Commercial Viability of Secondary Driving-Data Products from Autonomous Ride-Hailing Fleets: A Techno-Economic and Competitive Feasibility Framework
Year: 2026
Type: preprint
Abstract: Autonomous ride-hailing fleets can generate road, traffic, mapping, weather, and validation data as a by-product of mobility operations, but the existence of data does not establish a commercially viable data business. This paper develops a transparent techno-economic framework that separates external cash revenue from internally attributed strategic benefit, models subscription churn and contract timing, and tests the interaction of sensor bitrate, qualified collection hours, edge upload ratio, retention, dataset cadence, and buyer conversion. Reconciliation of the original lower-bound calibration shows external revenue of EUR 7.96 million against incremental cost of EUR 9.26 million over 36 months, producing a commercial cash contribution of EUR -1.30 million. A risk-adjusted reference case using a 100 Mbps selective commercial stream, 12 qualified collection hours per day, a 5 percent upload ratio, 20 percent annual subscription churn, six-month dataset cadence, and delayed strategic licences produces EUR 6.44 million of external revenue, EUR 12.97 million of cost, and a EUR -6.53 million commercial contribution. Sensitivity and multivariate uncertainty analyses identify retention, upload ratio, bitrate, collection hours, and fixed operating capability as the principal economic variables. Competitive analysis shows that a 500-vehicle fleet cannot credibly compete with incumbent traffic and mapping networks through generic geographic coverage. Commercial viability instead requires differentiated buyer-specific products based on dense local operational-design-domain coverage, multimodal edge cases, time-sensitive road changes, defensible data rights, and tightly controlled data-handling costs. This record contains the preprint PDF and a reproducibility package comprising the LaTeX manuscript source, Python model, figure files, scenario outputs, sensitivity results, and Monte Carlo analytical outputs. The numerical outputs are scenario-based analytical results and should not be interpreted as observed market transactions or empirical success probabilities.
Author: Padala, Shri Charan
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165757210
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