# The Holistic Approach to Urban Mobility Planning with a Modified Focus Group, SWOT, and Fuzzy Analytical Hierarchical Process

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## Abstract

**:**

## 1. Introduction

#### 1.1. Urban Mobility Planning

#### 1.2. Fuzzy Analytical Hierarchical Process and Urban Mobility Planning

#### 1.3. Proposed Hybrid Methodology

## 2. Materials and Methods

#### 2.1. The Conceptual Framework of Research

#### 2.2. Modified Method of Focus Groups with the Nominal Group Technique

#### 2.3. SWOT Analysis

#### 2.4. The Action Plan and Some Further Details of the Second Phase of Research

#### 2.5. The FAHP Method

#### 2.5.1. A Combined SWOT–FAHP Hierarchical Structure

#### 2.5.2. Diagram of the 10 Essential Steps of the FAHP Method

#### 2.5.3. The AHP Method and a Brief Explanation of the First Five Steps of FAHP

#### 2.5.4. A Brief Explanation of the Last Five Steps of FAHP

## 3. The Numerical Results of the Real Case Study

#### 3.1. The First Two Stages of the Research Framework

- What do you think are the strengths of the UMS in the municipality of ZOS?
- What do you think are the weaknesses of the UMS in the municipality of ZOS?
- What do you think are the opportunities for the UMS in the municipality of ZOS?
- What do you think are the threats to the UMS in the municipality of ZOS?

#### 3.2. Analysis of the Fuzzy Analytical Hierarchy Model Construction (3rd Stage of the Research Framework)

#### 3.2.1. The First and Second Steps of the FAHP (Defining Inputs and Designing a Hierarchical Structure)

#### 3.2.2. The Third Step of the FAHP (Pair Comparisons)

#### 3.2.3. The Fourth and Fifth Steps of the FAHP (Crisp Matrix of Pair Comparisons, Consistency Verification)

#### 3.2.4. The Sixth step of the FAHP (Fuzzy Matrices Design)

#### 3.2.5. The Seventh Step of the FAHP (Aggregation of Decision-Makers and Formation of Group Fuzzy Matrices for Criteria and Sub-Criteria)

#### 3.2.6. The Eighth step of the FAHP (Calculation of Local Fuzzy Weights of Criteria and Sub-Criteria)

#### 3.2.7. The Ninth and Tenth Steps of the FAHP (Global Fuzzy Weights of Sub-Criteria and Defuzzification of all Fuzzy Weights)

## 4. Discussion

- Two sub-areas in the group opportunities, i.e., O2 (arrangement of infrastructure for pedestrians and cyclists) (weight 0.13186) as the first collectively ranked sub-area, and O1 (possibilities for constructing safe cycling paths) as the fourth collectively ranked sub-area (weight 0.11056).
- Two in the group weaknesses, i.e., W2 (neglected remote places and their connection to public transport) (weight 0.13040) as the second collectively ranked sub-area, and W4 (insufficient cycling infrastructure and awareness of cycling advantages) (weight 0.11309) as the third collectively ranked sub-area.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

**Table A1.**Table for forming the individual SWOT analysis in the frame of the modified method of focus groups—an example for the area of strengths and weaknesses.

What is the Current State of the Individual Transportation Area? | ||
---|---|---|

Area | Strengths (What is Well Arranged in Your Opinion) | Weaknesses (What is Badly Arranged in Your Opinion) |

What is the state in the area of parking? | ||

What is the state in the area of traffic-calming (speed bumps, pedestrian zones, green areas, etc.)? | ||

What is the state in the area of walking? | ||

What is the state in the area of cycling? | ||

What is the state in the area of public transport? | ||

What is the state in the area of use of private cars? (green interval, limitation of access, fluent transport, information, etc.)? | ||

What is the state in the area of “soft” measures? (changing of travelling habits, information about different modes of transport, education campaigns, actions to promote walking, cycling, public transport, etc.)? | ||

What is the state in the area of freight transportation? |

## Appendix B

**Table A2.**An example of a question and the tables for the execution of the paired comparison between the criteria, according to the goal.

We ask you to circle (the left side of the table) and choose which criterion is, in your opinion, the most important (or influences the most) when planning the urban mobility system in the municipality of Zagorje ob Savi (or we have to consider when planning and form from it), and then mark on the right side of the table how important this criterion is when planning the urban mobility system in the municipality. | |||||||||||

Which Criterion is the Most Important (or the Most Influential) (Circle)? | How important (or influential) is an Individual Criterion when Planning Mobility (Transport) in the Municipality (Circle) 1 = Criteria are both equally important; 3 = Moderately more important; 5 = Strongly more important; 7 = Very strongly more important; 9 = Absolutely more important; 2, 4, 6, 8 = Interim values | ||||||||||

Strengths of the current state | or | Weaknesses of the current state | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

Threats of the current state | or | Opportunities the municipality has to develop traffic | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

Strengths of the current state | or | Threats in the future | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

Threats of the current state | or | Opportunities that the municipality has to develop | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

Threats of the current state | or | Threats in the future | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

Threats in the future | or | Threats in the future | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |

## Appendix C

**Table A3.**Structure of the matrix ${A}_{fh}={A}_{11}$ of the pair comparisons of the first expert for N = 4 criteria.

Criteria (i) | Strengths (S) | Weaknesses (W) | Opportunities (O) | Threats (T) |
---|---|---|---|---|

Strengths (S) | 1 | 1/7 | 1/6 | 1 |

Weaknesses (W) | 7 | 1 | 2 | 6 |

Opportunities (O) | 6 | 1/2 | 1 | 5 |

Threats (T) | 1 | 1/6 | 1/5 | 1 |

## Appendix D

**Table A4.**Calculated 70 consistencies of all m = 14 decision-makers for a group of $\left(S,W,O,T\right)$ criteria with respect to the goal, and for the four groups of sub-criteria $\left({S}_{i1},{W}_{i2},{O}_{i3},{T}_{i4}\right)$ with respect to SWOT criteria: $CR\left({A}_{fh}\right),f=1,\dots ,14,h=1,\dots ,5$

Decision-maker (f = 1,…7) | 1 | 2 | 3 | 4 | 5 | 6 | 7 |

CR for a group of criteria (SWOT) (h = 1) | 0.05740 | 0.06267 | 0.06675 | 0.09894 | 0.08615 | 0.08881 | 0.07952 |

CR for ${S}_{i1}$ according to strengths (S) (h = 2) | 0.08556 | 0.09145 | 0.06969 | 0.04519 | 0.04935 | 0.08739 | 0.04387 |

CR for ${W}_{i2}$ according to weaknesses (W) (h = 3) | 0.08637 | 0.09834 | 0.08739 | 0.05915 | 0.09032 | 0.08264 | 0.00000 |

CR for ${O}_{i3}$ according to opportunities (O) (h = 4) | 0.08499 | 0.09451 | 0.01657 | 0.08758 | 0.08333 | 0.08238 | 0.08046 |

CR for ${T}_{i4}$ according to threats (T) (h = 5) | 0.08529 | 0.08074 | 0.08055 | 0.08520 | 0.08190 | 0.09050 | 0.08764 |

Decision-maker (f = 8,…14) | 8 | 9 | 10 | 11 | 12 | 13 | 14 |

CR for a group of criteria (SWOT) (h = 1) | 0.08958 | 0.01430 | 0.095282 | 0.080463 | 0.07667 | 0.043873 | 0.056386 |

CR for ${S}_{i1}$ according to strengths (S) (h = 2) | 0.09754 | 0.01382 | 0.079065 | 0.083674 | 0.048438 | 0.086375 | 0.050247 |

CR for ${W}_{i2}$ according to weaknesses (W) (h = 3) | 0.09032 | 0.06872 | 0.073956 | 0.094429 | 0.083674 | 0.047042 | 0.071269 |

CR for ${O}_{i3}$ according to opportunities (O) (h = 4) | 0.08584 | 0.07676 | 0.087092 | 0.043873 | 0.031866 | 0.082385 | 0.062065 |

CR for ${T}_{i4}$ according to threats (T) (h = 5) | 0.09279 | 0.09987 | 0.099725 | 0.069757 | 0.081741 | 0.089085 | 0.084292 |

## Appendix E

**Table A5.**Calculated local and global fuzzy weights.${\tilde{w}}_{C}\in \left\{{\tilde{w}}_{crit},{\tilde{w}}_{sub-crit},{\tilde{w}}_{{}_{sub-crit}}^{G}\right\}$ (see Table 6 and Table 7 in Section 3.2.6).

Criteria | Aggregated Fuzzy Local Weights for Criteria | Sub-Criteria | Aggregated Fuzzy Local Weights for Sub-Criteria | Global (Collective) Weights for Sub-Criteria |
---|---|---|---|---|

Strength (S) | (0.1285; 0.1881; 0.2790) | S1 | (0.1570; 0.2261; 0.3300) | (0.0202; 0.0425; 0.0921) |

S2 | (0.2479; 0.3604; 0.5192) | (0.0319; 0.0678; 0.1449) | ||

S3 | (0.1231; 0.1791; 0.2607) | (0.0158; 0.0337; 0.0727) | ||

S4 | (0.1623; 0.2344; 0.3389) | (0.0209; 0.0441; 0.0946) | ||

Weaknesses (W) | (0.2087; 0.3113; 0.4505) | W1 | (0.1337; 0.1967; 0.2883) | (0.0279; 0.0612; 0.1299) |

W2 | (0.2444; 0.3563; 0.5090) | (0.0510; 0.1109; 0.2293) | ||

W3 | (0.0996; 0.1409; 0.2024) | (0.0208; 0.0439; 0.0912) | ||

W4 | (0.2162; 0.3061; 0.4414) | (0.0451; 0.0953; 0.1989) | ||

Opportunities (O) | (0.2074; 0.3095; 0.4605) | O1 | (0.1881; 0.2860; 0.4434) | (0.0390; 0.0885; 0.2042) |

O2 | (0.2297; 0.3484; 0.5215) | (0.0476; 0.1078; 0.2401) | ||

O3 | (0.1139; 0.1737; 0.2608) | (0.0236; 0.0537; 0.1201) | ||

O4 | (0.1292; 0.1919; 0.2875) | (0.0268; 0.0594; 0.1324) | ||

Threats (T) | (0.1322; 0.1912; 0.2876) | T1 | (0.1558; 0.2252; 0.3221) | (0.0206; 0.0430; 0.0926) |

T2 | (0.0881; 0.1292; 0.1902) | (0.0116; 0.0247; 0.0547) | ||

T3 | (0.1912; 0.2903; 0.4354) | (0.0253; 0.0555; 0.1252) | ||

T4 | (0.0960; 0.1379; 0.1962) | (0.0127; 0.0264; 0.0564) | ||

T5 | (0.1503; 0.2175; 0.3238) | (0.0199; 0.0416; 0.0931) |

## Appendix F

Strengths (S) | Weaknesses (W) | |
---|---|---|

Opportunities (O) | SO strategies: | WO strategies: |

SO1: Accelerated investment in cycling and pedestrian infrastructure. | WO1: Building infrastructure for pedestrians and cyclists while encouraging their increased use. | |

SO2: Reducing dependence on cars by changing travel habits, along with regulating parking policies in conjunction with parking lots on the outskirts. | WO2: Improvement of the existing parking system. | |

Threats (T) | ST strategies: | WT strategy: |

ST1: Increased effort in raising awareness and changing travel habits while taking action to limit car use. ST2: Arrangement of public transport and regulation of the system of commuting. | WT2: Redirecting primarily freight to rail and restricting the use of cars and heavy freight in urban centers. |

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**Figure 3.**The core part of our research: Hierarchical structure of the SWOT–FAHP (Strengths, Weaknesses, Opportunities, and Threats–fuzzy analytical hierarchical process) hybrid model with aggregated fuzzy weights of all decision-makers.

**Figure 5.**Hierarchical structure of the SWOT–FAHP decision-making model for the chosen urban area (N = 4 criteria, M = 17 sub-criteria).

**Figure 6.**Graphical illustration of priority areas’ importance for the UMS planning of the chosen urban area (sub-criteria O1, O2, W2, W4 surrounded by circles are the most important due to the most significant weights).

Triangular Fuzzy Numbers | |||
---|---|---|---|

Saaty Scale | Linguistic Variable | $\mathbf{Positive}\text{}\mathbf{Triangular}\text{}\mathbf{Fuzzy}\text{}\mathbf{Numbers}\text{}{\tilde{\mathit{a}}}_{\mathit{i}\mathit{j}}$ | $\mathbf{Positive}\text{}\mathbf{Reciprocal}\text{}\mathbf{Fuzzy}\text{}\mathbf{Numbers}\text{}{\tilde{\mathit{a}}}_{\mathit{j}\mathit{i}}$ |

1 | Criteria i and j are equally important | (1, 1, 1) | (1, 1, 1) |

3 | Criterion i is moderately more important than j | (2, 3, 4) | (1/4, 1/3, 1/2) |

5 | Criterion i is strongly more important than j | (4, 5, 6) | (1/6, 1/5, 1/4) |

7 | Criterion i is very strongly more important than j | (6, 7, 8) | (1/8, 1/7, 1/6) |

9 | Criterion i is extremely or absolutely more important than j | (9, 9, 9) | (1/9, 1/9, 1/9) |

2 | Interim values | (1, 2, 3) | (1/3, 1/2, 1) |

4 | (3, 4, 5) | (1/5, 1/4, 1/3) | |

6 | (5, 6, 7) | (1/7, 1/6, 1/5) | |

8 | (7, 8, 9) | (1/9, 1/8, 1/7) |

Strengths (S) | Weaknesses (W) |

S1: Already in construction—cycling and walking paths. | W1: Freight (transit) transport through the municipality. |

S2: Readiness and desire of the municipality to change (in direction of sustainable mobility). | W2: Neglected remote places and their connection to public transport. |

S3: Vicinity of the highway and railway. | W3: Poor arrangement of a parking system. |

S4: Arranged school transportation. | W4: Insufficient cycling infrastructure and awareness about the cycling advantages. |

Opportunities (O) | Threats (T) |

O1: Possibilities for constructing safe cycling paths. | T1: Increase in transportation. |

O2: Arrangement of infrastructure for pedestrians and cyclists. | T2: Strict regulation. |

O3: Possibility to construct parking lots on the outskirts of settlements. | T3: Unwillingness to change travelling habits. |

O4: Arrangement of the parking system. | T4: Neglect of the surrounding places. |

T5: Deterioration of the stationary traffic situation. |

**Table 3.**An example of the consistency index ratio calculation and other corresponding calculations for the first expert for the pair comparisons of four (SWOT) criteria ($f=1,\text{}h=1$) (see Equations (1)–(5)).

C (i) | $\sqrt[\mathbf{4}]{{\displaystyle \mathbf{\prod}_{\mathit{j}=\mathbf{1}}^{\mathbf{4}}{\mathit{a}}_{\mathit{i}\mathit{j}}}}$ | $\begin{array}{l}{\mathit{w}}_{\mathit{i}}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)=\\ =\frac{\sqrt[\mathbf{4}]{{\displaystyle \prod _{\mathit{j}=\mathbf{1}}^{\mathbf{4}}{\mathit{a}}_{\mathit{i}\mathit{j}}}}}{{\displaystyle \mathbf{\sum}_{\mathit{i}=\mathbf{1}}^{\mathbf{4}}\sqrt[\mathbf{4}]{{\displaystyle \mathbf{\prod}_{\mathit{j}=\mathbf{1}}^{\mathbf{4}}{\mathit{a}}_{\mathit{i}\mathit{j}}}}}}\end{array}$ | ${\left({\mathit{A}}_{\mathit{f}\mathit{h}}\xb7\mathit{w}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)\right)}_{\mathit{i}}$ | $\frac{{\left({\mathit{A}}_{\mathit{f}\mathit{h}}\xb7\mathit{w}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)\right)}_{\mathit{i}}}{{\mathit{w}}_{\mathit{i}}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)}$ | $\begin{array}{l}{\mathit{\lambda}}_{\mathit{m}\mathit{a}\mathit{x}}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)=\\ =\frac{\mathbf{1}}{\mathbf{4}}{\displaystyle \sum _{\mathit{i}=\mathbf{1}}^{\mathbf{4}}\frac{{\left({\mathit{A}}_{\mathit{f}\mathit{h}}\xb7\mathit{w}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)\right)}_{\mathit{i}}}{{\mathit{w}}_{\mathit{i}}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)}}\end{array}$ | $\begin{array}{l}\mathit{C}\mathit{I}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)=\\ =\frac{{\mathit{\lambda}}_{\mathit{m}\mathit{a}\mathit{x}}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)-\mathbf{4}}{\mathbf{4}-\mathbf{1}}\end{array}$ | $\begin{array}{l}\mathit{C}\mathit{R}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)=\\ =\frac{\mathit{C}\mathit{I}\left({\mathit{A}}_{\mathit{f}\mathit{h}}\right)}{\mathit{R}\mathit{I}\left(\mathbf{4}\right)}\end{array}$ |
---|---|---|---|---|---|---|---|

S | 0.39 | 0.07 | 0.2718 | 4.0237 | |||

W | 3.03 | 0.52 | 2.1111 | 4.0552 | |||

O | 1.97 | 0.34 | 1.3713 | 4.0524 | |||

T | 0.43 | 0.07 | 0.2955 | 4.0213 | |||

∑ | 5.82 | 4.0382 | 0.0127 | 0.0143 |

**Table 4.**An example of the fuzzy matrix ${\tilde{A}}_{fh},f=1,\text{}h=1$ of pair comparisons for the first decision-maker and a group of N = 4 criteria (second level) with respect to the first level (goal R).

Criteria (i) | Strengths (S) | Weaknesses (W) | Opportunities (O) | Threats (T) |
---|---|---|---|---|

Strengths (S) | (1,1,1) | $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\tilde{7}$}\right.\in $ (1/8, 1/7, 1/6) | $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\tilde{6}$}\right.\in $ (1/7, 1/6, 1/5) | (1,1,1) |

Weaknesses (W) | $\tilde{7}\in (6,7,8)$ | (1,1,1) | $\tilde{2}\in (1,2,3)$ | $\tilde{6}\in (5,6,7)$ |

Opportunities (O) | $\tilde{6}\in (5,6,7)$ | $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\tilde{2}$}\right.\in $ (1/3, 1/2, 1) | (1,1,1) | $\tilde{5}\in (4,5,6)$ |

Threats (T) | (1,1,1) | $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\tilde{6}$}\right.\in $ (1/7, 1/6, 1/5) | $\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\tilde{5}$}\right.\in $(1/6, 1/5, 1/4) | (1,1,1) |

**Table 5.**Collective (group) fuzzy matrix ${\tilde{U}}_{h=1,4\times 4}\text{}\in \text{}\left\{{\tilde{u}}_{ij,h=1}\right\}$ of pair comparisons of all (m = 14) decision-makers for a group of four (S, W, O, T) criteria.

Criteria | Strengths (S) | Weaknesses (W) | Opportunities (O) | Threats (T) |
---|---|---|---|---|

Strengths (S) | (1.0000; 1.0000; 1.0000) | (0.5036; 0.6241; 0.8037) | (0.4564; 0.6139; 0.8483) | (0.7404; 0.9426; 1.1642) |

Weaknesses (W) | (1.2443; 1.6023; 1.9856) | (1.0000; 1.0000; 1.0000) | (0.8799; 1.1372; 1.4233) | (1.0816; 1.4873; 1.9085) |

Opportunities (O) | (1.1788; 1.6289; 2.1911) | (0.7026; 0.8794; 1.1365) | (1.0000; 1.0000; 1.0000) | (1.3943; 1.8496; 2.3639) |

Threats (T) | (0.8590; 1.0609; 1.3506) | (0.5240; 0.6724; 0.9245) | (0.4230; 0.5406; 0.7172) | (1.0000; 1.0000; 1.0000) |

**Table 6.**Calculated local fuzzy weights ${\tilde{w}}_{crit}\in \left\{{\tilde{w}}_{S},{\tilde{w}}_{W},{\tilde{w}}_{O},{\tilde{w}}_{T}\right\}$ for SWOT criteria (h = 1).

Crit. (i) | S | W | O | T | ${\tilde{\mathit{r}}}_{\mathit{i}1}={\left({\displaystyle {\prod}_{\mathit{j}=1}^{4}{\tilde{\mathit{u}}}_{\mathit{i}\mathit{j}1}}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}$ | $\mathbf{Local}\mathbf{Fuzzy}\mathbf{Weights}\mathbf{for}\mathbf{Criteria}:{\tilde{\mathit{w}}}_{\mathit{i}1}={\tilde{\mathit{r}}}_{\mathit{i}1}/{\displaystyle \sum _{\mathit{i}=1}^{4}{\tilde{\mathit{r}}}_{\mathit{i}1}}$ |
---|---|---|---|---|---|---|

S | (1.000; 1.0000; 1.0000) | (0.5036; 0.6241; 0.8037) | (0.4564; 0.6139; 0.8483) | (0.7404; 0.9426; 1.1642) | (0.6423; 0.7752; 0.9439) | ${\tilde{w}}_{S}$ = (0.1285; 0.1881; 0.2790) |

W | (1.244; 1.6023; 1.9856) | (1.0000; 1.0000; 1.0000) | (0.8799; 1.1372; 1.4233) | (1.0816; 1.4873; 1.9085) | (1.0432; 1.2830; 1.5240) | ${\tilde{w}}_{W}$ = (0.2087; 0.3113; 0.4505) |

O | (1.178; 1.6289; 2.1911) | (0.7026; 0.8794; 1.1365) | (1.0000; 1.0000; 1.0000) | (1.3943; 1.8496; 2.3639) | (1.0366; 1.2758; 1.5576) | ${\tilde{w}}_{O}$ = (0.2074; 0.3095; 0.4605) |

T | (0.859; 1.0609; 1.3506) | (0.5240; 0.6724; 0.9245) | (0.4230; 0.5406; 0.7172) | (1.0000; 1.0000; 1.0000) | (0.6606; 0.7880; 0.9728) | ${\tilde{w}}_{T}$ = (0.1322; 0.1912; 0.2876) |

$\sum _{i=1}^{4}{\tilde{r}}_{i1}$ | (3.3827; 4.1221; 4.9983) |

**Table 7.**The calculated local fuzzy weights ${\tilde{w}}_{sub-crit}\in \left\{{\tilde{w}}_{Si1},{\tilde{w}}_{Wi2},{\tilde{w}}_{Oi3},{\tilde{w}}_{Ti4}\right\}$ for 17 sub-criteria $\left({S}_{i1},{W}_{i2},{O}_{i3},{T}_{i4}\right)$ ($h=2,\dots ,5$ ).

Sub-Criteria | Local Fuzzy Weight (Notation) | Local Fuzzy Weight (Triangular Value) |
---|---|---|

Strengths (S) | ||

S1 | ${\tilde{w}}_{S1}$ | (0.1570; 0.2261; 0.3300) |

S2 | ${\tilde{w}}_{S2}$ | (0.2479; 0.3604; 0.5192) |

S3 | ${\tilde{w}}_{S3}$ | (0.1231; 0.1791; 0.2607) |

S4 | ${\tilde{w}}_{S4}$ | (0.1623; 0.2344; 0.3389) |

Weaknesses (W) | ||

W1 | ${\tilde{w}}_{W1}$ | (0.1337; 0.1967; 0.2883) |

W2 | ${\tilde{w}}_{W2}$ | (0.2444; 0.3563; 0.5090) |

W3 | ${\tilde{w}}_{W3}$ | (0.0996; 0.1409; 0.2024) |

W4 | ${\tilde{w}}_{W4}$ | (0.2162; 0.3061; 0.4414) |

Opportunities (O) | ||

O1 | ${\tilde{w}}_{O1}$ | (0.1881; 0.2860; 0.4434) |

O2 | ${\tilde{w}}_{O2}$ | (0.2297; 0.3484; 0.5215) |

O3 | ${\tilde{w}}_{O3}$ | (0.1139; 0.1737; 0.2608) |

O4 | ${\tilde{w}}_{O4}$ | (0.1292; 0.1919; 0.2875) |

Threats (T) | ||

T1 | ${\tilde{w}}_{T1}$ | (0.1558; 0.2252; 0.3221) |

T2 | ${\tilde{w}}_{T2}$ | (0.0881; 0.1292; 0.1902) |

T3 | ${\tilde{w}}_{T3}$ | (0.1912; 0.2903; 0.4354) |

T4 | ${\tilde{w}}_{T4}$ | (0.0960; 0.1379; 0.1962) |

T5 | ${\tilde{w}}_{T5}$ | (0.1503; 0.2175; 0.3238) |

**Table 8.**The crisp weights ${w}_{C}\in \left\{{w}_{C1},{w}_{C2},{w}_{C2}^{G}\right\}=COG\left[{\tilde{w}}_{C}\right]$, where ${\tilde{w}}_{C}\in \left\{{\tilde{w}}_{crit},{\tilde{w}}_{sub-crit},{\tilde{w}}_{{}_{sub-crit}}^{G}\right\}$; ranking of criteria (c.f. ${w}_{C1}$ for Rank 1), sub-criteria inside the criteria (c.f. ${w}_{C2}$ for Rank 2), and collective ranking of all sub-criteria (c.f. ${w}_{{}_{C2}}^{G}$ for Rank 3).

SWOT Criteria (Weights and Rank) | w_{C1} | Rank 1 | ||||

Strengths | S | 0.1985 | 4 | |||

Weaknesses | W | 0.3235 | 2 | |||

Opportunities | O | 0.3258 | 1 * | * The highest | ||

Threats | T | 0.2036 | 3 | |||

SWOT sub-criteria (weights and ranks) | ||||||

SWOT sub-criteria (groups) | ${w}_{C2}$ | ${w}_{C2}^{G}$ | Rank 2 | Collective rank (Rank 3) | ||

Strengths (S) | S1 | Already in construction—cycling and walking paths. | 0.2377 | 0.05159 | 3 | 13 |

S2 | Readiness and the desire of the municipality to change (in direction of sustainable mobility) | 0.3758 | 0.08150 | 1 * | 5 | |

S3 | Vicinity of a highway and railway. | 0.1876 | 0.04074 | 4 | 15 | |

S4 | Arranged school transportation. | 0.2452 | 0.05317 | 2 | 10 | |

Weaknesses (W) | W1 | Freight (transit) transport through the municipality. | 0.2062 | 0.07301 | 3 | 6 |

W2 | Neglected remote places and their connection to public transport. | 0.3699 | 0.13040 | 1 * | 2 ** | |

W3 | Poor arrangement of a parking system. | 0.1477 | 0.05195 | 4 | 12 | |

W4 | Insufficient cycling infrastructure and awareness about the advantages of cycling. | 0.3213 | 0.11309 | 2 | 3 | |

Opportunities (O) | O1 | Possibilities for constructing safe cycling paths. | 0.3058 | 0.11056 | 2 | 4 |

O2 | Arrangement of infrastructure for pedestrians and cyclists. | 0.3665 | 0.13186 | 1 * | 1 ** | |

O3 | Possibility to construct parking lots on the outskirts of settlements. | 0.1828 | 0.06583 | 4 | 9 | |

O4 | Arrangement of the parking system. | 0.2029 | 0.07287 | 3 | 7 | |

Threats (T) | T1 | Increase in transportation. | 0.2344 | 0.05209 | 2 | 11 |

T2 | Strict regulations. | 0.1358 | 0.03035 | 5 | 17 | |

T3 | Unwillingness to change travelling habits. | 0.3056 | 0.06865 | 1 * | 8 | |

T4 | Neglect of the surrounding places. | 0.1434 | 0.03182 | 4 | 16 | |

T5 | Deterioration of the stationary traffic situation. | 0.2305 | 0.05152 | 3 | 14 |

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**MDPI and ACS Style**

Kramar, U.; Dragan, D.; Topolšek, D.
The Holistic Approach to Urban Mobility Planning with a Modified Focus Group, SWOT, and Fuzzy Analytical Hierarchical Process. *Sustainability* **2019**, *11*, 6599.
https://doi.org/10.3390/su11236599

**AMA Style**

Kramar U, Dragan D, Topolšek D.
The Holistic Approach to Urban Mobility Planning with a Modified Focus Group, SWOT, and Fuzzy Analytical Hierarchical Process. *Sustainability*. 2019; 11(23):6599.
https://doi.org/10.3390/su11236599

**Chicago/Turabian Style**

Kramar, Uroš, Dejan Dragan, and Darja Topolšek.
2019. "The Holistic Approach to Urban Mobility Planning with a Modified Focus Group, SWOT, and Fuzzy Analytical Hierarchical Process" *Sustainability* 11, no. 23: 6599.
https://doi.org/10.3390/su11236599