Manchester Travel Patterns: Visualizing Post-Pandemic Mobility Changes

Transport behavior in Greater Manchester underwent seismic shifts between 2019 and 2023. The Travel Diary Survey (TRADS) captures these changes in granular detail, but the raw data tells only part of the story. That's why I built the Manchester Travel Patterns Explorer – transforming complex mobility data into interactive visualizations that reveal how a major UK city adapted its travel behavior in the post-pandemic era.

The Dataset: TRADS Reveals All

The Greater Manchester Travel Diary Survey (TRADS) is a comprehensive mobility dataset that captures:

• 6,506 trip samples from 2019 (pre-pandemic baseline)
• 1,877 trip samples from 2023 (post-pandemic reality)
• 2,686 people surveyed in 2019 vs 895 in 2023
• Hourly travel patterns throughout the day
• Mode share data across 10 transport categories
• Trip distances, frequencies, and destination flows

The challenge? Making this complex dataset accessible to transport planners, policymakers, and curious residents who want to understand how their city moves.

Project Vision: From Data to Insights

I envisioned an interactive platform that could answer questions like:

• "How did public transport usage change after COVID?"
• "When are Manchester's peak travel times?"
• "Which transport modes gained or lost popularity?"
• "How do travel patterns vary by age group?"

The solution needed to be:

• Visually compelling: Clear charts that tell the story at a glance
• Interactive: Tabbed interface for different aspects of travel behavior
• Insightful: Key takeaways highlighted for busy professionals
• Responsive: Accessible on mobile for on-the-go reference

Technical Architecture

Modern React with Recharts

Built on Next.js 15 with TypeScript, the application uses Recharts for powerful data visualization:

import { BarChart, Bar, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, LineChart, Line } from 'recharts';

function ModeSection({ data }: { data: Data }){
  const chartData = data.modes.map(m => ({
    mode: m.mode.replace(', ', '\n'),
    '2019': m.pct2019 * 100,
    '2023': m.pct2023 * 100,
    change: ((m.pct2023 - m.pct2019) * 100)
  }));

  return (
    <ResponsiveContainer width="100%" height="100%">
      <BarChart data={chartData} margin={{ top: 20, right: 30, left: 20, bottom: 60 }}>
        <CartesianGrid strokeDasharray="3 3" stroke="#f0f0f0" />
        <XAxis dataKey="mode" tick={{ fontSize: 11 }} angle={-45} textAnchor="end" />
        <YAxis label={{ value: 'Percentage (%)', angle: -90, position: 'insideLeft' }} />
        <Tooltip formatter={(value, name) => [\`\${Number(value).toFixed(1)}%\`, name]} />
        <Bar dataKey="2019" fill="#9ca3af" name="2019" radius={[2, 2, 0, 0]} />
        <Bar dataKey="2023" fill="#16a34a" name="2023" radius={[2, 2, 0, 0]} />
      </BarChart>
    </ResponsiveContainer>
  );
}

Interactive Dashboard Components

The application features six main sections:

1. Overview Dashboard

• Key statistics cards with gradient backgrounds
• Automated insights highlighting top changes
• Integrated hourly pattern preview

2. Mode Share Comparison

• Interactive bar chart comparing 2019 vs 2023
• Automatic identification of significant changes
• Key changes summary cards

3. Hourly Trip Distribution

• Line chart showing travel patterns throughout the day
• Peak hour identification and analysis
• Responsive design for mobile viewing

4. Distance Analysis, Frequency Patterns, and Destination Flows

• Specialized visualizations for different aspects of travel behavior
• CSV download functionality for researchers

Key Discoveries from the Data

The Transport Revolution: 2019 → 2023

🚌 Bus Renaissance: Bus ridership experienced the most dramatic change

• 2019: 4.1% of all trips
• 2023: 12.2% of all trips
• Change: +8.1 percentage points (nearly tripled!)

🚶 Walking Surge: Active transport gained significant ground

• 2019: 36.2% of trips
• 2023: 39.6% of trips
• Change: +3.4 percentage points

🚗 Car Passenger Decline: Shared car travel dropped substantially

• 2019: 19.9% of trips
• 2023: 13.8% of trips
• Change: -6.1 percentage points

Peak Hour Patterns Remain Consistent

Despite dramatic mode shifts, Manchester's travel timing stayed remarkably consistent:

• Morning Peak: 8:00 AM (113k trips)
• Afternoon Peak: 3:00 PM (145k trips)
• Evening Commute: Distributed 5:00-6:00 PM
• Low Point: 4:00 AM (near zero activity)

Distance and Demographics

Trip Length Evolution:

• 2019: Average 5.97 km per trip
• 2023: Average 6.20 km per trip (+3.8%)
• Suggests slightly longer trips, possibly due to mode consolidation

Age Distribution (2023):

• 17-24 years: 18.6% of trips (highest single group)
• 25-34 years: 15.0% of trips
• 65+ years: 13.3% of trips
• Shows active travel across all age groups

Urban Planning Impact

What the Data Reveals

1. Public Transport Recovery: The dramatic bus ridership increase suggests successful public transport investments and possibly reduced car dependency post-pandemic.

2. Active Transport Growth: Walking increases indicate either shorter trip distances, better infrastructure, or changing lifestyle preferences.

3. Shared Mobility Decline: The drop in car passenger trips might reflect reduced household sharing or shift to individual transport modes.

4. Resilient Travel Patterns: Despite mode shifts, peak hour patterns remained consistent, suggesting fundamental commute patterns survived pandemic disruptions.

Policy Implications

• Bus Investment Vindicated: The 8.1pp increase in bus usage suggests infrastructure investments paid off
• Active Transport Infrastructure: Walking increases support continued investment in pedestrian facilities
• Peak Hour Management: Consistent peak patterns mean existing traffic management strategies remain relevant
• Integrated Planning: Mode shifts show the interconnected nature of transport choices

Conclusion

The Manchester Travel Patterns Explorer demonstrates how thoughtful data visualization can transform complex transport datasets into actionable insights. By revealing the dramatic shifts in Greater Manchester's mobility landscape—from the bus ridership surge to walking increases—the platform helps planners, policymakers, and residents understand how their city adapted to post-pandemic realities.

The most rewarding aspect was seeing how interactive charts could make transport planning data accessible to non-experts. When someone can quickly understand that bus usage nearly tripled while car passenger trips dropped by a third, that's data visualization serving its highest purpose.

As cities worldwide grapple with changing mobility patterns, tools that can quickly communicate transport trends will become increasingly valuable for evidence-based planning and public engagement.

The platform continues to inform transport planning decisions across Greater Manchester.

---

Data source: Greater Manchester Travel Diary Survey (TRADS) 2019 & 2023. This analysis is for informational and planning purposes. Transport planning decisions should incorporate multiple data sources and professional consultation.

Technical Note: The visualization methodology and statistical analysis are transparent and reproducible. All percentage calculations and trend analysis are documented in the project repository.