--- title: "Research and partnerships" description: "Reading list, dataset architecture, and partnership opportunities for insurers, fleet operators, public-sector environmental programmes, and academic researchers." url: https://odoma.ee/en/impact/research language: en published: 2026-05-02 updated: 2026-05-02 publisher: Odoma Digipädevuse Selts (Estonian non-profit, registry code 80659718) --- # Research and partnerships This page is the entry point for **researchers, insurers, fleet operators, and public-sector environmental programmes** who want to understand what Odoma measures, how it can plug into a study or a pilot, and what data is — and is not — available for sharing. For the technical detail on detection and scoring, see the [Methodology](https://odoma.ee/en/impact/methodology). For live aggregate impact metrics, see /impact. ## EU policy and regulatory context - **EU 2035 zero-emissions target for new cars and vans** — [European Commission, Cars and vans](https://climate.ec.europa.eu/eu-action/transport-decarbonisation/road-transport/cars-and-vans_en). The technical pathway is fleet electrification; the behavioural pathway — driving style — remains uncovered by the regulation, which is exactly the gap Odoma addresses. - **EEA — Sustainability of Europe's mobility systems 2024** — [eea.europa.eu](https://www.eea.europa.eu/en/analysis/publications/sustainability-of-europes-mobility-systems/climate). The reference document on whether the EU is on track for transport decarbonisation. Spoiler: not yet. - **EEA — Average CO₂ emissions from new passenger cars and future targets** — [eea.europa.eu](https://www.eea.europa.eu/en/analysis/maps-and-charts/average-co2-emissions-from-new-1). - **EEA — Decomposition analysis of EU-27 passenger-car CO₂ 2000–2023** — [eea.europa.eu](https://www.eea.europa.eu/en/analysis/publications/emissions-reduction-from-transport-in-europe-how-the-ets2-will-help-this-sector-meet-its-climate-targets/co2-emissions-passenger-cars). Quantifies how much of the change came from technology vs activity vs intensity. - **GDPR (Regulation EU 2016/679)** — [eur-lex.europa.eu](https://eur-lex.europa.eu/eli/reg/2016/679/oj). All Odoma data flows are designed to satisfy Articles 5, 6(1)(a), and 25. See §8 of the [Methodology](https://odoma.ee/en/impact/methodology#8-privacy-and-data-architecture). ## Eco-driving — peer-reviewed and EU-funded research - **Sanguinetti, A. et al. (2020)** — *Average impact and important features of onboard eco-driving feedback: A meta-analysis.* *Transportation Research Part F* 70, 80–90. The single most useful synthesis: across 17 studies, average improvement is **6.6 %**. [doi](https://doi.org/10.1016/j.trf.2020.02.011) · [open-access](https://escholarship.org/uc/item/99m5j3q7) · [local mirror](https://odoma.ee/research/sanguinetti-2020-eco-driving-feedback-meta-analysis.pdf) - **ecoDriver (FP7, 2011–2016)** — €14.6 M, 170 drivers, 7 countries. The benchmark for GPS-only smartphone applications: **2.5 % fuel savings**, OBD systems reach 6 %. [CORDIS 288611](https://cordis.europa.eu/project/id/288611) - **GamECAR (H2020, 2016–2019)** — €1 M, 36 drivers, Spain. Demonstrated **0.59 L/100 km savings** with eco-score, leaderboard, and challenge mechanics (p = 0.004, Cohen's d = 1.026 — large effect). [CORDIS 732068](https://cordis.europa.eu/project/id/732068) - **Baumgartner, M. et al. (2019)** — *Long-term effects of gamification on eco-driving behaviour.* *Energy Research & Social Science* 57. 22-month BEV study (n = 108) showing gamification builds transferable skill. [doi](https://doi.org/10.1016/j.erss.2019.101237) ## Telematics, harsh-event detection, and crash-risk research - **NJIT / MDPI Vehicles 2025** — *Evaluating Harsh Braking Events as a Surrogate Measure of Crash Risk Using Connected-Vehicle Telematics.* The reference paper on why harsh braking is the strongest crash-risk predictor in telematics. Industry thresholds range 0.20 g – 0.45 g. [mdpi.com](https://www.mdpi.com/2624-8921/8/3/68) - **ETSC 2019** — *Telematics and Road Safety.* European Transport Safety Council. The canonical EU-policy document on how telematics signals translate to measurable safety outcomes. [etsc.eu](https://etsc.eu/wp-content/uploads/TELEMATICS_FINAL_2019_LR.pdf) · [local mirror](https://odoma.ee/research/etsc-2019-telematics.pdf) ## Insurance and UBI market context The same signals Odoma collects (acceleration, braking, mileage, time-of-day) underpin the European usage-based insurance market. Useful market references: - **Berg Insight 2025** — *Insurance Telematics in Europe and North America, 9th edition.* European UBI policies: **13.8 M at end-2024, 20.1 M projected by 2029** (CAGR 7.8 %). [berginsight.com](https://media.berginsight.com/2025/12/04114002/bi-insurancetelematics9-ps.pdf) - **PTOLEMUS Consulting Group 2025 — UBI solution provider ranking.** Lists IMS, Octo, The Floow, Cambridge Mobile Telematics, Targa Telematics, Munic, Geotab, Vodafone Automotive, Arity, FairConnect, Dolphin Technologies, Redtail Telematics. [ptolemus.com](https://www.ptolemus.com/insight/2025-ubi-solution-provider-ranking/) - **Mordor Intelligence 2025 — Europe Insurance Telematics Market Size.** Market projected from USD 0.97 B (2025) to USD 2.78 B (2030). [mordorintelligence.com](https://www.mordorintelligence.com/industry-reports/europe-insurance-telematics-market) - **Database / DataBridge Market Research 2025 — Europe Usage-Based Insurance Market.** [databridgemarketresearch.com](https://www.databridgemarketresearch.com/reports/europe-usage-based-insurance-market) Odoma is not an insurance product. We publish the methodology and dataset schema in the open so that insurers and fleet operators can audit it before any pilot conversation. ## Crowd-sourced dataset uplink For external research and pilot use, Odoma offers an **opt-in, two-tier crowd-sourced dataset**: - **Tier A — non-longitudinal, anonymous by default.** Per-trip aggregate record (no coordinates, no exact timestamps, all numeric features bucketed into discrete bins). Default opt-in available in Settings. - **Tier B — longitudinal, explicit consent.** Adds a device-stored `pseudo_id` (Keychain on iOS, app-private storage on Android) so that multiple trips from the same device can be linked. Required for studies of within-driver behaviour change. Withdrawable at any time; `pseudo_id` rotation supported. Architectural invariants: - **No GPS coordinates ever leave the device.** Only bucketed aggregates do. - **No exact timestamps.** Only `time_of_day_bucket` (4 bins) and `day_of_week`. - **No raw streams.** Speeds and event rates are reported as multi-threshold CDFs. - **No direct identifiers.** No BT MAC, no BT name, no phone model, no VIN, no email. - **K-anonymity guard at backend** with auto-promoted `k = 20` for small countries (EE / LV / LT / LU / CY / MT etc.) to prevent re-identification. - **Region-aware data residency.** EU users land in an EU Firestore project; non-EU users land in a US project. ISO 3166-1 country code drives the routing. - **Schema versioning is additive.** New fields are nullable adds within `upload_schema_v: 1`; any breaking semantic change bumps to v2. The full schema and the rules governing it are public. (Documents are in the codebase repository under `docs/dataset_schema.md` and `memory/crowd_dataset_schema_rules.md`; we will publish the rendered HTML on this page once the uplink ships in v1.4.) ## Driver-attribution and passenger mode A practical issue for any phone-based driving telematics is what happens when the phone is **in a taxi**, **on public transport**, or **in the passenger seat of a friend's car**. The same accelerometer signal arrives, but it is no longer the holder's behaviour. Odoma classifies non-self trips with three confidence tiers: - **High confidence — public transport pattern.** Eight-feature heuristic (stops-per-km, duration variance, speed P95 ≤ 80, route linearity, etc.). Silently classified as `.publicTransport`. - **Medium confidence — style mismatch.** Z-score against the user's per-driver baseline, off-hours, BT mismatch. Classified as `.passenger` with a non-modal hint. - **Low confidence — single signal.** Defaults to `.self` and prompts the user to confirm in Trip Detail. For all non-`.self` trips, eco / safety / driver-loss aggregates **are not computed and not displayed**, and the trip is excluded from the crowd-sourced uplink. This is enforced as a hard guard, not a UI suggestion. A **Travel Mode** auto-activates after multiple signals (foreign country / multiple no-BT trips / multiple public-transport matches / no-saved-geofence starts) and biases classification toward `.passenger` for 7 days, suspending eco / safety aggregates for the period. The full design — eight ethical guardrails, classifier signals, override actions, and the off-roadmap Tier C (taxi/transit research with separate consent) — is documented internally and will be summarised here on uplink launch. ## Working with us We're open to: - **Pilot partnerships** — schools, fleet operators, environmental NGOs, universities, public-sector environmental programmes, and insurers interested in field-validating the methodology. - **Co-funding** — to scale beyond Estonia and from one product to a portfolio aligned with our six statutory aims. - **Volunteers** — translators, designers, environmental specialists, lawyers, communicators. For partnership enquiries: Contact us (https://odoma.ee/en/about/contact) See also: [About Odoma](https://odoma.ee/en/about) · [Methodology](https://odoma.ee/en/impact/methodology)