Pune vs Pimpri-Chinchwad — Vehicle Fleet Comparison
Pimpri-Chinchwad and Pune share roads, borders, and eventually a merged transit authority — but their vehicle fleets have grown at very different rates. At the turn of the millennium, PCMC's fleet was roughly a fifth the size of Pune's. By 2017-18, it was approaching 40%. This page traces that convergence across all major vehicle categories.
Both cities draw on the same Maharashtra RTO dataset: cumulative registrations on the books — not necessarily vehicles currently on the road, and not annual new registrations.
Fleet Size Over Time
PCMC Fleet — 2000-01
Pune Fleet — 2000-01
PCMC Fleet — 2017-18
Pune Fleet — 2017-18
Two-Wheelers: The Dominant Category
Two-wheelers — motorcycles, scooters, and mopeds — account for roughly 60–65% of all registered vehicles in both cities. PCMC's two-wheeler fleet grew at nearly twice Pune's rate, driven by workers commuting from dense residential zones (Pimpri, Bhosari, Charholi) to factory corridors.
Cars and Personal Transport
Cars represent the second-largest personal vehicle category. PCMC started with ~21% of Pune's car fleet in 2000-01; by 2017-18 that share had grown to ~44%. Auto-rickshaws, heavily regulated in both cities, remained a smaller category dominated by Pune.
Growth Rates Compared
PCMC's fleet grew faster in every major category. The strongest differential was in cars (+1,682% vs Pune's +682%) and two-wheelers (+700% vs +369%). Both cities show the classic Indian motorization arc — rapid growth through the 2000s, continuing through the 2010s.
See Also
- PCMC Vehicle Registrations — PCMC-only data with detailed sub-category breakdown and per-capita analysis
- Fleet Composition Trends — Growth rates, market share shifts, and indexed comparisons across PCMC categories
- Road Safety — Accident rates as both fleets expanded
Data: cumulative vehicle registrations with the Pimpri-Chinchwad and Pune RTOs, 2000-2001 to 2017-2018. Source: Maharashtra state RTO registration data via Open City Urban Data Portal.
Data Queries
SQL queries powering the visualizations above.
