Transportation Planning Forecast Fact Verification Training Dataset

#fact verification #temporal reasoning #evidence-based classification #transportation planning #traffic forecast verification #transportation policy assessment

5000
0.2GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-19

AI Analysis & Value Prop

This dataset focuses on transportation demand forecasts and related planning claims found in transportation planning webpages. Records organize claim categories and temporal attributes alongside forecast metrics, values, units, target years, baseline scenarios, and assumptions. Each record links the original webpage text to an evidence paragraph, an evidence-claim relation, and a supervised verification label, supporting analysis of forecast versus observed values and checks for temporal and scenario alignment. It is suitable for fact verification model training, evaluation, and transportation planning information extraction research.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
claim_text	string	A transportation demand forecast or related planning claim extracted from the webpage text.
source_url	string	The URL of the original webpage containing the transportation planning text.
target_year	integer	The year targeted by the forecast; may be left blank if the source does not specify a year.
webpage_text	string	The original text retrieved from the source webpage and used as the basis for claim and evidence annotations.
forecast_unit	string	The unit or measurement method associated with the forecast value, such as passenger trips, vehicle count, or percentage.
claim_category	string	The category addressed by the claim, such as a demand forecast, target year, baseline scenario, or forecast assumption.
forecast_value	string	The forecast value or range stated in the claim, retained in its original wording to accommodate ranges, approximations, and nonstandard formats.
scenario_match	boolean	Indicates whether the scenario described by the evidence matches the baseline scenario applicable to the claim; may be left blank when it cannot be determined.
forecast_metric	string	The transportation demand metric covered by the claim, such as passenger volume, trip count, or traffic flow.
forecast_period	string	The time interval or point covered by the forecast, such as a particular year, planning period, or phase.
baseline_scenario	string	The baseline or planning scenario on which the forecast is based, including its name and relevant details.
claim_temporality	string	Indicates whether the claim describes a future forecast, an actual observation, or another temporal attribute.
evidence_relation	string	Indicates whether the evidence paragraph supports or contradicts the claim to be verified.
evidence_paragraph	string	The original webpage paragraph used to support or verify the claim.
verification_label	string	The supervised verification conclusion assigned to the claim based on the evidence.
forecast_assumptions	string	Key assumptions underlying the forecast, such as assumptions about population, land use, policy, or travel behavior.
temporal_scope_match	boolean	Indicates whether the time scope of the evidence matches that of the claim; may be left blank when it cannot be determined.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: The data covers demand forecasts and related claims from transportation planning webpages. Fields include source URLs, webpage text, claims, claim categories and temporal attributes, forecast metrics, values, units, target years, forecast periods, baseline scenarios, and assumptions. Records also provide evidence paragraphs, evidence relations, verification labels, and temporal and scenario match indicators.

How can the data support model training and evaluation?: The structured fields connect claims with evidence and verification outcomes. They can support training or evaluating models that distinguish forecasts from observations, determine whether evidence supports a claim, and check temporal and scenario alignment. Review the accompanying documentation for label definitions, missing-value rules, and evaluation splits before use.

How can I obtain and use the data?: Use the delivery method provided on the current dataset product page or platform, and consult the accompanying documentation for field definitions and data formats. Potential uses include fact verification, information extraction, transportation planning research, and related model evaluation.

What licensing considerations apply to the webpage content?: Source URLs and webpage text may be subject to the original websites' copyright, access terms, or other restrictions. Before use, review the dataset license information and the source websites' terms. Confirm that you have the necessary rights and permissions before redistributing the content or using it commercially.

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Cite this Work

@dataset{Mobiusi2026,
  title={Transportation Planning Forecast Fact Verification Training Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/33f0fcf8f887d29fc5c2c5f7a08f863f?dataset_scene_id=1},
  urldate={2026-10-09},
  keywords={transportation planning dataset, demand forecasting, fact verification training data, forecast validation, web text dataset, evidence annotation, scenario verification, transportation AI},
  version={1.0}
}

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