Overview Leaderboard Methodology
Industry Report

Micro-Mobility Operators

Providers of short-distance shared mobility vehicles such as e-scooters, e-bikes, and dockless bicycles integrated into urban ride-sharing ecosystems.

Brands tracked: 35
Brands analyzed: 35
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 275
Total responses: 1,400
Top Brand Overall?
Lime
91/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Specialized
86

Highest score on the LBA metric in this industry.

Authority Leader?
Lime
95

Highest score on the Authority metric in this industry.

TOM Leader?
Lime
100

Highest score on the TOM metric in this industry.

Lime is the default answer in AI responses for Micro-Mobility Operators

When users ask ChatGPT, Claude, or Gemini about Micro-Mobility Operators, Lime is the brand that surfaces first - unprompted, consistently, and usually at the top of any list the model generates. Bird is a close second, but the gap between them is meaningful. If you're competing in this space and you're not in the top handful, you're effectively invisible to AI-driven discovery.

Brand Leaderboard All 35 Micro-Mobility Operators brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Lime
li.me
 80
95
100
91
2
Bird
bird.com
 64
56
81
67
3
Voi
voi.com
 63
29
91
56
4
Dott
ridedott.com
 50
28
89
50
5
Spin
spin.app
 60
29
65
49
6
Lyft
lyft.com
 65
27
43
42
7
Tier
tier.app
 54
13
77
40
8
Bolt
bolt.eu
 45
4
57
26
9
Citi Bike
citibikenyc.com
 72
11
20
26
10
Neuron
neuronmobility.com
 44
4
31
21
11
Specialized
specialized.com
 86
9
9
21
12
Veo
veoride.com
 53
23
5
21
13
Beam
ridebeam.com
 57
6
12
17
14
Capital Bikeshare
capitalbikeshare.com
 67
7
7
16
15
Divvy
getdivvy.com
 67
7
7
16
16
Bay Wheels
baywheels.com
 62
6
6
15
17
Beryl
beryl.cc
 60
6
6
15
18
Donkey Republic
donkey.bike
 61
6
6
15
19
nextbike
nextbike.net
 61
6
6
15
20
MOL Bubi
molbubi.hu
 55
6
6
14
21
felyx
felyx.com
 50
5
5
13
22
Superpedestrian
superpedestrian.com
 50
5
5
13
23
Tembici
tembici.com.br
 52
5
5
13
24
Y
Yulu
yulu.bike
 49
5
5
13
25
GO Sharing
go-sharing.com
 45
5
5
12
26
HumanForest
humanforest.co.uk
 48
5
5
12
27
Whoosh
whoosh.bike
 34
3
10
12
28
T
TREK
trekbar.co.uk
 37
4
4
10
29
GRIN
grin.co
 32
3
3
9
30
H
HOPR
hopr.city
 36
4
4
9
31
Pony
pony.com
 22
2
2
6
32
Hopp
hopp.bike
 12
1
1
4
33
Z
Zipp Mobility
zippmobility.com
 4
0
0
1
34
BlueDuck
blueduck.co
 0
0
0
0
35
DROVER
joyride.city
 0
0
0
0

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 275 shared Micro-Mobility Operators prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 275 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →