1 Introduction
2 Designing future people-centred mobility solutions
2.1 Future scenarios
2.2 Personas
2.3 Synthetic populations
2.4 Existing works
3 Persona clustering and reweighting method
3.1 Case study of Paris-Saclay
3.2 Description of 10-step methodology
3.2.1 Step 1: Choosing cluster attributes
3.2.2 Step 2: Data preparation
3.2.3 Step 3: Proto-persona (PP) clustering
PP | Rooms | Child < 5 | Houshold | Car | Couple | Degree | Student | Parking | Activity locaction | Birthplace | |
---|---|---|---|---|---|---|---|---|---|---|---|
Socio-prof | |||||||||||
1 | 4 | 1 | 4 | 1 | Yes | Without job | Yes | Yes | In municip. | In dep. | |
2 | 4 | 1 | 4 | 1 | Higher ed. | Master | Outs. dep. | Abroad | |||
3 | 4 | 2 | 1 | Retired | Vocational | Yes | Outs. region | ||||
4 | 2 | 1 | 1 | Yes | Higher ed. | Master | Outs. dep. | Outs. region | |||
5 | 4 | 1 | 4 | 1 | Employee | Vocational | Yes | Outs. municip. | Abroad | ||
6 | 3 | 3 | 1 | Yes | Without job | Yes | In municip. | In dep. | |||
7 | 5 | 4 | Higher ed. | Master | Yes | Outs. dep. | Outs. dep. | ||||
8 | 4 | 1 | 4 | 2 | Yes | Without job | In dep. | ||||
9 | 3 | 3 | 1 | Yes | Employee | Vocational | Outs. dep. | Outs. dep. | |||
10 | 2 | 1 | Yes | Higher ed. | Master | In municip. | Abroad | ||||
11 | 4 | 1 | 4 | 1 | Without job | Vocational | Yes | Abroad | |||
12 | 4 | 2 | 1 | Higher ed. | Master | Yes | Outs. dep | Outs. region | |||
13 | 5 | 3 | 2 | Yes | Intermediary | Technician | Yes | Outs. municip. | In dep. | ||
14 | 5 | 4 | 2 | Yes | Without job | College | Yes | Yes | Outs. dep. | ||
15 | 2 | 2 | Yes | Without job | Highschool | Abroad | |||||
16 | 3 | 1 | 1 | Yes | Retired | Vocational | Outs. region |
PP | Way of life | Gender | Status | Housing | Size sqm | Activity | Empl. time | Transport | Type | |
---|---|---|---|---|---|---|---|---|---|---|
1 | Child of couple | F | Single | Owner | 60–80 | < 14 years | Apartment | |||
2 | Couple with children | M | Married | Owner | 80–100 | Employed | Full time | Public transport | Apartment | |
3 | Adults without children | M | Married | Owner | 80–100 | Retired | House | |||
4 | Adults | M | Single | Tenant | 40–60 | Employed | Full time | Public transport | Apartment | |
5 | Couple with children | F | Married | Owner | 60–80 | Employed | Full time | Car | Apartment | |
6 | Child of single parent | M | Single | Tenant social housing | 60–80 | < 14 years | Apartment | |||
7 | Couple with children | F | Married | Owner | 120 + | Employed | Full time | Car | House | |
8 | Child of a couple | M | Single | Tenant social housing | 80–100 | < 14 years | Apartment | |||
9 | Single parent | F | Single | Tenant social housing | 60–80 | Employed | Full time | Public transport | Apartment | |
10 | Single under 40 | F | Single | Tenant | < 30 | Employed | Full time | Public transport | Apartment | |
11 | Couple with children | F | Married | Tenant social housing | 60–80 | Unemployed | Apartment | |||
12 | Adult without children | M | Married | Owner | 60–80 | Employed | Full time | Car | Apartment | |
13 | Child of a couple | M | Single | Owner | 80–100 | Employed | Full time | Car | House | |
14 | Child of a couple | M | Single | Owner | 120 + | Students | House | |||
15 | Single under 40 | M | Single | Tenant | < 30 | Unemployed | Apartment | |||
16 | Single over 40 | F | Widow | Owner | 40–60 | Retired | Apartment |
3.2.4 Step 4: Proto-personas per age group and area
3.2.5 Step 5: PP validity test for mobility
Variable | Description | Analysis |
---|---|---|
nb_dep | Number of trips per day | Sum |
DUREE | Duration of the trip | Mean, max, sum |
MTEMPSMAP | Walking time during the trip | Mean, sum |
3.2.6 Step 6: Uncertainty/trend compilation
Trends | Description | Values | |
---|---|---|---|
Area | Growth rate (%) | ||
Annual population growth | Paris | 0.4 | |
MGP | 0.6 | ||
IDF/CPS | 0.13 |
Age group | Female | Male | ||
---|---|---|---|---|
Ageing | The demographic ageing process leads to an increase in the age group of 60+. Different values are calculated based on statistical assumptions from INSEE [53] | 0–14 | 0.00 | 0.02 |
15–29 | −0.07 | −0.08 | ||
30–44 | 0.00 | −0.14 | ||
45–59 | −0.03 | −0.02 | ||
60+ | 1.65 | 2.50 |
Uncertainties | Description | Range | Sources |
---|---|---|---|
U1: Car ownership | Car ownership, in particular in Paris, is assumed to decrease, while in more remote places an increase is possible. Changing technology, prices, and policymaking allows various assumptions | Stable to less inside Paris/MGP, less to more outside MGP | [57] |
U2: Work/study location | With increasing transportation speed and trends like working-from-home, more people might work further away. Congestion or concepts like the 15-min city or urban villages could counteract | Across all regions significant possible changes | [58] |
U3: Household size | Household sizes are assumed to decrease. However, its level is unknown | Decreasing, strongest inside Paris | [59] |
3.2.7 Step 7: Future trend integration
Age groups | 0–14 y | 15–29 y | 30–44 y | 45–59 y | 60 + y | |||||
---|---|---|---|---|---|---|---|---|---|---|
Gender | M | F | M | F | M | F | M | F | M | F |
Paris | + 0.02 | ± 0 | −0.08 | −0.07 | −0.14 | 0 | −0.02 | −0.03 | + 2.5 | + 1.7 |
MGP | + 0.03 | + 0.01 | −0.07 | −0.06 | −0.13 | + 0.01 | −0.01 | −0.02 | + 2.5 | + 1.7 |
CPS | + 0.02 | ± 0 | −0.08 | −0.07 | −0.14 | + 0.01 | −0.02 | −0.03 | + 2.5 | + 1.7 |
IDF | + 0.02 | ± 0 | −0.08 | −0.07 | −0.14 | ± 0 | −0.02 | −0.03 | + 2.5 | + 1.7 |
3.2.8 Step 8: Uncertainties’ values per scenario
Scenario | Description |
---|---|
S1: Grumpy old transport | No significant changes to the current status |
S2: At an easy pace | Slow, consistent change across fields |
S3: Mine is yours | Strong shift towards a shared economy and mobility system (incl. MaaS) |
S4: Tech eager | Technology as driving force for majority of future mobility |
Scenarios | U1: Car ownership | U2: Proximity of work/study locations | U3: Household sizes |
---|---|---|---|
S1 | Increase (+ 5%) | Stable (± 0%) | Stable (± 0%) |
S2 | Decrease (−5%) | More local (± 1.5%/3%) | Decrease (−5%) |
S3 | Decrease (−5%) | More local (± 2%/4%) | Increase (+ 10%) |
S4 | Stable (± 0%) | Less local (−/ + 1.5%/3%) | Strong decrease (-10%) |
3.2.9 Step 9: Redistribute PP by scenario
-
\(K\in {\mathbb{N}}:\mathrm{Number}\,\mathrm{of}\,\mathrm{personas}\)
-
\({{\omega }{\prime}}_{k}\in \left({0,1}\right):\, \mathrm{Initial}\,\mathrm{population}\,\mathrm{share}\,\mathrm{per}\,\mathrm{persona }k \left(\mathrm{baseline}\right)\)
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\({\omega }_{k}\in \left({0,1}\right):\, \mathrm{Population}\,\mathrm{share}\,\mathrm{per}\,\mathrm{persona}\,\mathrm{k } (\mathrm{to}\,\mathrm{be}\,\mathrm{chosen})\)
-
\(\mathcal{A}:\, \mathrm{Set}\,\mathrm{ of}\,\mathrm{attributes}\,\mathrm{with}\,\mathrm{ a}\,\mathrm{mean}\,\mathrm{target}\,\mathrm{ value}\)
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\({a}_{k}\in {\mathbb{R}}:\mathrm{Value}\,\mathrm{of}\,\mathrm{attribute}\,\mathrm{A}\,\mathrm{in}\,\mathrm{persona}\,\mathrm{k}\)
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\(\widehat{A}\in {\mathbb{R}}: \mathrm{Target}\,\mathrm{mean}\,\mathrm{value}\,\mathrm{for}\,\mathrm{attribute}\,\mathrm{A}\)
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\(\mathcal{B}:\, \mathrm{Set}\,\mathrm{of}\,\mathrm{attributes}\,\mathrm{with}\,\mathrm{a}\,\mathrm{target}\,\mathrm{share}\,\mathrm{per}\,\mathrm{value}\)
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\({\mathcal{V}}_{B}: \mathrm{Set}\,\mathrm{ of}\,\mathrm{values}\,\mathrm{that}\,\mathrm{are}\,\mathrm{permissible}\,\mathrm{ for}\, \mathrm{attribute}\,\mathrm{ B}\)
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\({b}_{k,v}\in \left\{{0,1}\right\}:\,\mathrm{Defines}\,\mathrm{whether}\,\mathrm{attribute }\,\mathrm{B} \,\mathrm{in}\, \mathrm{persona} \,\mathrm{k}\, \mathrm{has}\,\mathrm{ value}\,\mathrm{ v}\)
-
\({\widehat{B}}_{v}\in \left\{{0,1}\right\}:\, \mathrm{ Target}\, \mathrm{share}\,\mathrm{for}\,\mathrm{value}\,\mathrm{v}\,\mathrm{of}\,\mathrm{attribute}\,\mathrm{B}\)
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\({\xi} \mathrm{AB}:\, \mathrm{Objective}\,\mathrm{ weight}\,\mathrm{for}\,\mathrm{attribute }\, AB\)
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\(\alpha \boldsymbol{ }:\mathrm{Regularisation}\,\mathrm{weight}\)