Have Your Say – ATRI 2025 Industry Issues Survey — The Road Ahead for Trucking

Complete the ATRI 2025 survey now: spend 10 minutes to register priorities that direct federal and state administration funding toward specific road repairs and safety programs, because timely input shifts project scoring and grant eligibility. Your responses help officials prioritize corridors, traffic control upgrades, and pavement preservation rather than relying on abstract models.

Explore the survey’s core areas and act on them: test automation in terminal and yard operations with a 90-day pilot, measure moves per hour, incident rate, and labor-hours saved, and set a 12–18 month payback target before fleet-wide buying decisions. Use the study results to rank infrastructure projects by measurable impacts–reduced crash rates, payload efficiency and fuel savings–so capital goes to sections that deliver the largest measurable return.

Address workforce gaps with concrete steps: require each supervisor to complete a standardized 40-hour safety-and-mentoring course, launch a six-month mentor program pairing new hires with experienced drivers, and tailor recruitment to reflect gender diversity in job postings and benefits packages. These actions reduce turnover and improve on-road performance by aligning supervision, training, and incentives with operational realities.

Contribute now so ATRI can compare pre-pandemic baselines with current conditions and identify persistent bottlenecks that warrant legislative or administrative fixes. Make sure your voice joins other surveyed american carriers, drivers, and suppliers–the survey converts frontline observations into actionable policy recommendations and investment priorities that directly affect buying cycles and daily operations.

Participate in the ATRI 2025 Survey: Practical steps

Submit the ATRI 2025 survey within the first two weeks of release; allocate 20–30 minutes per response and designate a single point person to complete and sign off on answers so entries remain consistent across your company.

Collect quantitative inputs for the past four quarters: driver turnover rate, average loaded miles, maintenance downtime hours, recruiting yields per posting, and the number of bathroom-access complaints per route. Pull supporting documents (safety logs, HR reports, marketing campaign KPIs) into a single folder and label files with YYYY-Q format to speed entry.

Ask supervisors to review draft answers and highlight items requiring additional investigation; record which topics require follow-up and assign owners with deadline dates. If your team has worked with outside researchers–e.g., palmer or a professor who evaluates safety metrics–attach their memos to explain methodology and reduce clarification requests.

Frame qualitative responses around the industrys three largest concerns you tracked: driver shortage trends, operations declines tied to regulatory changes, and disease-related disruptions to scheduling. Use plain language, avoid jargon, and include one concrete metric per claim (percentage change, headcount, or cost impact) so ATRI can quantify the effect on its agenda.

After submission, schedule a 30-minute debrief with stakeholders to review which recommendations you want ATRI to notice and to prepare targeted comments for stakeholder briefings. Set aside resources for follow-up interviews ATRI may request, remain focused on verifiable data, and archive your workbook so you can reproduce answers for future surveys in the Netherlands or other regions.

Confirm your role: carrier, driver, broker, supplier or regulator?

Select the single role that most closely matches your day-to-day duties; if you are contracted to another company, check “contracted” and list the company name so responses reflect your true affiliation.

Accurate role choice affects how ATRI weights responses and the relative result reported by role groups; past publication reported role-specific patterns that influence policy recommendations and industry guidance.

If you drive and dispatch is not your primary responsibility, choose “driver”; owner-operators who both drive and run a business should pick the role they spend the majority of hours in, and note secondary activities in the comments so analysts understand mixed careers.

Carriers should report fleet size and whether technology is used for routing, ELDs or telematics; brokers should identify if they operate under contract or as independent intermediaries, and suppliers should specify product categories so ATRI can link responses to supply-chain impacts.

Regulators must indicate jurisdiction and whether responses represent a single agency or a coalition; if your operations include italy or other regions, tick those boxes to allow regional breakdowns which make conclusions more actionable.

Highlight workplace issues you faced, such as talent shortages or safety concerns, and quantify them where possible (for example: number of vacancies, percentage rise in turnover); concrete figures improve the survey’s value and the publication’s precision.

Opt into the mailing list if you want to receive the summary report and full publication; you can also request anonymity while still allowing ATRI to use your responses for aggregated analysis.

Include specific examples of technology used in transportation, contract status, and any metrics your company tracks; those inputs produce clearer results and show appreciation for the frontline heroes whose feedback shapes practical recommendations and the final conclusion.

Rank priorities: method to choose your top three industry issues

Score each issue on three numeric axes–impact (50%), likelihood (30%), solvability (20%)–and choose the three issues with the highest weighted scores; this yields a clear, repeatable ranking instead of a subjective shortlist.

Implement the method in five actions: 1) list candidate issues and assign a simple 1–10 value on each axis; 2) compute Priority Index = 0.5*impact + 0.3*likelihood + 0.2*solvability for every issue; 3) sort by index and set issues scoring below 4 away as low priority; 4) flag ties for a team vote; 5) document rationale for your top three and record which data points drove the scores. Use a spreadsheet to automate calculations and capture versions over time.

Weight adjustments matter across occupations and fleet types: many driver-facing problems deserve higher solvability weight for driver trainers, while maintenance teams should elevate asset-related impacts. Respondents such as williams, palmer, and khalid described differing priorities during-covid-19 that shifted indexes for driver retention and supply chains; capture those perspectives in a small panel review consisting of at least one operations lead, one safety manager, and one driver representative.

Use concrete examples to validate choices: if accidents rate 7/10 impact, 6/10 likelihood, 5/10 solvability → Priority Index = 0.5*7 + 0.3*6 + 0.2*5 = 6.4; compare that to an asset-theft score of 8/10, 4/10, 3/10 → Index = 6.2. Those calculations show why accidents rank above asset theft in this scenario. Capture differences across white- and brown-fleets and across regions to avoid overgeneralizing from a single data source.

Address common gaps: quantify missing data before finalizing your three and run sensitivity checks by shifting weights ±10% to see ranking stability. Present the top three with supporting metrics, a one-paragraph operational plan for each, and a short mitigation budget; the team can then pick the best implementation sequence, which keeps debate focused and decisions actionable.

Prepare evidence: which KPIs, incidents and timelines to include

Deliver a dataset that lists KPIs with clear denominators and time stamps so reviewers can validate trends: crash rate per 100 million vehicle miles, fatality and injury counts per 10,000 driver-years, HOS violations per 1,000 inspections, detention hours per load, loaded miles percentage, deadhead percentage, average earnings per hour, average weekly hours, and tonnage moved per lane per month.

Report incidents with exact dates, locations and contributing factors: police-reportable collisions, near-misses captured by telematics, brake or tire failures logged in maintenance systems, cargo thefts, weather closures, COVID absenteeism, and supplier interruptions that forced order cancellations. Tag each incident with whether drivers, shippers or suppliers were deemed vulnerable and note if aging equipment contributed.

Use comparison windows: baseline (2018–2019), during-covid-19 (March 2020–December 2021), recovery (2022–2024) and rolling 12-month snapshots updated monthly. Highlight month-level spikes (for example, april 2020 and april 2021) and include weekly detail around major events. Keep raw logs and summaries for each period; records must be kept for at least 60 months to permit multi-level trend analysis.

Normalize metrics to reveal exposure: express crashes per million miles and incidents per 10,000 driver-hours, show tonnage per tractor and amount of freight delayed per lane. Provide sample sizes (number of drivers, tractors, loads) and percentage of fleet that participated in telematics or safety programs. Flag any subgroup with declines greater than 10% versus baseline (driver earnings, hours, or on-time performance).

Supply metadata and quality checks: source system names, timestamp resolution, missing-data flags, edit rules, and a changelog for data corrections. Include categorical codes for incident severity, maintenance type and supplier name so ATRI can crosswalk with national datasets. Indicate which carriers consented to share de-identified driver-level data and whether associations such as ooida or individuals like spencer and karickhoff participated or provided position statements.

Recommend thresholds for policy weight: treat sustained increases greater than 15% in crash rate or a 20% rise in detention hours over two consecutive quarters as high-priority; treat single-month outliers as signals for targeted inspections. Provide both absolute amounts and rate-based measures so readers from around the world see how local changes compare to global averages. ATRI evaluates submitted evidence against these metrics and will use them to inform survey recommendations and research priorities.

Protect sensitive data: how to anonymize responses and manage consent

Separate personally identifiable information (PII) from survey answers immediately: assign a random study ID, store PII in a secured database encrypted with AES-256, and keep survey payloads in a different schema that references only the study ID.

• Access controls: enforce role-based access, multi-factor authentication for administration accounts, and audit logs that record who accessed PII and when (retain logs for 2 years).
• Hashing and salts: hash direct identifiers (emails, phone numbers) with SHA-256 plus a per-project salt stored offline; do not reuse salts across projects or contracting vendors.
• Pseudonymization architecture: store PII, identifiers, and research responses in physically separate systems; use a one-way mapping service that issues ephemeral tokens for analysts.

Apply specific anonymization rules before sharing results with policymakers or publishing open datasets: aggregate to geographic units with at least 10 respondents per cell, remove free-text that mentions precise locations or vehicle registration, and bin dates to month or week rather than exact timestamps.

• Minimum cell size: enforce a threshold of >=10 for any cross-tabulation; if a cell would fall below that, merge categories or suppress the cell.
• Differential privacy for high-risk tables: consider adding calibrated noise (epsilon 0.1–1.0 for public tables) for counts that could otherwise allow re-identification on small populations such as specific carrier fleets or rare incident types.
• K-anonymity target: aim for k≥5 for published microdata used by trusted researchers, and require an independent re-identification risk test before release.

Design consent as clear, granular choices and record proof of consent: offer separate checkboxes for primary survey use, sharing with third-party services, and permission to contact for follow-up. Store consent version, timestamp, IP, and UI language to support audit requests.

1. Layered consent model: 1) core survey participation, 2) sharing anonymized aggregates with policymakers, 3) sharing de-identified microdata with vetted researchers.
2. Opt-in defaults: set all optional data uses to opt-in; do not pre-check boxes for additional uses or data sharing.
3. Withdrawal process: allow participants to withdraw within a 30-day window and implement automated deletion of PII within 72 hours of a verified request; mark withdrawn records as removed in all analytic datasets.

Minimize collection: remove questions that collect excess details not required to meet the study goal. For example, ask whether an incident occurred and permit respondents to rank concerns, but avoid collecting exact route coordinates or vehicle plate numbers unless critical and approved with explicit consent.

• Free-text fields: limit to 250 characters and run automated redaction for phone numbers, email addresses, and license plates before storing; flag any field that contains identifiable terms for manual review.
• Daily logs and shift data: bucket shift start times into three categories (day, evening, night) to preserve operational insight for drivers while reducing re-identification chance from highly specific daily patterns.
• E-scooters and niche modes: for incidents involving e-scooters or rare vehicle types, publish only aggregated counts at city or regional level to prevent singling out respondents.

Paper response handling: store completed paper forms in locked cabinets, digitize with a secure scanner into an encrypted repository, then shred originals within 30 days. Log chain-of-custody steps and limit paper copies to one working copy per project.

Vendor and contracting requirements: require SOC 2 Type II or ISO 27001 certification, specify breach notification within 72 hours, mandate data processing agreements that forbid onward sharing without consent, and include right-to-audit clauses. Test vendors annually for compliance.

• Third-party services: encrypt data in transit (TLS 1.2+) and at rest, segregate tenant data, and enforce strict key management. Do not send raw PII to analytics vendors; provide only pseudonymized IDs.
• Contract clauses: include minimum-security controls, retention limits, approved subprocessors list, and penalties for unauthorized disclosure.

Quality control and transparency: publish a short methodology note with each release that explains anonymization steps, suppression rules, and any additional transformations so policymakers and the sector can assess utility and residual risk.

• Re-identification testing: perform technical tests quarterly and after major schema changes; measure re-identification probability and report that metric internally.
• Communication to respondents: tell respondents how their voice will be used, what was shared, and the chance of any re-identification (report estimated risk percentage for sensitive tables when asked in additional questions).

Incident response and retention: maintain a breach playbook that includes immediate containment, notification to affected participants within 72 hours, and regulatory reporting as required. Set a default PII retention of 3 years, then either securely delete or fully anonymize remaining records for long-term analysis.

Train staff and enforce procedures: provide role-specific training every 6 months, require attestations for anyone handling PII, and conduct random audits of daily handling practices to check that security controls didnt lapse.

Measure privacy impact: run a Data Protection Impact Assessment (DPIA) before launch, document residual risks, and set a measurable mitigation goal (reduce re-identification risk by at least 75% from baseline). That creates a defensible record for administration and helps policymakers trust published findings.

Post a comment to this article: step-by-step posting instructions and a short template

Keep your comment under 200 words, cite one specific ATRI 2025 finding, and suggest a single actionable change so readers and editors can act ahead of the next industry meeting.

1. Determine where to post: use the comment box below the article or the linked discussion forum. Look for a visible field labeled “Leave a comment” or “Join discussion.”

2. Prep content: pick one thematic point (safety, retention, telematics, prevention). Summarize collected data in one sentence (e.g., “a commissioned study collected five metrics showing substantial driver turnover”).

3. Keep it concrete: state the main problem, one sample metric, and one recommended strategy. Example: “Retention fell 12% year-over-year; implement telematics-based scheduling to reduce overtime.” A little context helps readers decide whether to engage.

4. Tone and format: write in active voice, avoid long paragraphs, and use one-line bullets only if the platform preserves line breaks. If leaving contact info, include role and city; privacy rules must guide what you share.

5. Proof and post: check spelling, confirm links to sourced reports, then submit. After posting, look for replies and plan a short follow-up comment if theyd ask for more detail.

Short template and quick examples are in the table below – adapt the niche and strategies to the varied jobs and fleets you know (local routes, long-haul trucks, specialized hauling).

If you handle varied stuff across terminals, determine one pilot site, get five weeks of telematics data, then report measured gains. This approach helps readers see where change can start and where the industry must focus to reduce risks trucks and drivers face.