An Integrated Tour-based Truck Travel Forecasting Model Ian Harrington Central Transportation...

An Integrated Tour-basedTruck Travel Forecasting Model

Ian Harrington

Central Transportation Planning Staff

Boston, Massachusetts

Outline of Presentation

• Why prepare a new truck model?• Identifying available data• Trip generation model structure• Trip distribution model structure• Trip table adjustment• Forecasting future truck travel

Why Prepare a New Truck Model?

• Previous truck trip tables based on old survey data

• Using truck trip tables allows for no estimation of impact of changes in demographics, infrastructure, tolls, or other changes in regional transportation system

Data Available forTruck Travel Forecasts

• Truck ownership data• Truck/Vehicle Inventory and Use Surveys• Residential location and characteristics• Survey of sample of local businesses• Field observations of trucks• Truck trip generation rates• Interregional truck trip table• Vehicle classification counts

Trip Generation Model Structure

Trucks fall into the following use categories:• Tankers• Household Goods• Truckload/Less-than-Truckload• Food and Warehouse Distribution• Intermodal• Package• Heavy• Retail• Pickup/Van

Trip Generation Model Structure

Truck tours consist of the following trip types:• Regional Truck Tour Ends• Intermediate Starts and Stops• Regional Truck Entrances/Exits• External Truck Entrances/Exits• Through Truck Entrances/Exits

Regional Truck Tour EndsNumber of truck tour ends is a function of:• Number of trucks• Number of tours per day• Portion of days each truck active

TE = 2 * Trucks * Tours * % Days Active Day

Estimated for each truck use category

Regional Truck Tour Ends

Number of trucks per employee by industrial sector based on CTPS survey

AverageSector Trucks/EmpGovernment 0.060Manufacturing 0.045

Agric, Mining, & Constr 0.539Transport, Comm, & Util 0.262

Service 0.030Fin, Insur, & Real Estate 0.003

Retail 0.039Wholesale 0.147

0.076


Cross-classification of trucks by use category and industry based on CTPS field observations

Hhld LTL & Food & PickupSector TankersGoods TruckloadWarehouseIntermodalPackage Heavy Retail and VanGovernment 0.0% 0.0% 0.0% 0.0% 0.0% 20.0% 48.0% 0.0% 32.0%Manufacturing 0.0% 0.0% 0.0% 42.2% 0.0% 0.0% 31.0% 1.7% 25.0%AMC 0.3% 0.0% 0.0% 0.3% 0.0% 0.0% 42.1% 0.3% 57.2%TCU 2.7% 13.2% 34.2% 1.5% 4.0% 11.9% 13.5% 0.2% 18.8%Service 0.7% 0.0% 0.0% 1.2% 0.0% 0.0% 27.6% 0.5% 69.8%FIRE 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 23.1% 0.0% 76.9%Retail 19.9% 0.0% 0.0% 11.2% 0.0% 0.0% 2.7% 53.4% 12.8%Wholesale 5.9% 0.0% 0.0% 78.6% 0.0% 0.0% 2.7% 5.9% 6.8%


Trucks in Government and Manufacturing industries have distinct distributions by use category

Hhld LTL & Food &Sector TankersGoods TruckloadWarehouseIntermodalGovernment 0.0% 0.0% 0.0% 0.0% 0.0%Manufacturing 0.0% 0.0% 0.0% 42.2% 0.0%

PickupSector PackageHeavy Retail and VanGovernment 20.0% 48.0% 0.0% 32.0%Manufacturing 0.0% 31.0% 1.7% 25.0%

Regional Truck Tour EndsOperational data from TIUS/VIUS data for

MassachusettsDays Tours

Active per DayTankers 61.0% 2.01

Household Goods 63.8% 0.9LTL/TL 91.2% 0.9

Food/Warehouse 81.5% 1Intermodal 88.5% 0.95Package 81.5% 1.2Heavy 68.8% 1.15Retail 94.0% 1.1Pickup/Van 86.9% 1.3

Intermediate Starts and Stops

Based upon truck trip generation rates in literature with adjustments for Eastern MA

Hhld LTL Food & Inter- PU/Tankers Goods /TL Warehouse modal Package Heavy Retail Van Total

Government 0.0034 0.0004 0 0.05 0 0.04 0.02 0.015 0.09 0.219

Manufacturing 0.004 0.0003 0.05 0.09 0.003 0.05 0.06 0.021 0.15 0.428

Agric, Mining, & Constr 0.003 0.00005 0.05 0.05 0 0.03 0.03 0.02 0.1 0.283

Transport, Comm, & Util 0.0035 0.0003 0.05 0.05 0.001 0.044 0.044 0.01 0.05 0.253

Service 0.0017 0.0004 0 0.05 0 0.06 0.019 0.015 0.09 0.236

Fire, Insur, & Real Estate 0.003 0.0005 0 0.05 0 0.05 0.02 0.015 0.09 0.229

Retail 0.003 0.0002 0.01 0.53 0.0003 0.04 0.02 0.01 0.09 0.704

Wholesale 0.002 0.0001 0.05 0.06 0.0023 0.04 0.02 0.01 0.11 0.294

Households 0.0095 0.0009 0 0.002 0 0.03 0.035 0.015 0.121 0.213

Group Quarters 0.0010 0.0011 0 0.0008 0 0.0115 0.0035 0.0058 0.0465 0.070


Truck trips generated per employee at government and manufacturing worksites

Hhld LTL Food & Inter-Tankers Goods /TL Warehousemodal

Government 0.0034 0.0004 0 0.05 0Manufacturing 0.004 0.0003 0.05 0.09 0.003

PU/Package Heavy Retail Van Total

Government 0.04 0.02 0.015 0.09 0.2188Manufacturing 0.05 0.06 0.021 0.15 0.4283


Supply of intermediate starts and stops based on operational data:

S&S = Stops/Tour * Tour Ends/2

Stops Stopsper Tour per Tour

Tankers 7 Package 21Household Goods 2 Heavy 4LTL/Truckload 4 Retail 6Food & Warehouse 14 Business PU/Van 4Intermodal 2


Intraregional truck tour starts and stops:

Intra S&S = Tour Ends/2 * (1 - % Tours Ext)

* Stops/Tour

Pct Trips Pct TripsExternal External

Tankers 24.0% Package 2.0%Household Goods 16.5% Heavy 11.0%LTL/Truckload 39.3% Retail 5.0%Food & Warehouse 8.5% Business PU/Van 18.0%Intermodal 50.0%


Regional truck interregional tour

starts and stops:

Reg IX S&S = Tour Ends/2 * % Tours Ext

* Stops/Tour

Assume interregional tours have one-half the number of stops per tour within region


External truck intermediate starts and stops:

Ext S&S = Total S&S – Intra S&S

– Local IX S&S

Regional Truck Entrances/Exits

Regional truck interregional tour external tour ends:

Reg IX Ext TE = 2 * Reg IX S&S Stops/Tour

Assume interregional tours have one half the number of stops per tour within region

External Truck Entrances/Exits

External truck tour ends

Ext TE = 2 * Ext S&S Stops/Tour

Assume interregional tours have one-half the number of stops per tour within region

Through Truck Entrances/Exits

Based on external survey truck volumes, subtract estimated crossings from total

Thru TE = Tot Vol – Reg IX Ext TE – Ext TE

Truck Trip Distribution

• Use estimated trip ends and adjust initial gamma functions to match estimated regional trip length frequencies by use category based on TIUS/VIUS data for Massachusetts and an interregional trip table

• Use double-TAZ setup to match appropriate trip end pairs in trip tables


Match appropriate pairs of trip productions and attractions for intraregional and through truck trips

TAZ A Ext A TAZ B Extern BAttrs Attrs Attrs Attrs

TAZ A P:Local Reg TEsProds A:Local Reg S&Ss

Ext AProds

TAZ B P:Local Reg S&Ss P:Local Reg S&SsProds A:Local Reg TEs A:Local Reg S&Ss

Ext B P:Thru TEsProds A:Thru TEs


Match appropriate pairs of trip productions and attractions for interregional truck trips

TAZ A Ext A TAZ B Extern BAttrs Attrs Attrs Attrs

TAZ A P:Reg IX S&Ss P:Reg IX S&SsProds A:Reg IX Ext S&Ss A:Reg IX Ext TEs

Ext A P:Reg IX Ext TEs

Prods A:Reg IX S&Ss

TAZ B P:Ext S&Ss P:Ext S&SsProds A:Ext S&Ss A:Ext TEs

Ext B P:Ext TEsProds A:Ext S&Ss

Trip Table Estimation

• Since estimated truck trip tables are based on so many assumptions, need to check distribution results

• Create set of truck vehicle counts by use category using vehicle classification counts and a cross-classification of truck use category and FHWA truck class

• Use resultant trip table as seed for new gamma functionsThree Five Six Seven Eight Nine Ten Eleven Totals

Tankers 757 2,499 587 12 336 800 43 0 5,033 2.2%Household Goods 1,722 532 12 0 67 136 0 0 2,470 1.1%LTL/Truckload 60 943 185 0 1,027 1,066 64 58 3,404 1.5%

Food & Warehouse 7,956 5,056 834 0 865 1,242 12 12 15,978 7.1%Intermodal 0 0 0 0 386 326 0 0 711 0.3%Package 9,032 3,681 125 0 0 0 0 0 12,837 5.7%Heavy 7,403 19,215 4,744 850 1,107 2,342 423 0 36,085 16.1%Retail 19,970 1,636 12 0 48 106 0 0 21,772 9.7%Business PU/Van 125,174 1,348 0 0 0 0 0 0 126,521 56.3%TOTALS 172,073 34,910 6,498 862 3,836 6,019 543 70 224,812

Forecasting Future Truck Travel

• Apply truck trip generation model -- with future scenario employment, household, group quarters, and external station trip ends -- to estimate tour ends, starts and stops, and entrances/exits

• Apply gamma functions and productions and attractions for initial estimate of truck trip tables

• Apply trip table adjustment factors to produce future-year truck trip tables based upon future-year demographics and network characteristics

Summary

• Now our truck travel forecasting model is sensitive to changes in regional demographic characteristics, infrastructure, tolls, and the regional transportation system.

Contact Information

Ian [email protected]

Chief Transportation Planner

David S. [email protected]

Central Transportation Planning Staff to the Boston Region Metropolitan Planning Organization (www.bostonmpo.org)

An Integrated Tour-based Truck Travel Forecasting Model Ian Harrington Central Transportation...

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