# Evaluation of the Effectiveness of Traffic Calming Measures by SPEIR Methodology: Framework and Case Studies

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Framework of SPEIR Methodology

- (1)
- determination of speed profiles for the survey site;
- (2)
- estimation of effectiveness indicators;
- (3)
- analysis and interpretation of results.

#### 2.1. Determination of Speed Profiles for the Survey Site

- Pneumatic tubes with automatic traffic counting systems: They are used for long surveys of vehicle speeds and annual average daily traffic counts. They are sufficiently accurate and inexpensive but lack flexibility in practice.
- Inductive loop detectors: Generally inexpensive and adaptable systems. However, they are not very flexible because they are permanently installed.
- Fixed RADAR stations: They can be installed on existing poles or on specially designed roadside structures. They are moderately expensive and can simultaneously measure the speeds of different vehicles in both directions of travel.

- Mobile RADAR stations and LiDAR guns: they have high versatility, precision and immediate correspondence between vehicles and measured speeds. However, they are very expensive.
- On-board diagnostic (OBD) black boxes: they allow knowing the exact location and speed of the vehicle through GPS systems. They are characterized by sufficient accuracy if the device is able to receive data from a sufficient number of satellites. However, a large number of devices may be required to obtain statistically significant samples.

#### 2.2. Estimation of Effectiveness Indicators

_{a}) evaluates the accumulated speeding along the entire road segment, while the second index (R

_{a}) evaluates the accumulated speed uniformity.

_{a}is the normalized relative area (per unit length) between the speed profile values that are above the speed limit and the speed limit line. The measure can be applied to individual speed profiles or to an operating speed profile. Given a speed limit, the areas between the speed profile and the speed limit line must be determined. Only the areas above the speed limit line (A

_{si}) must be considered in the measurement (Figure 2a). The accumulated speeding must be calculated using Equation (1) as the sum of the areas divided by the length of the segment (L). Consequently, E

_{a}is directly related to the cumulative speeding.

- E
_{a}: accumulated speeding (m/s); - ∑As
_{i}: sum of areas bounder between the speed profile and the speed limit where speed in higher than speed limit (m^{2}/s); - L: road section length (m).

**Figure 2.**Example of evaluation of the areas for the definition of the effectiveness indicators: (

**a**) accumulated speeding; (

**b**) accumulated speed uniformity.

_{a}is defined as the normalized relative area (per unit length) lying between the speed profile and the line of average speed. The measure can be applied to individual speed profiles or to the operating speed profile. The first step is to calculate the average speed of the speed profile along the road. In this way, the areas lying between the speed profile and the average speed line (A

_{ri}) are obtained (Figure 2b). The consistency measure is the sum of the absolute values of the areas divided by the length of the segment (L). Therefore, R

_{a}is inversely related to the accumulated speed uniformity. Equation (3) must be applied.

- R
_{a}: accumulated speed uniformity (m/s); - ∑Ar
_{i}: sum of areas in absolute values bound between the speed profile and the average speed (m^{2}/s); - L: road section length (m).

#### 2.3. Analysis and Interpretation of Results

_{a}is determined.

_{a}indicators. The simplest solution, but one that could prove ineffective or only partially effective, would be to modify the traffic calming measures at the study site and verify their effectiveness by reapplying the SPEIR methodology. In this case, there could be a risk that the new traffic calming measures installed at the survey site would still be insufficient to provide the desired safety performance.

## 3. Case Studies and Results

#### 3.1. Selection of Zones 30

#### 3.2. Speed Data Collection

#### 3.3. Determination of Speed Profiles

- In Zone 1, there are significant deviations of the speed from the average value. The latter coincides with the speed limit of 30 km/h. In particular, it can be observed how the speed peaks reached by the users are significantly reduced when the distances between the speed tables are reduced (e.g., between ST3 and ST4).
- In Zone 2, the speed profiles of the test drivers are quite homogeneous and there are no significant variations. However, it is obvious that the average speed values are higher than those prescribed in this Zone 30.
- In Zone 3, the speed profiles show little variation compared to the average, which is just above the 30 km/h speed limit. It is particularly noticeable that the speed profiles at the chicane fall much more below the average speed of the zone than on the central island.

#### 3.4. Estimation of Effectiveness Indicators

_{a}) were calculated in relation to the speed profiles of the 20 test drivers and for each zone that was the subject of the experimental study. They were calculated according to the procedure outlined in Section 2.2.

_{ave}) assumed by the users who carried out the different driving tests.

#### 3.5. Results

_{a}associated with the three Zones 30 analyzed. Both tests show that the three distributions of the variable ${\mathrm{E}}_{\mathrm{a}}^{*}$ and the variable R

_{a}are always statistically comparable to normal distributions (the tests always yield values of p-value > 0.05).

_{a}, while Figure 8 shows the boxplots associated with the distributions of R

_{a}for the three zones analyzed. This plot has been divided into three areas delimited by the thresholds given in Table 1.

## 4. Discussion

- No outliers were found in any of the three Zones. So, in any case, the TCMs do not lead to significant anomalies in the test drivers who participated in the experiment in terms of too high or too low speeds of the imposed limit.
- All three Zones have levels of effectiveness that exceed the threshold labeled as “good”. However, it should be noted that Zone 1, characterized by a median value of ${\mathrm{E}}_{\mathrm{a}}^{*}$ of 0.75, performs largely acceptably and ensures that the values between the second and third quartiles, representing 50% of the total, are kept within a narrow range (dispersion of 0.53) and between the thresholds for “good” and “acceptable”. Only in a few cases, as shown by the final value of the upper whisker of 1.04, does it border on what is considered a “poor” performance.
- Zone 3 provides an overall acceptable level of performance. The box is essentially bounded by the two threshold values (lower limit equal to 0.69 and upper limit equal to 1.014). However, the value at the end of the upper whisker of 1.40 shows how drivers can be misled into adopting speeds well above the limit imposed by the traffic signs.
- Zone 2 is the least successful in getting road users to obey the 30 km/h speed limit in effect on that section of road. The median value of ${\mathrm{E}}_{\mathrm{a}}^{*}$, which is 1.11, represents a “poor” level of performance. Moreover, the box is almost entirely above the line that establishes the worst performance of the Zone in question in terms of compliance with the speed limit. If we also consider that (a) the end of the upper whisker shows how the values of the fourth quartile of ${\mathrm{E}}_{\mathrm{a}}^{*}$ increase to a value above 1.60, (b) the first quartile is almost entirely contained in the part of the graph bounded below by the acceptance threshold; it becomes clear that the sequence of speed tables in Zone 2 is not an effective measure to ensure the dynamics “legally” required by a Zone 30.

- For all three Zones, there were no outliers for any test driver. This means that in no case do the traffic calming measures lead to very anomalous behavior in terms of uniformity of speed.
- The medians for Zones 2 and 3 take similar values (0.62 and 0.72). Thus, in both Zones, users are induced to equalize their speed very close to the average value. It is also interesting to note that 50% of the R
_{a}values are in an overall range between 0.48 and 0.83. This confirms the excellent response of the traffic calming measures in the two Zones to ensure a uniform speed. Instead, looking at the whiskers of the two boxplots, it can be seen that Zone 3 guarantees even more than Zone 2 a narrower dispersion of R_{a}values, actually containing all values between the minimum value of 0.36 and the maximum value of 1.03 (dispersion range 0.67). Zone 2, on the other hand, has a much wider dispersion of R_{a}values, equal to 1.03, although the end of the upper whisker, corresponding to R_{a}= 1.27, indicates in any case a “good” performance of the considered Zone. - Zone 1 influences user behavior, but to a lesser extent than the other two Zones. The median of R
_{a}, which is 1.15, is below the threshold of 1.5 and, thus, representative of a “good” condition, but is significantly higher than the values for the other two Zones. Additionally, in this case, 50% of the R_{a}values (height of the box) are distributed in a range with reduced amplitude (equal to 0.37) between the extreme values of 0.93 and 1.3. However, the analysis of the whiskers of the boxplot shows how the fourth quartile of the distribution, bounded above by the value 1.66, indicates that the area in question induces a certain number of users to reach speeds well above the average value; in these cases, the Zone actually presents an “acceptable” operating condition, even if it is far from the one considered “poor”.

_{a}using the boxplot technique allows us to confirm that Zone 3, characterized by the chicane and the central island, is the one that most affects the uniformity of the assumed speed of users who played the role of test driver. On the other hand, the succession of speed tables in Zone 1 leads to speed variations with respect to the average value, which, although a condition for the acceptance of the speed-calming measures offered, proves the inhomogeneous behavior of the users who carried out the test in this Zone. This result does not agree with the study of Agerholm et al. [47], which shows that chicanes lead to larger speed fluctuations before reaching the chicane compared to humps. However, it is necessary to point out the small number of studies analyzing the uniformity of the speed of TCMs.

_{a}. In order to derive a final judgment on the overall effectiveness of the three Zones studied, the following considerations were made, based on the simultaneous analysis of the two aforementioned indicators:

- Zone 2, although not able to guarantee the basic requirement of a Zone 30, i.e., the respect of the speed limit, is characterized by the presence of TCMs suitable to influence the behavior of road users, who are induced to travel through the Zone without particular variations compared to the average speed, which is always below 40 km/h. This means that this design solution could be suitable to be installed in a context where it is necessary to respect a speed limit higher than 30 km/h (e.g., a 40 zone).
- The TCMs in Zone 2 are of the same type (vertical misalignments) as in Zone 1 and differ in that the height of the speed boards is 4 cm in Zone 2 and 8 cm in Zone 1. This difference in height speaks in favor of the effectiveness of Zone 1, which is largely acceptable in terms of compliance with the speed limit and can also ensure good uniformity of speed.
- Zone 3, similar to Zone 1, guarantees compliance with the 30 km/h speed limit, although at a lower level of acceptance. On the other hand, Zone 3 offers the best performance in terms of uniformity of speed (100% of Ra indicator values are well below the “Good” threshold).

_{a}). This is certainly the original aspect of the SPEIR methodology proposed in this study. The authors strongly believe that the effectiveness of TCMs is not exclusively related to the value of speed reduction they produce, but also to the uniformity of speed, a parameter strongly related to the safety conditions of the site where the TCMs are installed.

## 5. Conclusions

- (1)
- It is possible to compare different design solutions and better understand their effectiveness, also in terms of reproducibility within the same site or in terms of implementation in urban contexts that one wants to adapt, for example, by establishing a Zone 30;
- (2)
- If the proposed methodology is applied internationally to evaluate the effectiveness of TCMs in different urban contexts and the results are made publicly available, an important database could be created within a reasonable period of time. Thanks to this database, the values of ${\mathrm{E}}_{\mathrm{a}}^{*}$ and R
_{a}corresponding to the different design configurations would be clearly known. In this way, the values of the two effectiveness indicators would be available for the different types of traffic calming measures (e.g., chicanes, speed tables, speed bumps, chokers, etc.) and their installation criteria (e.g., distance between bumps, characteristic length of the chicane, extent of narrowing). These indicators could, thus, be used as reference parameters in new designs or in functional adaptation measures; - (3)
- Achieving the objectives mentioned in the previous two points (comparison of different design solutions and creation of a reference database) can also be carried out using design scenarios created in simulated environments. The SPEIR methodology could be tested in virtual environments and, once validated, used to characterize different simulated configurations in which traffic calming measures are present through the proposed effectiveness indicators.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Balant, M.; Lep, M. Comprehensive Traffic Calming as a Key Element of Sustainable Urban Mobility Plans—Impacts of a Neighbourhood Redesign in Ljutomer. Sustainability
**2020**, 12, 8143. [Google Scholar] [CrossRef] - Dickerson, A.; Peirson, J.; Vickerman, R. Road Accidents and Traffic Flows: An Econometric Investigation. Economica
**2000**, 67, 101–121. [Google Scholar] [CrossRef] [Green Version] - Zang, K.; Batterman, S. Air pollution and health risks due to vehicle traffic. Sci. Total Environ.
**2013**, 15, 307–316. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fuks, K.; Moebus, S.; Hertel, S.; Viehmann, A.; Nonnemacher, M.; Dragano, N.; Möhlenkamp, S.; Jakobs, H.; Kessler, C.; Erbel, R.; et al. Long-term urban particulate air pollution, traffic noise, and arterial blood pressure. Environ. Health Perspect.
**2011**, 119, 1706–1711. [Google Scholar] [CrossRef] [Green Version] - Albalate, D.; Fageda, X. Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas. Sustainability
**2019**, 11, 5092. [Google Scholar] [CrossRef] [Green Version] - Granà, A.; Giuffrè, T.; Guerrieri, M. Exploring Effects of Area-Wide Traffic Calming Measures on Urban Road Sustainable Safety. J. Sustain. Dev.
**2010**, 3, 38–49. [Google Scholar] [CrossRef] [Green Version] - Useche, S.A.; Alonso, F.; Montoro, L. Validation of the walking behavior questionnaire (WBQ): A tool for measuring risky and safe walking under a behavioral perspective. J. Transp. Health
**2020**, 18, 100899. [Google Scholar] [CrossRef] - Pulvirenti, G.; Distefano, N.; Leonardi, S. Elderly perception of critical issues of pedestrian paths. Civ. Eng. Archit.
**2020**, 8, 26–37. [Google Scholar] [CrossRef] - Distefano, N.; Pulvirenti, G.; Leonardi, S. Neighbourhood walkability: Elderly’s priorities. Res. Transp. Bus.
**2021**, 40, 100547. [Google Scholar] [CrossRef] - Gálvez-Pérez, D.; Guirao, B.; Ortuño, A. Road Safety of Elderly Pedestrians in the Urban Context: An Approach Based on Infrastructure and Socioeconomic Variables. Transp. Res. Procedia
**2021**, 58, 254–261. [Google Scholar] [CrossRef] - Leonardi, S.; Distefano, N.; Pulvirenti, G. Identification of road safety measures for elderly pedestrians based on k-means clustering and hierarchical cluster analysis. Arch. Transp.
**2020**, 56, 107–118. [Google Scholar] [CrossRef] - WHO—World Health Organization. Global Status Report on Road Safety; World Health Organization: Geneva, Switzerland, 2018. [Google Scholar]
- European Commission. Facts and figures—Pedestrians. In European Road Safety Observatory; Directorate General for Transport; European Commission: Brussels, Belgium, 2021. [Google Scholar]
- ACI-ISTAT. Incidenti stradali in Italia (English translation: Road accidents in Italy). In Report of ACI-ISTAT—Anno 2019; ACI-ISTAT: Rome, Italy, 2020. [Google Scholar]
- ACI-ISTAT. Incidenti stradali in Italia (English translation: Road accidents in Italy). In Report of ACI-ISTAT—Anno 2020; ACI-ISTAT: Rome, Italy, 2021. [Google Scholar]
- Distefano, N.; Leonardi, S.; Consoli, F. Drivers’ Preferences for Road Roundabouts: A Study based on Stated Preference Survey in Italy. KSCE J. Civ. Eng.
**2019**, 23, 4864–4874. [Google Scholar] [CrossRef] - Ciampa, D.; Diomedi, M.; Giglio, F.; Olita, S.; Petruccelli, U.; Restaino, C. Effectiveness of unconventional roundabouts in the design of suburban intersections. Eur. Transp. Eur.
**2020**, 80, 1–16. [Google Scholar] [CrossRef] - Gonzalo-Orden, H.; Pérez-Acebo, H.; Linares Unamunzaga, A.; Rojo Arce, M. Effects of traffic calming measures in different urban areas. Transp. Res. Procedia
**2018**, 33, 83–90. [Google Scholar] [CrossRef] - Sharpin, A.B.; Adriazola-Steil, C.; Job, S.; Obelheiro, M.; Welle, B.; Imamoglu, C.T.; Bhatt, A.; Liu, D.; Lleras, N.; Luke, N. Low-Speed Zone Guide. 2021. Available online: https://files.wri.org/d8/s3fs-public/2021-05/WRI_LowSpeedZone_web.pdf?VersionId=mMwA8aq.BSpHZ6w97Z4E2NSd5o1UPN7B (accessed on 17 January 2022). [CrossRef]
- Kempa, J. Respecting a speed limit and its effectiveness in a traffic calming zone. IOP Conf. Ser. Mater. Sci. Eng.
**2019**, 603, 042068. [Google Scholar] [CrossRef] - Distefano, N.; Leonardi, S. Evaluation of the Benefits of Traffic Calming on Vehicle Speed Reduction. Civ. Eng. Arch.
**2019**, 7, 200–214. [Google Scholar] [CrossRef] - Vaitkus, A.; Čygas, D.; Jasiuniene, V.; Jateikiene, L.; Andriejauskas, T.; Skrodenis, D.; Ratkeviciute, K. Traffic Calming Measures: An Evaluation of the Effect on Driving Speed. Promet-Traffic Transp.
**2017**, 29, 275–285. [Google Scholar] [CrossRef] - Daniel, B.D.; Nicholson, A.; Koorey, G. Analysing speed profiles for the estimation of speed on traffic-calmed streets. Transp. Res. Rec.
**2011**, 20, 57–70. [Google Scholar] - Pérez-Acebo, H.; Ziółkowski, R.; Linares-Unamunzaga, A.; Gonzalo-Orden, H. A Series of Vertical Deflections, a Promising Traffic Calming Measure: Analysis and Recommendations for Spacing. Appl. Sci.
**2020**, 10, 3368. [Google Scholar] [CrossRef] - Gayathri, K.B. Evaluation of the Effectiveness of Traffic calming measures. In Proceedings of the International Conference on Systems, Energy & Environment (ICSEE), Singapore, 3–5 February 2021. [Google Scholar] [CrossRef]
- Ziółkowski, R. Speed Management Efficacy on National Roads—Early Experiences of Sectional Speed System Functioning in Podlaskie Voivodship. Transp. Probl.
**2018**, 18, 5–12. [Google Scholar] [CrossRef] [Green Version] - Zito, R.; Taylor, M.A.P. Speed profiles and vehicle fuel consumption at LATM devices. In Proceedings of the 18th ARRB Conference, Christchurch, New Zealand, 2–6 September 1996; Volume 18, pp. 391–406. [Google Scholar]
- Jurewicz, C.; Sobhani, A.; Woolley, J.; Dutschke, J.; Corben, B. Exploration of vehicle impact speed-injury severity relationships for application in safer road design. Transp. Res. Procedia
**2016**, 14, 4247–4256. [Google Scholar] [CrossRef] [Green Version] - Wann, J.P.; Poulter, D.R.; Purcell, C. Reduced sensitivity to visual looming inflates the risk posed by speeding vehicles when children try to cross the road. Psychol. Sci.
**2011**, 22, 429–434. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lobjois, R.; Cavallo, V. Age-related differences in street-crossing decisions: The effects of vehicle speed and time constraints on gap selection in an estimation task. Accid. Anal. Prev.
**2007**, 39, 934–943. [Google Scholar] [CrossRef] [PubMed] - Webb, E.A.; Bell, S.; Lacey, R.E.; Abell, J.G. Crossing the road in time: Inequalities in older people’s walking speeds. J. Transp. Health
**2017**, 5, 77–83. [Google Scholar] [CrossRef] - 20’s Plenty for Us. 20mph Limits Save Time and Improve Traffic Flow. 2012. Available online: http://www.20splentyforus.org.uk/BriefingSheets/20mph_Improves_Traffic_Flow.pdf (accessed on 10 January 2022).
- 20’s Plenty for Us. 20mph Limits Improve Air Quality Where People Live. 2010. Available online: http://www.20splentyforus.org.uk/BriefingSheets/pollutionbriefing.pdf (accessed on 10 January 2022).
- Jones, S.J.; Brunt, H. Twenty miles per hour speed limits: A sustainable solution to public health problems in Wales. J. Epidemiol. Community Health
**2017**, 71, 699–706. [Google Scholar] [CrossRef] - Brindle, R.E. Speed-Based Design of Traffic Calming Schemes. In Institute of Transportation Engineers (ITE) Annual Meeting 2005; ITE: Melbourne, Australia, 2005. [Google Scholar]
- Berloco, N.; Colonna, P.; Intini, P.; Masi, G.; Ranieri, V. Low-cost smartphone-based speed surveying methods in proximity to traffic calming devices. Procedia Comput. Sci.
**2018**, 134, 415–420. [Google Scholar] [CrossRef] - Kyriakoua, C.; Christodouloua, S.E.; Dimitrioua, L. Do Vehicles Sense, Detect and Locate Speed Bumps? Transp. Res. Procedia
**2020**, 52, 203–210. [Google Scholar] [CrossRef] - Dong, D.; Li, Z. Smartphone Sensing of Road Surface Condition and Defect Detection. Sensors
**2021**, 21, 5433. [Google Scholar] [CrossRef] - Tu, W.; Xiao, F.; Li, L.; Fu, L. Estimating traffic flow states with smart phone sensor data. Transp. Res. Part C
**2021**, 126, 103062. [Google Scholar] [CrossRef] - Polus, A.; Mattar-Habib, C. New Consistency Model for Rural Highways and Its Relationship to Safety. J. Transp. Eng.
**2004**, 130, 286–293. [Google Scholar] [CrossRef] - Moreno, A.T.; García, A. Use of speed profile as surrogate measure: Effect of traffic calming devices on crosstown road safety performance. Accid. Anal. Prev.
**2013**, 61, 23–32. [Google Scholar] [CrossRef] [PubMed] - Domenichini, L.; Branzi, V.; Meocci, M. Virtual testing of speed reduction schemes on urban collector roads. Accid. Anal. Prev.
**2018**, 110, 38–51. [Google Scholar] [CrossRef] [PubMed] - Dinasty Purnomo, A.; Dewi, H.; Syafi, I. Correlation analysis between speed bump dimensions and motorcycle speed in residential areas. MATEC Web Conf.
**2018**, 195, 04013. [Google Scholar] [CrossRef] - Antic, B.; Pesic, D.; Vujanic, M.; Lipovac, K. The influence of speed bumps heights to the decrease of the vehicle speed-Belgrade Experience. Saf. Sci.
**2013**, 57, 303–313. [Google Scholar] [CrossRef] - Bachok, K.S.; Mohamed, M.Z.; Ibrahim, M.; Hamsa, A.A.K. A theoretical overview of road hump effects on traffic speed in residential environments. Plan. Malays. J.
**2016**, 14, 343–352. [Google Scholar] [CrossRef] - Zhang, C.; Qin, S.; Yu, H.; Zheng, B.; Li, Z. A Review on Chicane Design based on Calming Theory. J. Eng. Sci. Technol. Rev.
**2020**, 13, 188–197. [Google Scholar] [CrossRef] - Agerholm, N.; Knudsen, D.; Variyeswaran, K. Speed-calming measures and their effect on driving speed—Test of a new technique measuring speeds based on GNSS data. Transp. Res. F Traffic Psychol. Behav.
**2017**, 46, 263–270. [Google Scholar] [CrossRef]

**Figure 3.**Schemes of traffic calming measures in the studied sites: (

**a**) Zone 1; (

**b**) Zone 2; (

**c**) Zone 3.

Effectiveness Indicators | Good | Acceptable | Poor |
---|---|---|---|

Accumulated speeding ${\mathrm{E}}_{\mathrm{a}}^{*}$ (m/s) | ${\mathrm{E}}_{\mathrm{a}}^{*}$ ≤ 0.7 | 0.7 < ${\mathrm{E}}_{\mathrm{a}}^{*}$ < 1.0 | ${\mathrm{E}}_{\mathrm{a}}^{*}$ ≥ 1.0 |

Accumulated speed uniformity R_{a} (m/s) | R_{a} ≤ 1.0 | 1.0 < R_{a} < 2.0 | R_{a} ≥ 2.0 |

Site | Speed Limit [km/h] | Length [m] | N. of Speed Profiles | S_{ave} [km/h] | R_{a(min)} [m/s] | R_{a(max)} [m/s] | R_{a(85)} [m/s] | ${\mathbf{E}}_{\mathbf{a}\left(\mathbf{min}\right)}^{\mathbf{*}}$ [m/s] | ${\mathbf{E}}_{\mathbf{a}\left(\mathbf{max}\right)}^{\mathbf{*}}$ [m/s] | ${\mathbf{E}}_{\mathbf{a}\left(\mathbf{85}\right)}^{\mathbf{*}}$ [m/s] |
---|---|---|---|---|---|---|---|---|---|---|

ZONE 1 | 30 | 447 | 20 | 30.00 | 0.78 | 1.66 | 1.32 | 0.51 | 1.04 | 0.91 |

ZONE 2 | 30 | 196 | 20 | 34.89 | 0.25 | 1.28 | 0.88 | 0.66 | 1.61 | 1.39 |

ZONE 3 | 30 | 280 | 20 | 32.84 | 0.36 | 1.03 | 0.87 | 0.55 | 1.41 | 1.06 |

**Table 3.**Statistical parameters related to the distribution of ${\mathrm{E}}_{\mathrm{a}}^{*}$ values.

${\mathbf{E}}_{\mathbf{a}}^{\mathbf{*}}$Distribution (ZONE 1) | Statistic | Std. Error |

Mean | 0.769990 | 0.0312493 |

Median | 0.749350 | |

Variance | 0.020 | |

Std. Deviation | 0.1397513 | |

Minimum | 0.5154 | |

Maximum | 1.0438 | |

Range | 0.5284 | |

${\mathbf{E}}_{\mathbf{a}}^{\mathbf{*}}$Distribution (ZONE 2) | Statistic | Std. Error |

Mean | 1.138506 | 0.0547420 |

Median | 1.113268 | |

Variance | 0.060 | |

Std. Deviation | 0.2448137 | |

Minimum | 0.6640 | |

Maximum | 1.6167 | |

Range | 0.9527 | |

${\mathbf{E}}_{\mathbf{a}}^{\mathbf{*}}$Distribution (ZONE 3) | Statistic | Std. Error |

Mean | 0.894378 | 0.0483706 |

Median | 0.923961 | |

Variance | 0.047 | |

Std. Deviation | 0.2163197 | |

Minimum | 0.5567 | |

Maximum | 1.4184 | |

Range | 0.8617 |

R_{a} Distribution (ZONE 1) | Statistic | Std. Error |

Mean | 1.129056 | 0.0527978 |

Median | 1.155217 | |

Variance | 0.056 | |

Std. Deviation | 0.2361188 | |

Minimum | 0.7866 | |

Maximum | 1.6668 | |

Range | 0.8801 | |

R_{a} Distribution (ZONE 2) | Statistic | Std. Error |

Mean | 0.656615 | 0.0607425 |

Median | 0.620068 | |

Variance | 0.074 | |

Std. Deviation | 0.2716487 | |

Minimum | 0.2557 | |

Maximum | 1.2856 | |

Range | 1.0300 | |

R_{a} Distribution (ZONE 3) | Statistic | Std. Error |

Mean | 0.692028 | 0.0414654 |

Median | 0.720154 | |

Variance | 0.034 | |

Std. Deviation | 0.1854391 | |

Minimum | 0.3681 | |

Maximum | 1.0318 | |

Range | 0.6637 |

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**MDPI and ACS Style**

Distefano, N.; Leonardi, S.
Evaluation of the Effectiveness of Traffic Calming Measures by SPEIR Methodology: Framework and Case Studies. *Sustainability* **2022**, *14*, 7325.
https://doi.org/10.3390/su14127325

**AMA Style**

Distefano N, Leonardi S.
Evaluation of the Effectiveness of Traffic Calming Measures by SPEIR Methodology: Framework and Case Studies. *Sustainability*. 2022; 14(12):7325.
https://doi.org/10.3390/su14127325

**Chicago/Turabian Style**

Distefano, Natalia, and Salvatore Leonardi.
2022. "Evaluation of the Effectiveness of Traffic Calming Measures by SPEIR Methodology: Framework and Case Studies" *Sustainability* 14, no. 12: 7325.
https://doi.org/10.3390/su14127325