b) busses. The entering passenger cars can be assigned as standard passenger cars or carpool passenger cars.

### *3.4. Car-Following Models*

AIMSUN relies on the car-following model, which is based on the Gipps experimental model [35]. The AIMSUN car-following model can be considered as an ad hoc model, where the model parameters are not set to be global and can be adjusted depending on the values of local parameters (e.g., type of driver, the geometry of the roadway section, the influence of vehicles on the adjacent lanes, etc.). The model is based on the two major components, including the following: (a) acceleration; and (b) deceleration. Acceleration represents an intention of a given vehicle to achieve a certain speed. On the other hand, deceleration occurs as a result of the following vehicle driving at a speed that is lower than the desired speed. Based on the AIMSUN car-following model, the maximum speed *Va*(*n*, *t* + *T*) to which vehicle *n* is able to accelerate at the time (*t* + *T*) can be calculated as follows [35]:

$$V\_a(n, t + T) = V(n, t) + 2.5 \cdot a(n) \cdot T \cdot \left[1 - \frac{V(n, t)}{V^\*(n)}\right] \cdot \sqrt{0.025 + \frac{V(n, t)}{V^\*(n)}}\tag{1}$$

where:

*V*(*n*, *t*)—the speed of vehicle *n* at time *t* (m/sec); *V* ∗ (*n*)—the desired speed of vehicle *n* for a given roadway section (m/sec); *a*(*n*)—the maximum acceleration of vehicle *n* (m/sec<sup>2</sup> ); *T*—the reaction time (sec).

The maximum speed *Vb*(*n*, *t* + *T*) to which vehicle *n* is able to accelerate at the time (*t* + *T*), taking into account the vehicle characteristics and the limitations that are imposed by preceding vehicle (*n* − 1), can be computed using the following equation [35]:

$$V\_b(n, t + T) = d(n) \cdot T + \sqrt{\frac{d(n)^2 \cdot T^2 - d(n) \cdot [2 \cdot \{\mathbf{x}(n - 1, t) - s(n - 1) - \mathbf{x}(n, t)\} \cdot \mathbf{T} }{-V(n, t) \cdot T - \frac{V(n - 1, t)^2}{d(n - 1)} \, ]} \tag{2}$$

where:

*d*(*n*)—the maximum deceleration desired by vehicle *n* (m/sec<sup>2</sup> ); *x*(*n*, *t*)—the position of vehicle *n* at time *t* (m); *x*(*n* − 1, *t*)—the position of the preceding vehicle (*n* − 1) at time *t* (m);

*s*(*n* − 1)—the effective length of the preceding vehicle (*n* − 1) (m);

´*d*(*n* − 1)—the deceleration desired by the preceding vehicle (*n* − 1) (m/sec<sup>2</sup> )

Based on Equations (1) and (2), the definitive speed of vehicle *n* for a time interval (*t*, *t* + *T*) can be calculated as follows [35]:

$$V(n, t + T) = \min\{V\_a(n, t + T), V\_b(n, t + T)\}\tag{3}$$

SimTraffic relies on two car-following models: (a) fast following model and (b) slow following model. The fast following model is used for the cases when the leading vehicle speed is above 0.6 m/sec. On the other hand, the slow following model is applied for the slow-moving or stopped leading vehicle. The distance between vehicles or distance to the stopping point (*D*) can be calculated based on the following relationship [36]:

$$D = X^L - L^L - D^B - X^S \tag{4}$$

where:

*X <sup>L</sup>*—the position of the lead vehicle or stopping point (m); *L <sup>L</sup>*—the length of the lead vehicle (m; 0 for stopping point);

*DB*—the distance between (assumed to be 1.5 m);

*X <sup>S</sup>*—the position of the subject vehicle (m).

In SimTraffic, the stopped vehicles will not start moving until the distance to the leading vehicle reaches 1.5 m. The latter creates a startup reaction time of approximately 1.0 sec per vehicle [36]. For example, the 10th vehicle will not enter the network after approximately 10.0 sec the first vehicle entered the network. The following formula is used by SimTraffic to estimate the distance between vehicles, adjusted for the speed differential and reduced by the traveling vehicle's desired headway (*DSa f e*, m) [36]:

$$D^{Saf\varepsilon} = D + \frac{\min\left[\left(S^L\right)^2 - \left(S^S\right)^2; 0\right]}{2\cdot a} - S^S \cdot H \tag{5}$$

);

where:

*S <sup>L</sup>*—the speed of the leading vehicle (m/sec); *S <sup>S</sup>*—the speed of the subject vehicle (m/sec); *a*—the vehicle deceleration (assumed to be 1.2 m/sec<sup>2</sup>
