This guide, ‘The Tool for the Rapid Assessment of Urban Mobility in Cities with Data Scarcity (TRAM)‘, is prepared by Clean Air Asia and the Institute for Transportation and Development Policy for UN-Habitat (2013).
It introduces a practical methodology for cities in developing countries that lack reliable mobility data. TRAM offers a quick, low-cost way to assess urban mobility conditions and identify priority issues, with a focus on the needs of low-income and marginalized groups.
Key Points
- Need for TRAM: Many rapidly urbanizing cities lack the capacity or resources to conduct comprehensive mobility surveys. Without data, governments often respond with short-term fixes like road widening, neglecting long-term sustainability. TRAM fills this gap by providing a simpler, affordable tool for data collection and analysis.
- Goals: TRAM is designed to help local leaders, planners, and municipal authorities make informed, pro-poor decisions by mapping transport conditions, highlighting gaps, and identifying where detailed studies are most needed.
- Components: The tool has three main parts:
- Stakeholder Meetings: Engaging officials and experts to frame local mobility issues.
- Focus Group Discussions: Capturing the experiences of vulnerable groups (e.g., women, elderly, differently-abled, low-income households).
- Household Travel Surveys: Collecting trip data (purpose, mode, distance, cost, and challenges) from representative neighborhoods using a typology method (central/peripheral, income, and transit access).
- Methodology: By surveying just 12 neighborhood types instead of entire cities, TRAM reduces cost and time while still generating citywide mobility insights. Results include statistics on mode share, travel time, costs, and perceptions of transport safety and quality.
- Strengths: Low-cost, fast (can be done in six weeks), participatory, and inclusive of marginalized voices. Provides both quantitative and qualitative data that can guide mobility planning.
- Weaknesses: Results are less statistically rigorous than full-scale surveys, rely on expert judgment for neighborhood classification, and may not fully capture long-term urban mobility trends.
- Future Improvements: Incorporating new digital survey and mapping tools (e.g., mobile apps, GPS-based data collection) could enhance accuracy and reduce costs further.