Improvements and Innovations in TDF

1. Improvements and Innovations in TDF CE 451/551 Source: NHI course on Travel Demand Forecasting (152054A) Chapter 12

2. Review of the Four-Step Process • What was the original intention of the TDF process? • What has changed? • What questions are we trying to answer now? • Is the current model structure capable of addressing these issues? • What are some of the weaknesses in the models?

3. Improvements to Traditional Four-Step Modeling Accessibility and Land Use Character Improved Travel Surveys and Data Collection (all steps) Trip Generation Land Use Allocation and Forecasting Procedures Non-motorized Transportation PEF BEF LOS Trip Chaining Behavior and Activity Modeling Trip Distribution Multimodal Travel Time and Impedance Dynamic Traffic Assignment and Simulation Modeling Feedback Analysis Mode Choice Operational Improvements Trip Assignment Time-of-Day Models and Peak Spreading Improved Speed Models

4. What are we currently working with? • 4-Step Process is Current State of the Practice for TDF and Policy Analysis. • Basics Developed 1950’s and 1960’s • Post-War Expansion Period in U.S. • Urban Population Growth • Motor Vehicles More Ubiquitous • Suburban Sprawling Starting blogs.ipswitch.com/archives/2005/08/ http://www.allposters.com/ www.fortunecity.com/tinpan/parton/2/1950s.html

5. Focused on Infrastructure Development • Interstate Construction Era • Where to Build Them? • How Many Lanes? • Straight Forward Planning Context • Coarse Forecasting Procedures Sufficient • Population Increases and So Do Trips. • Just figure out where the facilities were needed.

6. Times Are Changing… • 1970’s Transportation Systems Management Promoted • 1980’s Travel Demand Management Proposed • We now embrace a wider range of Transportation Control Measures. • These measures are increasingly more sophisticated. • The trip based 4-step procedures previously developed are quickly becoming insufficient.

7. Advantages of 4-Step Process • The simplified process made forecasting practical using: • Standard Survey Methods • Census and other Data Sets • Utilizing existing computational capabilities • It Also Facilitated Quantitative Analysis of Travel Demand (which is based on the complexities of travel behavior)

8. Urban Transportation Planning System • UTPS* Standard Analysis Package • Led to PC-based Programs PlanPac • Initially Developed by BPR • Enhanced by FHWA • These have made the forecasting procedure affordable to most any MPO • Still talking circa 1960’s and 1970’s *Not the University of Toronto Pagan Society!

9. Recognized Internal Inconsistencies • Productions and Attractions typically do not match and require adjustment. • Travel Times used for Trip Distribution are often different than those used for Assignment. • External Analysis Deficiencies • Peak Hour Limitations • Need for Special Generators • Estimation of Intrazonal Travel Times • Determination of the Speed Volume Relationship • K-Factor use

10. Data Inefficiency • When disaggregate models were first proposed in the 1970’s it was argued the 4-step process was not data efficient • Remember…Computing Power Limited and Statistical Theory for model estimation was in infancy. • Aggregation of HH and Emp. data to TAZ

11. Behavioral Foundation • Assumptions can be problematic • Trip Generation Model Example: • Cross-class and regression assume #trips generated is function of persons in HH and vehicles available. In reality, income is a more representative variable for estimating trips

12. Additional Limitations/Issues • Intersection Delay Typically Ignored • All Delay is assumed on links • Highly Coordinated Signal Systems • ITS Technology • In-vehicle Congestion Info • Assumption Travel Only Occurs on the Network • Over-Simplified Network • Centroids and Centroid Connectors for local travel • For air pollution studies off network travel must be added • Simplified Capacities • Only based on number of lanes and side-friction (area type) • Not considered – Truck movements, terrain, geometry

13. Additional Limitations/Issues • Time of Day Variations • Typically 10% Rule of Thumb • Variations are more complex in reality • A small variation by 1%-2% could make a big difference. • Emphasis on Peak Hour • Forecasts are average weekday. • More projects are using “peak-period” forecasts • Derived by hand calculations

14. Improvements to Traditional Four-Step Modeling Accessibility and Land Use Character Improved Travel Surveys and Data Collection (all steps) Trip Generation Land Use Allocation and Forecasting Procedures Non-motorized Transportation PEF BEF LOS Trip Chaining Behavior and Activity Modeling Trip Distribution Multimodal Travel Time and Impedance Dynamic Traffic Assignment and Simulation Modeling Feedback Analysis Mode Choice Operational Improvements Trip Assignment Time-of-Day Models and Peak Spreading Improved Speed Models

15. How Can Models Be Improved? • Transportation models are being called upon to provide forecasts for a complex set of problems that in some cases can go beyond their capabilities and original purpose • Better Data • All models are based on data about travel patterns and behavior. • If these data are out-of-date, incomplete or inaccurate … the results will be poor no matter how good the models are • One of the most effective ways of improving model accuracy and value is to have a good basis of recent data to use to calibrate the models and to provide for checks of their accuracy

16. How Can Models Be Improved? • Inclusion of Other Modes • Transit • Bicycling • Walking • Better Auto Occupancy Models • Sensitive to Policy Issues such as Parking Costs • Ride Sharing • Use More Trip Purposes • May Help to Address Complex HH Trip Patterns • Trip Chaining • More Sensitive Trip Generation Factors

17. How Can Models Be Improved? • Better Representation of Land Access • Smaller Zones • More Connectors • Land use policies that facilitate transit use or that provide high quality site design with good pedestrian access are not well represented in the transportation models. • Trip Distribution and Costs • Trip distribution models should use a generalized measure of distance that includes costs of travel by different means including parking costs.

18. How Can Models Be Improved? • Add Land Use Feedback • Gain a better representation of the interaction of land use and travel demand. • Land use simulation models should be added to the sequence of models to help to determine how a proposed transportation system will lead to land use changes.

19. How Can Models Be Improved? • Add intersection delays • In an urban traffic network most delay is encountered at traffic signals or stop signs • Travel forecasting models should include routines that calculate the delay encountered at intersections • Feedback Loops with Congested Travel Time.

21. TMPI Outreach and Technical Assistance • Review panel to direct program • Conferences • Internet home page • Clearinghouse • Technical assistance from experts in the field • Training centers • Newsletters

22. TRANSIMS • TRansportation ANalysis and SIMulation Systems (TRANSIMS) • Components: • Individual household and travelers • Micro-simulation • Detailed transportation network • Air quality • Analyst toolbox

23. Parting Statements • no single model system is suited for all study objectives • The trip-based, four-step procedure continues to be an effective demand forecasting procedure for certain types of problems, yet, current policy contexts call for alternative models • The array of transportation planning tools available to policy makers needs to be expanded.