# Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN

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## Abstract

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## 1. Introduction

_{2}), hydrocarbon (HC), and nitrogen oxides (NOx) can be predicted for different modal operations from driving cycle data of light-duty vehicles in different driving conditions. However, the emission model should be corrected to accurately calculate CO

_{2}emissions on urban roads for a traffic speed below 20 km/h [26]. In turn, the Vehicle Specific Power (VSP) methodology is able of capturing the dependence of emissions on speed and its changes, and the effect of roadway grade on engine power demand [14]. In this view, the EPA’s MOtor Vehicle Emission Simulator (MOVES) estimates emissions for mobile sources at the national-level, county-level and project-level for criteria air pollutants, greenhouse gases and air toxics, and uses VSP and instantaneous speed as basic variables [27]. Other models have been developed for passenger cars with the purpose of linking real-world emissions to driving cycle characteristics at a microscale level and have been already employed for traffic management, control measures, air quality modelling; e.g., [28]. The use of Global Positioning System (GIS) technologies and on-board diagnostics devices in collecting second-by-second trajectory data give now great flexibility to calculate vehicle emissions in the real-world, since the emissions estimates can be allocated spatially [29,30]. Literature also informs on capabilities of microscopic traffic simulation models to produce emissions and fuel consumption data related to dynamic traffic-flow conditions on the road entity or network [31]. However, there is still not enough evidence to suggest that accurate vehicle speed and acceleration data (or their probability distributions) may be provided due to inadequate car-following equations and challenges with modelling the driver behaviour on multi-lane roadways [32]. Nevertheless, research efforts to improve both capability of microsimulation models to replicate realistic traffic behaviour or vehicle trajectory data, and traffic generation accuracy at the network or single node level, can be especially useful for managing traffic safety and environmental issues on urban roads because microsimulation allows the modelling of engineering solutions for road infrastructures and their traffic conditions, sometimes difficult to observe in the real world or not yet implemented in the field.

#### The Aim of the Paper

## 2. Literature Review

^{®}software to assess environmental performances at roundabouts with right-turn bypasses under increasing entering traffic volumes. Although roundabouts may be a cost-effective solution, the macroscale level tools used did not allow us to investigate in greater depth the dependence of emissions on driver behaviour. To optimize roundabout modelling, Lakuari et al. [42] employed numerical simulation to predict CO

_{2}emissions. Repeated changes in vehicle speed in the ring resulted in greater CO

_{2}emission rates than at entry or exit lanes, whereas CO

_{2}emissions reduced where many circulating vehicles slowed down or stopped because of the vehicles entering the roundabout without a safe gap to the vehicles in the circulating lane. However, emissions caused by frequent stops-and-go in high traffic conditions could be reduced by using traffic lights at entries. Other experiences concerned the employ of a hierarchical Bayesian regression analysis to model speed profiles of different drivers at roundabouts which can be useful in determining individual or group-wise emissions estimates [43]. However, the above papers concerned the emission testing at roundabouts based on previously developed general emission models. Thus, given the specific characteristics of geometry and traffic on roundabouts, there is still a need to develop emission models for exhaust gases for roundabouts based on real data.

_{2}emissions and fuel consumption on arterial roads have been achieved coupling dynamic micro traffic models with instantaneous emissions models; however, the large computation times especially for large-scale urban networks is a drawback of this kind of combination [51]. In turn, Stogios et al. [52] examined the effects of driving settings with Automated Vehicles (AVs) on greenhouse gas emissions (GHG) at urban corridors by combining traffic microsimulation and emissions modelling. The results highlighted that there is potential for up to 24 percent in GHG emission reduction when the effects of vehicle powertrain technology are included; no significant reductions of GHG emission were found when AVs alone characterized driving settings. Thus, the value of the study is to open interesting scenarios on autonomous driving impacts on traffic.

- (1)
- Societal—It allows to send the collected trajectory in a central management platform able of processing a considerable amount of data, converting them into digital information to estimate traffic emissions from the mobile source, and then, returning data to the community of users equipped with their smartphones to collectively share information, for instance, about hotspot emission locations at urban roundabouts;
- (2)
- Scientific—It identifies certain parameters of driving behaviour using a traffic modelling approach aimed at accurately analysing and comparing road units (i.e., road segments or road intersections, and so on) when changes in design or operation are considered from an environmental point of view as the life cycle thinking approaches strongly require.

## 3. Materials and Methods

#### 3.1. Field Data Collection

^{2}) at time t

_{2}(s), while v

_{1}is the speed (m/s) at time t

_{1}(s), and v

_{2}is the speed (m/s) at time t

_{2}(s) [58]. According to [59], acceleration manoeuvre ended in the event that the increment in speed between two consecutive data points was less than 0.1 m/s

^{2}for the next 5 s. Similarly, deceleration values have been determined from the time onwards where its absolute values from Equation (1) were greater than or equal to 0.1 m/s

^{2}for 5 consecutive seconds [58,60]. The observed speed profiles of all the sampled roundabouts were divided into two subsets depending on the driving direction where the corresponding trajectories were experienced in the field (see directions AB and BA in Figure 2). An analogy among the curvilinear paths of the test vehicle in the two driving directions has been found. To test whether the direction AB was the same as BA, statistical tests were performed to compare the two corresponding subsets of data. The two-sample t-test was done to determine if the means of the two subsets of data were significantly different from each other. The two-tailed F-test was also done to answer the question whether the variances of the two samples were equal against the alternative that they were not (see Table 2 and Table 3 for the summary statistics). Based on the p-values in the tables, it cannot conclude that a significant difference exists between the two driving directions in all the sampled roundabouts; in turn, the F-test results show that there is not enough evidence to reject the null hypothesis that the two sample variances are equal at the 0.05 significance level.

#### 3.2. Characterisation of the Speed-Time Profiles

#### 3.3. The VSP Methodology

_{r}and aerodynamic drag F

_{a}, and to increase the potential P

_{e}and kinetic energies K

_{e}of the vehicle as follows [14]:

^{2}); A is the coefficient associated with tire rolling resistance (kW·s/m), and B is the coefficient associated with the mechanical rotating friction and higher-order rolling resistance losses both expressed (kW·s

^{2}/m

^{2}); C is the coefficient associated with the aerodynamic drag (kW s

^{3}/m

^{3}); m expresses the mass for the specific vehicle type in metric ton; g is the acceleration due to gravity; sin θ is the road grade. Thus, the VSP can be equal to zero under zero vehicle speed or idling, while the VSP is positive (or negative) during acceleration (or deceleration). Given the impact of various factors influencing VSP and the variability in instantaneous vehicle emissions on road segments of different types of roads, there is the need to promote the development of a binning process in order to reduce variability found in the cases examined [15].

_{2}, CO, NOx, and HC that is fixed for a specific vehicle type (regulation class, fuel, model, year, mileage, or weight). Since a Light Passenger Diesel Vehicle (LPDV) was used as a test vehicle in this study, Table 4 depicts each interval of power requirements corresponding to each 14 VSP mode, and CO

_{2}, CO, NOx, and HC emission factors by VSP mode for diesel powertrain [24,55]; the emission rates for light passenger gasoline vehicles and light commercial diesel vehicles can be found in [24]. Figure 4 shows the relative frequencies of time spent in each VSP mode for representative speed-time profiles experienced by the “sentry” test vehicle through the sampled roundabouts. The time percentages in both driving directions have been shown to be broadly consistent with each other both in the VSP modes 1 to 2 (deceleration) and 4 to 5 (acceleration); the vehicle spent a high proportion of time in VSP mode 4 or 5 as it exited the roundabout to reach the cruise speed, while the proportion of time sensibly appears to reduce from VSP mode 5 onward. In some cases, a low proportion of time still appears in 10 to 13 VSP modes denoting high acceleration events. Based on the speed-time profiles and the distribution of time spent in each VSP mode, emissions by source pollutant were estimated [24]:

_{ij}is the total emission for the speed-time profile i and pollutant j (g); k is the label for second of travel (s); F

_{kj}is the emission factor for each pollutant j in label for second k (g/s); N

_{k}is the total seconds (s). Equation (5) was used to calculate second-by-second emission rates for each speed-time profile experienced by the test vehicle; the total emission by pollutant at each roundabout can be calculated as average of emissions for each pollutant and speed-time profile [62].

_{2}emissions from the time spent in each VSP mode and the second-by-second emission rates for the speed profiles through the Roundabouts 2 and 4 in Figure 2 along the entire distance travelled on the field. The distributions of CO

_{2}emissions for the other roundabouts exhibited similar patterns. The relative increase in the percentage of CO

_{2}emissions with the distance from the entry resulted highest in acceleration and short stop-and-go events, the slope being the steepest. In this regard, Figure 5a shows that the test vehicle while interacting with the other vehicles in traffic experienced short stop-and-go and produced high percentages of total CO

_{2}emissions during the acceleration from zero to the cruise speed. Repeated speed changes in the circulatory roadway caused greater CO

_{2}emission rates than at entries. The relative increase of CO

_{2}emissions percentage with the distance was higher in the acceleration mode especially when the test vehicle got in the circulatory roadway with a minimum speed and started accelerating to reach the desired speed to exit (see Figure 5b). According to previous experiences on roundabouts in Palermo City, Italy [62] acceleration events in the circulating and exiting areas contributed to more than 25 percent of the emissions for a given speed profile.

## 4. AIMSUN Modelling

#### 4.1. AIMSUN Calibration

^{2}, and normal deceleration having a default value of 4 m/s

^{2}. A sensitivity analysis explored the effects of different combinations of model parameters on the modelled outputs so that they matched or were comparable with the real-world observations. Calibration under the mean values of the 85th or 95th percentiles of accelerations and decelerations extracted from all the field-observed trajectories travelled through the sampled roundabouts regardless of the driving direction best fitted the observed data (see Table 3); it also provided VSP distributions closer to field data than the distribution under default parameters. No distributions of 85th or 95th percentile desired speeds were defined since the maximum speeds from empirical data appropriately represented the maximum speeds desired by drivers under normal circumstances. By way of example, Figure 7a shows the comparison between the observed and simulated speed profiles for Roundabout 5 in Figure 2e under default parameters and calibration with 85th and 95th percentile parameters. Figure 7b shows, in turn, an example of scattergram analysis done to compare the observed versus simulated speeds under calibration with the 95th percentile parameters extracted from all the field-observed trajectories experienced by the test vehicle through the roundabouts regardless of driving direction. The same figure shows the regression lines of observed versus simulated speeds at the detection stations plotted along with the 95% prediction interval; the R

^{2}values and the fact that only a few points fall outside the confidence band in both graphs imply that the model could be accepted as significantly close to the reality. The results confirmed improvements in vehicle speed after calibration, while traffic volumes resulted slightly modified. Encouraging results under calibrated parameters were also obtained in terms of Geoffrey E. Havers’ statistic (GEH) [31]. With reference to Figure 7b, the GEH values of 100% for each driving direction meant that the deviation of the simulated speed values under calibrated parameters with respect to the corresponding measurements resulted in less than 5 in 100% of the cases and the model could be accepted.

_{obs}and µ

_{sim}) are equal for the two samples made by VSP distributions based on the observed and simulated speed-time profiles for the movements AB (or BA) through each roundabout; the null hypothesis that the two means are equal is rejected if |t| > t

_{1-α/2,N}, where t

_{1-α/2,N}is the critical value of the t distribution with N degrees of freedom at the significance level of α = 0.05. Table 5 shows the results of the t-test for the observed and simulated VSP distributions in the driving direction AB through each roundabout in Figure 2. Specifically, it can be deduced that there is no significant difference in the two distributions of the observed and simulated VSP values by driving direction; analogous results have been also obtained for the opposite direction BA.

^{2}(that is the mean value of the distribution of the 85th percentile accelerations observed in the field); thus, there would be modelling no realistic scenario for a vehicle entering the simulated roundabout network model, if it could not achieve any instantaneous accelerations greater than 1.0 m/s

^{2}when travelling unconstrained below the speed limit. Among other things, the vehicles took a longer time to travel the circulatory roadway without significant changes in speed. Calibrating the model with 95th percentile values of the relevant parameters observed in the field at the existing roundabouts, in turn, tended to be more effective in producing VSP distributions enough consistent with what was drawn from the observed trajectory data. However, in this study, the absolute values of emissions were not of much concern given the potential for differences depending on the test vehicle characteristics; among the other things, the intent of this paper was not to derive definitive emission inventories but to explain the relative emission differences associated with various values of acceleration and deceleration.

#### 4.2. Results

_{2}, CO, NOx and HC. Emissions for each source pollutant and representative speed profile by driving direction were calculated employing Equation (5) based on the time spent by the test vehicle in each VSP mode and the second-by-second emission rates in Table 4.

_{2}emissions in Figure 9a show that the percentage variations between the observed data and those simulated with the default parameters of AIMSUN were significantly higher than 5% (in the case of Roundabouts 1, 3 and 4 even higher than 15%); under simulation with the 85th percentiles of the acceleration and deceleration, the simulated values underestimated the observed ones and also showed percentage reductions greater than or equal to 5% (Roundabouts 1, 2, 4, 5). Emission values simulated with the 95th percentiles of the acceleration and deceleration were close to the observed ones (Roundabouts 1 and 6) and showed percentage increases less than 5%; only a percentage reduction of 1% resulted on Roundabout 2, however less than the reduction percentage found under 85th percentiles of the acceleration and deceleration (approximately equal to 11% on the same roundabout).

_{X}+ HC) emissions: under simulation with the default parameters of AIMSUN, the percentage increases between the observed and simulated data were significantly higher than 25%, while under simulation with the 85th percentile values of the acceleration and deceleration, the percentage reductions were greater than or equal to 5% (Roundabouts 2, 4, 5) or the percentage increase was around 7% for Roundabout 6. In turn, under simulation with the 95th percentiles of the acceleration and deceleration, the simulated values showed a certain variability by roundabout where they were close to the observed data or having slight percentage reductions (e.g., on Roundabouts 2 and 4), however less than under simulation with the 85th percentile values of the relevant parameters on the same roundabouts. In order to ensure the validity of the methodological approach proposed in this paper and to confirm its applicability and reproducibility, the conversion of the Roundabout 2 in Figure 2b into a turbo counterpart was conceptualized based on [68]. This roundabout was chosen as an example with reference to its typical design. Speed profiles were collected again at Roundabout 2 during the morning (7:00–8:00 a.m.) and afternoon peak hours (6:00–8:00 p.m.) on weekdays (Tuesday to Friday) in July 2020.

_{2}emissions for the two roundabout layouts which will be discussed in the next section.

## 5. Discussion

_{2}and CO emissions lower than other cases. Specifically, the results concerning CO

_{2}and CO emissions in Figure 9a,b show that the percentage variations between the observed data and those ones simulated with the default parameters of AIMSUN were significantly higher than 5%, and in some cases even higher than 15% (Roundabouts 1, 3 and 4 for CO

_{2}and Roundabouts 1 and 4 for CO). Under simulation with the 85th percentiles of the acceleration and deceleration, the percentage reductions between observed and simulated data were greater than or equal to 5% (Roundabouts 1, 2, 4, 5 in the case of CO

_{2}; Roundabouts 1,2, 5,6 in the case of CO). Simulation with the 95th percentiles of the acceleration and deceleration provided simulated values close to those observed (Roundabouts 1, 2 and 6 in the case of CO

_{2}and Roundabouts 4, 5 and 6 in the case of CO) with increases from about 1 to 3%. The results for (NOx + HC) emissions, in turn, showed a certain variability by roundabout where simulations with the 95th percentiles of the acceleration and deceleration have returned both slight percentage increases and reductions between observed and simulated data, but, in the complex, smaller in absolute value than simulation under the 85th percentile values of the relevant parameters. However, the results may have been influenced by the selected test vehicle driven in the field, the geometric design of the sampled roundabouts not always corresponding to typical layouts, or the calibration process done to adjust those parameters which allowed the model to better match the observed emissions.

_{2}emissions only through the existing roundabout and turbo roundabout. According to the literature [70], the results showed that higher time was spent in acceleration through the turbo roundabout than the roundabout as typical standard for turbo roundabouts. However, under the low traffic volume conditions surveyed in the field, the conversion of a two-lane roundabout to a turbo roundabout gave a comparable amount of emissions; thus, the two-lane roundabout still remains as the more appropriate layout in the context of installation under examination. In general, the turbo roundabout option still represents the best solution from a safety point of view when a two-lane roundabout instead of a single-lane roundabout should be designed unless a multi-lane roundabout remains the preferred option if a maximum output of capacity is expected [71].

## 6. Conclusions

- (1)
- The selected test vehicle driven in the field;
- (2)
- Interactions with pedestrians or cyclists;
- (3)
- Variability in driving behaviour profiles experienced in the field;
- (4)
- Comparison of emissions just for one diesel car;
- (5)
- Roundabouts located in flat roads;
- (6)
- The selected driving movements here considered.

- the chance to expand the roundabout sample in order to afford the general validation of the proposed methodology and to make a correlation between the prevailing geometric characteristics and the results obtained in terms of emissions; this is closely linked both to the use of more sentinel vehicles to better characterize the speed profiles experienced through the road units, given that the relative occurrence of each possible profile may be sensitive to the prevailing traffic levels or entry demand;
- the calibration process that should also include the “global parameters” or further “local parameters” of AIMSUN for their possible effects on the simulated vehicle activity, however in combination with the “vehicle attributes” that were here fine-tuned based on the percentile values extracted from the parameter distributions surveyed in the field;
- the transferability of the methodology should be tested with reference to other design alternatives to assess the environmental effects due to the conversion of an existing layout to another with similar space footprint, and to estimate the life-cycle costs of intersection design alternatives before the installation in the real world.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Schematic aerial view of Auto-Cad figures for the sampled roundabouts in the road network of Palermo City, Italy, each of them named as follows: (

**a**) Roundabout 1; (

**b**) Roundabout 2; (

**c**) Roundabout 3; (

**d**) Roundabout 4; (

**e**) Roundabout 5; (

**f**) Roundabout 6. Note: each image contains the origin or destination (A, B) of through movements travelled by the test vehicle in the driving directions from A to B and from B to A.

**Figure 4.**Relative frequencies of time spent in VSP modes at the sampled roundabouts in Figure 2 as follows: (

**a**) Roundabout 1; (

**b**) Roundabout 2; (

**c**) Roundabout 3; (

**d**) Roundabout 4; (

**e**) Roundabout 5; (

**f**) Roundabout 6. Note: direction AB means the through movement from A to B in Figure 2; direction BA means the through movement from B to A in Figure 2.

**Figure 7.**Observed vs. simulated data at Roundabout 5 (directions AB and BA in Figure 2e): (

**a**) speed profiles; (

**b**) regression line of observed vs. speeds simulated under calibration with 95th percentile parameters. Note: observed stands for observed data; simulation (default) stands for default data; simulation (85th percentile) or simulation (95th percentile) stand for simulation under parameters calibrated with the mean values of the 85th or 95th percentiles of the values of acceleration and deceleration observed in the field.

**Figure 8.**The relative frequencies of time spent in VSP modes under simulation with the default parameters and calibration with 85th or 95th percentile values of the selected vehicle attributes of AIMSUN for: (

**a**) Roundabout 2, movement AB, in Figure 2b; (

**b**) Roundabout 4, movement AB, in Figure 2d. Note: simulation (default) stands for default data; simulation (85th) or simulation (95th) stand for simulation under parameters calibrated with the mean values of the 85th or 95th percentiles of the values of acceleration and deceleration observed in the field.

**Figure 9.**Observed versus simulated emissions at sampled roundabouts (through movements): (

**a**) CO

_{2}; (

**b**) CO; (

**c**) NOx + HC.

**Figure 10.**Turbo roundabout versus roundabout: (

**a**) speed-time profiles; (

**b**) simulated CO

_{2}emissions for through movements.

No | Entry (Exit) | Outer Diameter [m] | Entry (Exit) Lane Width ^{1} [m] | Ring Width [m] | Entry Traffic ^{2} [vph] | Conflicting Traffic ^{3} [vph] | Entry (Exit) Speed ^{4} [km/h] | Circulating Speed [km/h] |
---|---|---|---|---|---|---|---|---|

1 | 3 (4) | 48.0 | 3.50 (3.50) | 7.00 | 1576 | 1508 | 22.1 (30) | 18.2 |

2 | 4 (4) | 80.0 | 4.50 ^{5} (4.50) | 8.00 | 3984 | 3196 | 25.9 (36) | 23.1 |

3 | 4 (4) | 50.0 | 3.50 (3.50) | 9.00 | 2336 | 1904 | 23.3 (31) | 19.9 |

4 | 4 (4) | 60.0 | 4.75 ^{6} (4.75) | 10.00 | 1306 | 1372 | 29.8 (42) | 24.8 |

5 | 4 (4) | 80.0 | 4.00 (5.00 ^{7}) | 9.00 | 3992 | 2971 | 25.1 (35) | 23.1 |

6 | 4 (4) | 80.0 | 5.00 (4.50) | 10.00 | 988 | 972 | 30.0 (38) | 25.5 |

^{1}Entry width before widening the entry roadway or before the by-pass for right turns.

^{2}Average values of traffic volumes (in vehicles per hour—vph) from the right and left lanes videotaped during the morning peak period (7:00–8:30 a.m.) where the entry traffic distribution of 60-40 between major and minor roads was observed.

^{3}Average values of traffic volumes (all circulating lanes) observed for the morning peak period (7:00–8:30 a.m.).

^{4}Average values of speeds at the entry and exit lines.

^{5}5.00 m (4.00 m) for the one-lane entry (exit) on minor street.

^{6}3.50 m for the one-lane entry (exit) on minor street.

^{7}4.50 m for the one-lane exit on minor street.

**Table 2.**Summary statistics for the distributions of key kinematic parameters collected in the field.

Parameter | Maximum Speed [m/s] | Maximum Acceleration [m/s ^{2}] | Maximum Deceleration [m/s ^{2}] |
---|---|---|---|

μ_{AB} ^{1} (s.e.) | 14.459 (0.384) | 1.770 (0.077) | 2.498 (0.108) |

μ_{BA} ^{1} (s.e.) | 14.117 (0.357) | 1.553 (0.074) | 2.417 (0.111) |

95% c.i. for difference in means | (−0.700, 1.385) | (0.004, 0.430) | (−0.227, 0.390) |

t_{0.05,92} value ^{2} | 0.652 | 2.023 | 0.522 |

t-critical value | 1.986 | 1.986 | 1.986 |

p-Value (α = 0.05)^{3} | 0.516 | 0.05 | 0.603 |

F_{0.05,46,46} value ^{4} | 1.158 | 1.102 | 0.946 |

F-critical value | 1.796 | 1.796 | 1.796 |

F-probability | 0.620 | 0.740 | 0.850 |

^{1}µ

_{AB}and µ

_{BA}stand for the mean values of the samples of the observations of each parameter in AB and BA directions;

^{2}t-value is the result of the two-sample t-test done to compare the equality of the means (µ

_{AB}and µ

_{BA}) of samples from two populations with equal sample size: reject the null hypothesis that the two means are equal if |t| > t

_{1-α/2,N}where t

_{1}-

_{α/2,N}is the critical value of the t distribution with N degrees of freedom at the significance level α = 0.05);

^{3}α is the significance level;

^{4}F-value is the result of the two-tailed F-test done to answer the question whether two samples come from populations with equal variances (the hypothesis that the two variances were equal is rejected if $F>{F}_{\alpha /2,{N}_{1}-1,{N}_{2}-1}$, where ${F}_{\alpha /2,{N}_{1}-1,{N}_{2}-1}$ is the critical value of the F distribution with N

_{1}-1 and N

_{2}-1 degrees of freedom at the significance level of α = 0.05).

**Table 3.**Summary statistics for the distributions of the 85th or 95th percentile accelerations and decelerations observed in the field.

Parameter | 85th Percentile Acceleration [m/s^{2}] | 95th Percentile Acceleration [m/s ^{2}] | 85th Percentile Deceleration [m/s ^{2}] | 95th Percentile Deceleration [m/s ^{2}] |
---|---|---|---|---|

μ_{AB} ^{1} (s.e.) | 0.916 (0.030) | 1.286 (0.039) | 1.423 (0.082) | 2.054 (0.089) |

μ_{BA} ^{1} (s.e.) | 0.868 (0.025) | 1.195 (0.042) | 1.251 (0.059) | 1.882 (0.085) |

95% c.i. for difference in means | (−0.031, 0.126) | (−0.024, 0.205) | (−0.022, 0.381) | (−0.073, 0.417) |

t_{0.05,92} value ^{2} | 1.205 | 1.56 | 1.76 | 1.393 |

t-critical value | 1.986 | 1.986 | 1.986 | 1.986 |

p-Value (α = 0.05) ^{3} | 0.231 | 0.121 | 0.10 | 0.167 |

F_{0.05,46,46} value ^{4} | 1.362 | 1.18 | 1.76 | 1.081 |

F-critical value | 1.796 | 1.796 | 1.796 | 1.796 |

F-probability | 0.30 | 0.60 | 0.10 | 0.80 |

^{1}µ

_{AB}and µ

_{BA}stand for the mean values of the samples of the observations of each parameter in AB and BA directions;

^{2}t-value is the result of the two-sample t-test done to compare the equality of the means (µ

_{AB}and µ

_{BA}) of samples from two populations with equal sample size: reject the null hypothesis that the two means are equal if |t| > t

_{1-α/2,N}where t

_{1}-

_{α/2,N}is the critical value of the t distribution with N degrees of freedom at the significance level of α = 0.05);

^{3}α is the significance level;

^{4}F-value is the result of the two-tailed F-test done to answer the question whether two samples come from populations with equal variances (the hypothesis that the two variances were equal is rejected if $F>{F}_{\alpha /2,{N}_{1}-1,{N}_{2}-1}$, where ${F}_{\alpha /2,{N}_{1}-1,{N}_{2}-1}$ is the critical value of the F distribution having N

_{1}-1 and N

_{2}-1 degrees of freedom at the significance level of α = 0.05).

VSP Mode | Kw/ton ^{2} | Mean Modal Emission Rates (g/s) | |||
---|---|---|---|---|---|

CO_{2} | CO | NO_{x}, | HC | ||

1 | VSP < −2 | 0.21 | 0.00003 | 0.0013 | 0.00014 |

2 | −2 ≤ VSP < 0 | 0.61 | 0.00007 | 0.0026 | 0.00011 |

3 | 0 ≤ VSP < 1 | 0.73 | 0.00014 | 0.0034 | 0.00011 |

4 | 1 ≤ VSP < 4 | 1.50 | 0.00025 | 0.0061 | 0.00017 |

5 | 4 ≤ VSP < 7 | 2.34 | 0.00029 | 0.0094 | 0.00020 |

6 | 7 ≤ VSP < 10 | 3.29 | 0.00069 | 0.0125 | 0.00023 |

7 | 10 ≤ VSP < 13 | 4.20 | 0.00058 | 0.0155 | 0.00024 |

8 | 13 ≤ VSP < 16 | 4.94 | 0.00064 | 0.0178 | 0.00023 |

9 | 16 ≤ VSP < 19 | 5.57 | 0.00061 | 0.0213 | 0.00024 |

10 | 19 ≤ VSP < 23 | 6.26 | 0.00101 | 0.0325 | 0.00028 |

11 | 23 ≤ VSP < 28 | 7.40 | 0.00115 | 0.0558 | 0.00037 |

12 | 28 ≤ VSP < 33 | 8.39 | 0.00096 | 0.0743 | 0.00042 |

13 | 33 ≤ VSP < 39 | 9.41 | 0.00077 | 0.1042 | 0.00040 |

14 | VSP ≥ 39 | 10.48 | 0.00073 | 0.1459 | 0.00042 |

^{1}LPDV stands for Light Passenger Diesel Vehicles;

^{2}as calculated by Equation (5).

**Table 5.**The results of the two-sample t-test for the observed vs. simulated VSP distributions for the sampled roundabouts (direction AB in Figure 2).

VSP | μ_{obs} ^{1}(s.e.) | μ_{sim} ^{1}(s.e.) | 95% c.i. for Difference in Means | t_{(α = 0.05)}(d.f) ^{2} | t-Critical | p-Value (α = 0.05) ^{3} |
---|---|---|---|---|---|---|

Roundabout 1 | ||||||

obs. vs. default | 1.90 (0.80) | 3.15 (1.48) | (−4.628, 2.135) | 0.73 (55) | 2.004 | 0.46 |

obs. vs. 85th percentile | 0.98 (0.60) | 1.80 (1.09) | (−3.313, 1.667) | 0.66 (71) | 1.993 | 0.51 |

obs. vs. 95th percentile | 1.67 (0.77) | 1.96 (1.24) | (−3.228, 2.654) | 0.20 (55) | 2.004 | 0.85 |

Roundabout 2 | ||||||

obs. vs. default | 3.52 (3.06) | 5.11 (2.69) | (−9.747, 6.571) | 0.39 (58) | 2.002 | 0.69 |

obs. vs. 85th percentile | 3.56 (2.36) | 2.54 (1.23) | (−2.340, 8.371) | 1.13 (53) | 2.005 | 0.27 |

obs. vs. 95th percentile | 3.09 (2.02) | 2.80 (1.30) | (−3.135, 7.366) | 0.39 (64) | 1.997 | 0.43 |

Roundabout 3 | ||||||

obs. vs. default | 1.60 (1.75) | 3.31 (2.57) | (−7.969, 4.557) | 0.55 (49) | 2.009 | 0.58 |

obs. vs. 85th percentile | 1.52 (1.69) | 2.40 (1.47) | (−5.372, 3.621) | 0.39 (57) | 2.002 | 0.69 |

obs. vs. 95th percentile | 1.14 (1.48) | 1.44 (1.58) | (−4.630, 4.019) | 0.14 (68) | 1.995 | 0.88 |

Roundabout 4 | ||||||

obs. vs. default | 1.60 (1.57) | 5.99 (2.66) | (−10.51, 1.942) | 1.39 (46) | 2.014 | 0.17 |

obs. vs. 85th percentile | 1.75 (1.32) | 0.82 (1.05) | (−4.288, 2.420) | 0.55 (72) | 1.993 | 0.58 |

obs. vs. 95th percentile | 2.47 (0.85) | 2.32 (1.09) | (−2.607, 2.899) | 0.10 (88) | 1.980 | 0.91 |

Roundabout 5 | ||||||

obs. vs. default | 2.08 (1.21) | 3.67 (3.33) | (−8.749, 5.573) | 0.44 (39) | 2.023 | 0.66 |

obs. vs. 85th percentile | 2.75 (0.79) | 1.73 (0.80) | (−1.220, 3.259) | 0.90 (104) | 1.983 | 0.37 |

obs. vs. 95th percentile | 2.46 (0.96) | 1.77 (1.71) | (−3.242, 4.606) | 0.35 (68) | 1.995 | 0.73 |

Roundabout 6 | ||||||

obs. vs. default | 1.98 (1.21) | 1.09 (1.58) | (−3.089, 4.881) | 0.45 (74) | 1.994 | 0.65 |

obs. vs. 85th percentile | 2.01 (1.22) | 0.22 (1.22) | (−1.722, 5.294) | 1.01 (74) | 1.992 | 0.31 |

obs. vs. 95th percentile | 1.86 (1.28) | 0.98 (1.67) | (−3.331, 5.080) | 0.41 (70) | 1.996 | 0.68 |

^{1}µ

_{obs}and µ

_{sim}stand for the mean values of the samples of the observed vs. simulated VSP distributions for each roundabout in direction AB;

^{2}|t|value of the two-sample t-test done to compare the equality of the means of samples of two populations with equal sample size;

^{3}α = 0.05 is the significance level.

**Table 6.**The results of the two-sample t-test for comparing average emissions between observed and simulated data (default, 85th and 95th) by pollutant and by driving direction AB and BA.

Pollutants | μ_{AB} ^{1}(s.e.) | μ_{BA} ^{1}(s.e.) | 95% c.i. for Difference in Means | t_{(α = 0.05)}(d.f) ^{2} | t-Critical | p-Value (α = 0.05) ^{3} |
---|---|---|---|---|---|---|

CO_{2} | ||||||

default (AB) | 66.54 (4.89) | 69.41 (4.17) | (−17.20; 11.46) | 0.45 (10) | 2.228 | 0.66 |

default (BA) | 76.85 (8.78) | 74.64 (3.87) | (−19.19; 23.60) | 0.23 (7) | 2.364 | 0.82 |

85th percentile (AB) | 66.54 (4.89) | 64.27 (6.77) | (−16.36; 20.90) | 0.27 (9) | 2.262 | 0.80 |

85th percentile (BA) | 76.85 (8.78) | 65.99 (6.05) | (−12.92; 34.63) | 1.01 (9) | 2.262 | 0.34 |

95th percentile (AB) | 66.54 (4.89) | 71.26 (6.66) | (−23.14; 13.70) | 0.57 (9) | 2.262 | 0.60 |

95th percentile (BA) | 76.85 (8.78) | 67.80 (5.66) | (−14.24; 32.33) | 0.86 (9) | 2.262 | 0.42 |

CO | ||||||

default (AB) | 0.011 (0.001) | 0.012 (0.001) | (−0.005; 0.002) | 0.76 (10) | 2.228 | 0.46 |

default (BA) | 0.010 (0.001) | 0.011 (0.001) | (−0.003; 0.001) | 0.84 (10) | 2.228 | 0.42 |

85th percentile (AB) | 0.011 (0.001) | 0.010 (0.001) | (−0.003, 0.004) | 0.44 (10) | 2.228 | 0.67 |

85th percentile (BA) | 0.010 (0.001) | 0.010 (0.001) | (−0.002; 0.003) | 0.36 (10) | 2.228 | 0.73 |

95th percentile (AB) | 0.011 (0.001) | 0.012 (0.001) | (−0.004; 0.003) | 0.23 (10) | 2.262 | 0.82 |

95th percentile (BA) | 0.010 (0.001) | 0.010 (0.001) | (−0.002; 0.003) | 0.15 (10) | 2.262 | 0.88 |

NOx | ||||||

default (AB) | 0.29 (0.03) | 0.41 (0.04) | (−0.247, 0.009) | 2.06 (9) | 2.262 | 0.07 |

default (BA) | 0.27 (0.04) | 0.38 (0.04) | (−0.233, 0.013) | 1.99 (10) | 2.228 | 0.07 |

85th percentile (AB) | 0.29 (0.03) | 0.26 (0.03) | (−0.064, 0.133) | 0.78 (10) | 2.228 | 0.45 |

85th percentile (BA) | 0.27 (0.04) | 0.26 (0.03) | (−0.093; 0.114) | 0.22 (9) | 2.262 | 0.83 |

95th percentile (AB) | 0.29 (0.03) | 0.28 (0.04) | (−0.108, 0.117) | 0.10 (10) | 2.228 | 0.93 |

95th percentile (BA) | 0.27 (0.04) | 0.27 (0.03) | (−0.098; 0.109) | 0.12 (9) | 2.262 | 0.90 |

HC | ||||||

default (AB) | 0.007 (0.001) | 0.006 (0.001) | (−0.001; 0.003) | 0.87 (8) | 2.306 | 0.41 |

default (BA) | 0.006 (0.001) | 0.005 (0.001) | (−0.001, 0.002) | 0.85 (8) | 2.306 | 0.42 |

85th percentile (AB) | 0.007 (0.001) | 0.006 (0.001) | (−0.003; 0.003) | 0.27 (10) | 2.228 | 0.80 |

85th percentile (BA) | 0.006 (0.001) | 0.007 (0.001) | (−0.002; 0.0006) | 1.23 (10) | 2.228 | 0.25 |

95th percentile (AB) | 0.007 (0.001) | 0.006 (0.001) | (−0.002; 0.003) | 0.46 (9) | 2.262 | 0.65 |

95th percentile (BA) | 0.006 (0.001) | 0.006 (0.001) | (−0.001; 0.0012) | 0.03 (9) | 2.262 | 0.98 |

^{1}µ

_{AB}and µ

_{BA}stand for the mean values of the samples of the observed and simulated emissions for each pollutant and driving directions AB and BA;

^{2}|t|value of the two-sample t-test done to compare the equality of the means of samples of two populations with equal sample size;

^{3}α = 0.05 is the significance level.

Cross Section | [m] | |

Radius of the inside roadway, inner edge | R_{1} | 15.00 |

Radius of the inside roadway, outer edge | R_{2} ^{1} | 20.40 |

Radius of the outside roadway, inner edge | R_{3} ^{2} | 20.70 |

Radius of the outside roadway, outer edge | R_{4} ^{3} | 25.95 |

Inner or outer edge line offset | 0.45 | |

Inside lane | 4.35 | |

Divider inner or outer line offset | 0.20 | |

Divider | 0.30 | |

Outside lane | 4.25 | |

Inside roadway width | 5.50 | |

Outside roadway width | 5.25 | |

Shift 1—inside to middle | 5.15 | |

Shift 2—middle to outside | 4.95 | |

Bias 1 for R_{1} (Bias 2 for other radii) | 2.58 (2.48) | |

Arc centre bias for R_{1} (Bias 2 for other radii) | 2.60 (2.45) |

^{1}R

_{2}= R

_{1}+ inside roadway width-bias difference (differences match roadway widths);

^{2}R

_{3}= R

_{2}+ divider width;

^{3}R

_{4}= R

_{3}+ outside roadway width.

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**MDPI and ACS Style**

Acuto, F.; Coelho, M.C.; Fernandes, P.; Giuffrè, T.; Macioszek, E.; Granà, A. Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN. *Energies* **2022**, *15*, 1371.
https://doi.org/10.3390/en15041371

**AMA Style**

Acuto F, Coelho MC, Fernandes P, Giuffrè T, Macioszek E, Granà A. Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN. *Energies*. 2022; 15(4):1371.
https://doi.org/10.3390/en15041371

**Chicago/Turabian Style**

Acuto, Francesco, Margarida C. Coelho, Paulo Fernandes, Tullio Giuffrè, Elżbieta Macioszek, and Anna Granà. 2022. "Assessing the Environmental Performances of Urban Roundabouts Using the VSP Methodology and AIMSUN" *Energies* 15, no. 4: 1371.
https://doi.org/10.3390/en15041371