COVID、CAV 和预测：马里兰州对不确定未来的数据驱动情景分析.pdf

1、COVID,CAV,and Forecasting:Marylands Data Driven Scenario Analysisfor an Uncertain Future Mark Radovic,Gannett Fleming,Inc.Jonathan Avner,Whitman Requardt&Associates,LLCMARYLAND SNAPSHOTRanked 42nd in Area(12,407 mi2)Ranked 19th in Population(6.16 Million),+580,000 by 2045Ranked 22nd in Employment(2.

2、28 Million,3%unemployment)Ranked 5th in Population Density(636.1 residents/mi2)2.29 Million Households,Median Income:$91,400Port of Baltimore is the 11th busiest port in the USTRANSPORTATION CHALLENGESPolitical and geographically diverse(seasonal component)Military bases:Ft.Meade,Ft.Detrick,Aberdeen

3、 Proving Grounds,Andrews Airforce Base,etc.Over 11,000 service members,retirees,civilian employees,contractors and their families reside on Fort Meade245,400 federal employees in nearby Washington,DC8 million travelers visit Ocean City,MD each yearSome roadway segments rank among the most congested

4、in the country4MARYLAND STATEWIDE TRANSPORTATION MODEL(BACKGROUND)Developed in 2006 in coordination with the University of MarylandOriginally built off the of Baltimore Metropolitan Council(BMCs)travel demand model4-step,trip-based model with approx.1,500 TAZsRegional networks were static,stitched t

5、ogetherDeveloped as a complimentary tool to the BMC and MWCOG modelsFHWA peer review performed in 2014Migration from DOS-batch execution to CUBE Voyager/CatalogMARYLAND STATEWIDE TRANSPORTATION MODEL(RECENT ACTIVITIES)Highway network build directly from SHAs centerline data promotes linkage with MDO

6、T asset dataDevelopment ofmulti-resolution zonal and network databases allow for additional flexibility and ability for more refined analysis.MPO zones are fully nested withing MSTM zones S/E data directly input from cooperative forecastsSHRP/2 truck-touring model,commercial vehicle model&national s