How does your city move?
Transport quality of life rankings for 10 Indian cities, based on 19 indicators from IISc Bangalore research.
Framework: Allirani & Verma (2025), IISc Bangalore
"A novel transportation Quality of Life Index framework for evaluating sustainable transport interventions"
Kolkata Metropolitan Region
9 points from grade B — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 57%Accessibility
Weight: 23% | Score: 34%Environmental
Weight: 18% | Score: 51%Mobility
Weight: 16% | Score: 66%Biggest Gap
Accessibility → Transit Stop Density
Transit stop density of 1.1 stops/km², compounded by only 35 km of dedicated cycle infrastructure
Recommendation
Adding feeder bus routes in underserved areas would improve stop density and last-mile coverage
Data Readiness
63%New Town Rajarhat lacks published bus route and walking infrastructure data; WBTC publishing GTFS feeds and KMC mapping footpath encroachment would enable corridor-level NMT analysis
Chennai
9 points from grade B — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 42%Accessibility
Weight: 23% | Score: 58%Environmental
Weight: 18% | Score: 56%Mobility
Weight: 16% | Score: 60%Biggest Gap
Health → Cycling
Cycling share at just 4% of trips, compounded by only 45% of roads have paved footpaths
Recommendation
Building protected cycle networks + bike-share systems could shift trips from private vehicles
Data Readiness
59%Publishing walking/cycling infrastructure data enables corridor-level safety analysis
Mumbai Metropolitan Region
1 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 46%Accessibility
Weight: 23% | Score: 36%Environmental
Weight: 18% | Score: 50%Mobility
Weight: 16% | Score: 49%Biggest Gap
Accessibility → Transit Stop Density
Transit stop density of 0.3 stops/km², compounded by only 23 km of dedicated cycle infrastructure
Recommendation
Adding feeder bus routes in underserved areas would improve stop density and last-mile coverage
Data Readiness
52%TMT, NMMT, and KDMT do not publish GTFS feeds, making satellite city transit coverage hard to verify; no OpenAQ sensors are configured for the MMR, and walking infrastructure data is unavailable across all four constituent cities
Greater Bengaluru
3 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 43%Accessibility
Weight: 23% | Score: 39%Environmental
Weight: 18% | Score: 40%Mobility
Weight: 16% | Score: 49%Biggest Gap
Accessibility → Cycle Infrastructure
Only 30 km of dedicated cycle infrastructure, compounded by rail transit network at 74 km
Recommendation
Building 90 km of protected cycle lanes would significantly improve active mobility access
Data Readiness
74%Publishing walking/cycling infrastructure GIS data enables corridor-level gap analysis
Hyderabad
4 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 40%Accessibility
Weight: 23% | Score: 28%Environmental
Weight: 18% | Score: 60%Mobility
Weight: 16% | Score: 42%Biggest Gap
Accessibility → Cycle Infrastructure
Only 31 km of dedicated cycle infrastructure, compounded by transit stop density of 8.1 stops/km²
Recommendation
Building 93 km of protected cycle lanes would significantly improve active mobility access
Data Readiness
61%Publishing walking/cycling infrastructure data would enable complete active mobility assessment
Pune Metropolitan Region
4 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 46%Accessibility
Weight: 23% | Score: 36%Environmental
Weight: 18% | Score: 32%Mobility
Weight: 16% | Score: 47%Biggest Gap
Environmental → PM₂.₅
PM2.5 at 182 µg/m³ — 12.1x the WHO guideline, compounded by green cover at only 1.2 m² per person — below WHO minimum of 9 m\u00B2
Recommendation
Fleet electrification + congestion pricing could cut transport-related PM2.5 by 30%
Data Readiness
48%PMPML does not publish separate ridership data for PMC vs PCMC zones, making it impossible to assess whether Pimpri-Chinchwad gets proportional bus service; PCMC lacks pedestrian audit data
Kochi
6 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 37%Accessibility
Weight: 23% | Score: 21%Environmental
Weight: 18% | Score: 72%Mobility
Weight: 16% | Score: 34%Biggest Gap
Accessibility → Transit Stop Density
Transit stop density of 1.4 stops/km², compounded by rail transit network at 29 km
Recommendation
Adding feeder bus routes in underserved areas would improve stop density and last-mile coverage
Data Readiness
43%Publishing safety and NMT infrastructure data unlocks health dimension scoring
Ahmedabad
11 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 43%Accessibility
Weight: 23% | Score: 20%Environmental
Weight: 18% | Score: 37%Mobility
Weight: 16% | Score: 30%Biggest Gap
Accessibility → Transit Stop Density
Transit stop density of 0.3 stops/km², compounded by only 29 km of dedicated cycle infrastructure
Recommendation
Adding feeder bus routes in underserved areas would improve stop density and last-mile coverage
Data Readiness
52%Publishing AMTS GTFS feeds and walking infrastructure data enables corridor-level access analysis
National Capital Region
13 points from grade C — improving data coverage would enable targeted upgrade analysis
Health
Weight: 43% | Score: 23%Accessibility
Weight: 23% | Score: 36%Environmental
Weight: 18% | Score: 27%Mobility
Weight: 16% | Score: 55%Biggest Gap
Health → VRU Fatality Share
59% of traffic fatalities are pedestrians and cyclists, compounded by only 22% of roads have paved footpaths
Recommendation
Protected cycle lanes + pedestrian-priority zones at key junctions could halve VRU fatalities
Data Readiness
46%Noida and Ghaziabad publish no city bus data (zero dedicated services), Gurugram lacks pedestrian audit data, and none of the satellite cities publish open GTFS feeds or walking infrastructure data
Indore
8 points from grade D — reducing VRU fatality share from 42% to 29% would add 2 points toward this target
Health
Weight: 43% | Score: 27%Accessibility
Weight: 23% | Score: 7%Environmental
Weight: 18% | Score: 21%Mobility
Weight: 16% | Score: 34%Biggest Gap
Accessibility → Transit Stop Density
Transit stop density of 1.8 stops/km², compounded by only 3 km of dedicated cycle infrastructure
Recommendation
Adding feeder bus routes in underserved areas would improve stop density and last-mile coverage
Data Readiness
30%Publishing bus frequency and walking infrastructure data enables transit coverage gap analysis
Who this is for
Use cases
Non-profits
Benchmark cities for advocacy. Use open data to support active mobility proposals.
Researchers
Access 19 indicators across 10 cities. Open methodology, reproducible results.
Government
Evidence for infrastructure proposals. Walking and cycling corridor data for transport planning.
Investors
Assess city-level transport quality as part of real estate and infrastructure due diligence.
Walking data is especially valuable
Walking route data reveals pedestrian corridors, first/last mile patterns, and areas where footpath infrastructure is needed. As more walkers and transit commuters join Altmo, the intelligence product expands beyond cycling infrastructure into comprehensive active mobility planning.