1. Using Linked Non-Home-Based Trips in Virginia
Hadi Sadrsadat, PhD, RSG Vince Bernardin, PhD, RSG
May 15, 2017

2. Objectives

3. Additional Options for Agencies
Main option for dealing with NHB trips has been just to develop an entirely new activity-based model
According to a TMIP survey at the end of 2013:
Near term, 3/4 of agencies will not have an activity-based model
Long term, 1/3 – 2/3 of agencies will stay with trip-based models
As TRB Special Report 288 states: “there is no single approach to travel forecasting or set of procedures that is ‘correct’ for all applications or all MPOs.”

4. The Challenge: The Problems with NHB trips
Fundamental problems with NHB trips in trip-based models:
When four-step models evolved in the 1950s & 1960s, NHB trips only accounted for ~15% of trips
According to surveys, NHB trips now 30% of all trips
By definition, NHB trips are disconnected from the households that make them and, thus, from the household’s HB trips
This disconnect affects all dimensions of travel – location, mode, & time
NHB trips are like noise in most models – pumped up to produce the right amount of overall traffic – but adding error to results

5. Spatial Problems with NHB Trips
Physically impossible pattern for NHB trip patterns in four-step models:
Travelers/vehicles appear and disappear, making trips between places they never went in the first place
Unreasonable responses by NHB trips:
New households can generate new NHB trips in remote locations they almost never visit on home-based trips

6. Other Problems with NHB Trips
Mode and Time of NHB trips
NHB trip modes are not conditional on HB trip mode choice in 4-step models:
Shifting both HB and NHB trips to transit in transit improvement
Decreasing HB & NHB walk trips in transit improvement
In reality, more HB transit trips can lead to more NHB walk trips

7. Methodology

8. Understanding the Problem
Four step models represent all trips through two component models: trip generation and trip distribution
These correspond to the decision of whether/how frequently to make a trip and where to go (destination)
These two models cannot properly define a NHB trip
A NHB trip requires a third piece of information/decision: the origin, where to visit the destination from:
2 spatial (distribution) models are necessary to represent a NHB trip because neither its origin or destination are known

9. Background of New Methodology
A Simple Solution:
New methods explored by FHWA’s Travel Model Improvement Program (TMIP)
Successfully implemented in Salt Lake City by RSG
Very promising results with more reasonable responses to land use scenarios
Simplest version of the method was used for this project

10. Linking NHB Trips to HB Trips
Linking and running NHB distribution in series rather than in parallel with the HB distribution models
HB & NHB distribution can assign both the origin and destination to NHB trips
No changes to the HB model components

11. Generation by Mode
Generation of NHB trips is done by mode, as a simple function of the HB attractions to that zone by each mode
Mode choice is therefore unnecessary for NHB trips

12. Methodology Details
NHB trips can be scaled to total NHB trips estimated in initial generation
NHB trips are split to production and attraction equally
The same friction factors were applied to NHB trips in distribution
Explain that scaling is an option – done in Harrisonburg but not Fredericksburg

13. Model Implementations in Virginia

14. Harrisonburg Small city in Virginia 81,000 population
NHTS, AirSage, and Streetlight data
Typical 4-step model:
263 zones
5 trip purposes (HBW, HBS, HBO, HBU, NHB)
4 modes (DA, HOV, Transit, Non-motorizes)
4 time periods
No destination choice
Feedback loop

15. Fredericksburg Small city in Virginia, near DC 375,000 population
NHTS, AirSage, and Streetlight data
More transit ridership & slugging in I-95 express lanes corridor
Typical 4-step model:
814 zones
5 trip purposes (HBW, HBS, HBO, HBU, NHB, IEW, IENW)
4 modes (DA, HOV2, HOV3+, Transit)
4 time periods
No destination choice
Feedback loop
Mention, one of the few places in the US with slugging – special mode in mode choice

16. Charlottesville Small city in Virginia 150,000 population
NHTS, AirSage, and Streetlight data
UVA student population
Advanced trip-based model:
~300 TAZ
synthetic population
disaggregate trip generation
destination choice
linked NHB trips
accessibility variables
perceived time
CAV “plumbing”
4 time periods
feedback loop
In early stages of development, will incorporate other advances in trip-based modeling

17. Calibrated NHB Models Harrisonburg
Simple regression models from household survey data
NHB SOV = 0.251*HBW_SOV *HBO_SOV *HB_HOV
(t=5.69) (t=8.11) (t=5.14)
NHB HOV = 0.500*HBW_HOV *HBO_HOV *HB_SOV
(t=3.44) (t=10.01) (t=1.05)
HB SOV are most likely to generate NHB SOV, HB HOV more likely to generate NHB HOV, but both can generate either
Note that this is a logical connection between the modes

18. Calibrated NHB Models Fredericksburg (under development)
Different TOD models for HOV
Also Transit NHB trips
NHB TRN = 0.154*HB_TRN *HB_AUTO
(18.25) (t=1.58)
HB TRN are most likely to generate NHB TRN, but HB AUTO can generate a small number too
Example: Person can get a ride to work, then take the bus somewhere else after work, etc.

19. Model Calibration NHB Trip Length Distribution
Some inconsistency due to small sample NHTS data in Harrisonburg

20. Model Validation Old model – 1.98 New model – 2.96 NCHRP 716 – 3.0
Overall NHB Trip Rate per Household
Old model – 1.98
New model – 2.96
NCHRP 716 – 3.0

21. Dynamic Validation / Sensitivity Testing
New Residential Growth
Important to test model response properties, not just base year replication
Model response to new developments
New hypothetical growth in town of Bridgewater SW of Harrisonburg
No Development
With Development
Population
253
5000
Household 91
1800
Auto
170
3400

22. Original Model Response
It would probably be better if we could get the NHB maps on the same scale as the HB

23. Enhanced Model Response
It would probably be better if we could get the NHB maps on the same scale as the HB

24. Conclusions

25. Conclusions
The enhanced Harrisonburg model with linked NHB trips performs better than the original model:
More reasonable responses to hypothetical new residential growth
Clearer, more intuitive and reasonable connections between NHB and HB modes
Better able to replicate observed NHB trip rates, mode shares and OD patterns with less calibration

26. Contact Hadi Sadrsadat, PhD www.rsginc.com Hadi.Sadrsadat@rsginc.com
Consultant