claim_text stringclasses 3
values | source_url null | target_year int64 2.04k 2.05k | webpage_text stringclasses 3
values | forecast_unit stringclasses 3
values | claim_category stringclasses 1
value | forecast_value stringclasses 3
values | scenario_match bool 1
class | forecast_metric stringclasses 3
values | forecast_period stringclasses 3
values | baseline_scenario stringclasses 3
values | claim_temporality stringclasses 1
value | evidence_relation stringclasses 3
values | evidence_paragraph stringclasses 3
values | verification_label stringclasses 3
values | forecast_assumptions stringclasses 3
values | temporal_scope_match bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Under the reference case, weekday vehicle trips are forecast to total 480,000 in 2035. | null | 2,035 | The Central Corridor Travel Demand Plan states: Under the reference case, weekday vehicle trips are forecast to total 480,000 in 2035. The forecast table lists 2035: 430,000 weekday vehicle trips; 2040: 480,000 weekday vehicle trips. Both figures use the reference case, which assumes adopted zoning and no additional ro... | trips per weekday | 需求预测 | 480,000 | true | weekday vehicle trips | 2035 | Reference case | 预测 | 反驳 | The forecast table lists 2035: 430,000 weekday vehicle trips; 2040: 480,000 weekday vehicle trips. Both figures use the reference case, which assumes adopted zoning and no additional road-capacity program beyond projects already funded. | 反驳 | Adopted zoning and no additional road-capacity program beyond projects already funded. | false |
Under the low-growth case, inbound rail demand during the morning peak is forecast at 18,000 passengers per hour in 2050. | null | 2,050 | Coastal Access Plan, 2050 demand assessment. Under the low-growth case, inbound rail demand during the morning peak is forecast at 18,000 passengers per hour in 2050. The low-growth case assumes annual population growth of 0.4 percent. The assessment compares low-growth, adopted-plan, and accelerated-development cases ... | passengers per hour | 需求预测 | 18,000 | true | inbound rail demand during the morning peak | 2050 morning peak | Low-growth case | 预测 | 证据不足 | The assessment compares low-growth, adopted-plan, and accelerated-development cases for 2050. The main-page summary does not provide the model table or an independent value for inbound morning-peak rail demand; detailed demand tables are listed in Appendix C. | 证据不足 | Annual population growth of 0.4 percent. | true |
Under the adopted land-use baseline, weekday transit boardings are forecast to reach 1.26 million in 2045. | null | 2,045 | Regional Mobility Outlook, adopted plan. The forecast covers average weekday service. Under the adopted land-use baseline, weekday transit boardings are forecast to reach 1.26 million in 2045. This is a modeled forecast, not an observed count. The baseline assumes adopted zoning, completion of the funded rapid-transit ... | boardings | 需求预测 | 1.26 million | true | weekday transit boardings | 2045 | Adopted land-use baseline | 预测 | 支持 | Under the adopted land-use baseline, weekday transit boardings are forecast to reach 1.26 million in 2045. This is a modeled forecast, not an observed count. The baseline assumes adopted zoning, completion of the funded rapid-transit network, and population growth in line with the regional plan through 2045. | 支持 | Adopted zoning, completion of the funded rapid-transit network, and population growth in line with the regional plan through 2045. | true |
Transportation Planning Forecast Fact Verification Training Dataset
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.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| 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 | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| 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 |
Source & Contact
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