🔬 User Research Report — March 2026

Traffic Jams:
A Human Experience

A mixed-methods study of urban traffic congestion — from emotional responses to behavioural patterns — conducted with 13 participants across 24 in-depth interview questions.

Natallia · Tanya · Dmitry
Traffic Jam Experience
13 people
In-depth Interview
24 per person
11–14 March 2026
Quantitative Overview

Key Metrics

A statistical snapshot of 13 interviews — demographics, transport choices, emotions, and core pain points.

13
Participants
4 age groups
85%
Use a car
11 of 13 — partly or fully
85%
Feel anger / stress
Primary emotion in traffic
92%
Prefer predictability
Longer but certain over faster but unknown

Age distribution

N = 13 interviews, 11–14 March 2026
35–44 (5) 18–24 (3) 25–34 (3) 45–54 (2)

Emotional reaction to traffic jams

Dominant feeling reported (Q10 — thematic coding)
Thematic Analysis

Key Insights

Six cross-cutting themes identified through analysis of 312 responses (13 × 24 questions).

Biggest pain point (Q21)

Open-answer coding into dominant category

Coping strategies (Q16)

What people already do to handle traffic

Predictability vs. speed — which matters more? (Q22)

"Would you prefer a longer trip with a guaranteed arrival time?" — 11/13 chose predictability
01
Control over time = need #1
The core request is not "remove traffic" but "give me predictability." 11 of 13 explicitly valued arrival time accuracy over raw speed.
02
Anger depends on stakes, not duration
The same 30-minute delay is neutral when "no one is waiting" and unbearable when "someone is counting on me." Emotional intensity is driven by context, not minutes.
03
The car is a compromise, not a choice
Most people would rather not drive — but are forced to: children, heavy loads, suburban distance, inconvenient transfers. "I'd love to, but…"
04
Navigation apps are hygiene, not innovation
100% of drivers use Yandex Maps or Google Maps. It's a baseline expectation. The real pain: when there are no alternatives, and the app doesn't say so.
05
Traffic breaks chains, not just trips
One delay cascades: child late to school → mother late to work → doctor appointment lost. The systemic effect is consistently underestimated.
06
The sacrifice is always sleep and family
When there's no other option, people wake up earlier. The price: less sleep, less time with loved ones. "I sacrifice time — and that is my most important resource." (Galina)

Participants' voices

Direct quotes illustrating key themes

"For me, a guaranteed travel time would be the ideal solution. I'm willing to leave earlier — I just need to know exactly when I'll arrive."
— Anna, 35–44, suburban commuter
"I get very angry in traffic jams — the intensity depends on whether I'm in a hurry or not."
— Anatoly, 35–44, car-only commuter
"The worst outcome is that I won't get to where I need to be, or I'll be very, very late."
— Alena, 35–44, multimodal user
"I sacrifice time — and that is my most important resource. Buying a car meant I could at least be stuck in traffic comfortably."
— Galina, 25–34, mother + worker
"Uncertainty and disruption of plans — mine and other people's."
— Anna, 35–44, on her biggest fear
"I left at 10 pm and arrived home at 5 am. We were driving for seven hours."
— Galina, 25–34, extreme case
"Even if the trip is a bit longer, it's easier when I know exactly how long it will take. Then I can plan."
— Yury, 18–24, student / worker
"I've already done what I could — I switched to the metro. I don't know what else can be done."
— Natallia, 45–54, adapted user
User Personas

2 User Personas

Synthesised from 13 interviews into two archetypes — based on behavioural patterns, motivations, and life context.

AM
Anna the Multitasker
"I can't afford to be late — someone is always waiting."
👩‍👧 Parent 💼 In-person work 35–44 🚗 Car + metro
Context

Lives in the suburbs. Morning is a chain: home → school → office. Leaves at 7:20 am. A 10-minute delay doubles the entire journey. Responsible for children and employer simultaneously.

Core needs
Pain points
One delay collapses the whole day
Children late → school unhappy
Stress transfers to children in the car
Feeling of helplessness and anger
Current workarounds

Leaves with a 15-min buffer. Delegates children to her husband when a critical meeting is scheduled. Sometimes parks the car and takes the metro. "We've already optimised everything we can control."

Key quote

"For me, it's all about predictability."

SS
Solo Steve
"I drive alone with music on — but the time is still wasted."
👨 Solo driver 🚗 Car only 35–54 📍 Fixed routes
Context

Commutes alone on a stable route. Knows all the "best" roads; often refuses to change behaviour even when aware of congestion. Traffic is part of life — adapted to it, but still gets angry. Values the comfort and privacy of the car.

Core needs
Pain points
Lost time — the primary pain
Anger when travel time doubles
Late to meetings and appointments
Less sleep from leaving earlier
Current workarounds

Listens to podcasts / YouTube. Leaves a bit later in the evening. "Not willing to wake up earlier." Stays in the car even when alternatives exist — the car is personal space.

Key quote

"The best option is longer but with an accurate time — because I manage my own schedule."

Behavioural comparison matrix

Dimension Anna the Multitasker Solo Steve
Primary goal Predictability + don't let others down Save time + stay comfortable
Reaction to jam Immediate decision-making mode Angry, but passively waits
Readiness to change High — already switches to metro Low — "not willing to wake up earlier"
Type of stress Systemic (dependency chain) Personal (lost time and control)
Key solution Accurate forecast + multimodal options Comfort during jam + smart departure time
Customer Journey Map

User Journey Map

6 stages of a morning commute — actions, thoughts, emotions, and pain points at each step.

🌅
Preparation
Checking maps, planning early departure
🚗
Departure
In the car, setting route, reading traffic
🟡
Slowdown
Flow decelerating, first signs of congestion
🔴
Standstill
Full stop, unknown duration, helplessness
🔄
Decision
Alt route, metro, or wait it out
🏁
Arrival
On time or late, notifying those waiting
😐 Neutral
🙂 Focused
😟 Anxious
😡 Angry
🤔 Calculating
😮‍💨 Relief
🌍 Planet Persona Overlay — CO₂ footprint by journey stage

Critical moments in the journey

The realisation moment

When a person understands the jam is unavoidable. A critical window for deciding — jump to the metro or stay. Requires immediate, accurate information.

📱

The navigator fails

"No alternatives available" — the most painful moment. When every route on the map is red, people feel completely powerless.

📞

Notifying others

Calling school, work, partner. Social stress layers on top of physical frustration. "What's worst is when people are waiting for me."

Planet-Centered Design

Planet Persona: Earth in the Traffic Jam

Applying Planet-Centered Design — adding the "planet persona" to our CJM. What does Earth lose while we sit in traffic?

🌍

Persona: Planet Earth (urban ecosystem)

Age: 4.5 billion years · Location: global · Status: under pressure

"Every minute idling in a traffic jam is an engine running without moving — exhaust with no progress, fuel burned for nothing. Urban traffic at global scale generates hundreds of millions of tonnes of CO₂ annually from idle alone."

Planet's needs
  • Reduction of CO₂ emissions
  • Less engine idling time
  • Optimised flows without more cars
  • Shift toward public transport
Planet's pain points
  • ~3–5 L/h fuel burned while idling
  • PM2.5 and NOx in stopped traffic
  • Urban heat island effect
  • Road surface degradation from overload
Planet's goals
  • ↓ Transport CO₂ by 45% by 2030
  • Growth of electric vehicles
  • Smart traffic — fewer full stops
  • Multimodality as the default

Intersection insight: Our participants want predictability — the planet wants less idle traffic. Accurate travel time forecasting → fewer "precautionary" car trips → fewer cars on the road → less congestion → less CO₂. This is a true win-win: the human-centered and planet-centered solutions align.

CO₂ impact by transport scenario

Relative environmental footprint of different commuting strategies (normalised: normal car trip = 100)
How Might We

"How Might We…"

Design questions formulated from the research — entry points for ideation and solution generation.

How might we give people an accurate arrival time — even in bad conditions — so they can make informed decisions before they even leave home?
How might we reduce stress for chained journeys (child → school → office) where one delay breaks the entire day?
How might we make switching to public transport just as frictionless and comfortable as staying in the car?
How might we turn time spent in traffic from lost time into productive time — for people who cannot leave their vehicle?
How might we help the city notify drivers about alternatives before they have even set off?
How might we reduce CO₂ from idle traffic while simultaneously solving users' top request — predictability?

Open-source research context

Supporting statistics and trends on urban congestion from secondary sources

INRIX 2024
89 hrs/yr

Average time lost in traffic per driver in major cities annually. Moscow, London, Mexico City rank in the global top 3.

McKinsey 2023
$87B

Annual economic losses from congestion in European cities — productivity, fuel, and public health costs.

EEA 2023
27%

Transport's share of total EU CO₂ emissions. Idle traffic in jams accounts for up to 30% of that figure.

APA / BPS Research
34%

Of drivers report a significant increase in anxiety and aggression when daily delays exceed 20 minutes.

Google / Waze Research
15 min

Average buffer people build into their schedule to absorb unpredictability — the so-called "anxiety buffer."

ITF / UITP 2024
2.4×

Increase in satisfaction when switching from car to predictable public transport — even when the journey is longer.

Research Methodology

Methodology

Mixed method: qualitative interviews + thematic coding + open-source secondary data.

13
In-depth
user interviews
24
Questions
per participant
6
Thematic clusters
after coding
4
Days of data collection
(11–14 Mar 2026)

Research limitations

Sample size

N=13 is insufficient for statistical significance. Patterns are compelling for qualitative analysis but require quantitative validation at scale.

Sampling bias

35–44 overrepresented (38%). No respondents aged 55+. All from the CIS / Europe region. No perspective from Global South megacities.

Self-report

Data is based on recall, not observation. Idealisation of behaviour and recall bias for extreme events are possible.