The document discusses activity-based travel demand models and their potential advantages for analyzing transportation demand management (TDM) strategies. It provides an overview of what activity-based models are, how they differ from traditional trip-based models, and how certain TDM strategies could be represented within the activity-based modeling framework. While these models offer improved ability to estimate traveler responses to policies, the document also notes some of the challenges with using them, such as cost and data requirements.
Abstract
One of the most persistent problems faced by the San Francisco County Transportation Authority (the Authority) is that of handling a growing collection of counts. Traffic, pedestrian and bicycle counts have been collected by staff, consultants and sister agencies for numerous planning studies at various locations in San Francisco over the years. But how should these counts be organized? Some are in Excel workbooks of varying and spontaneous formats, others consist of scanned handwritten documents, and finally some are on (gasp!) paper.
Since the modeling team at the Authority has a continuous need for these counts in order to calibrate and validate the travel demand model as well as to inform model development, these counts have come under the team’s purview. After a couple of failed attempts to standardize Excel formats and directory structures, the modeling team decided to modernized its counts management system. The Authority first explored proprietary software products, but found them either too expensive, cumbersome, or inflexible. Instead, Authority staff embarked on developing Count Dracula, an open source counts management tool. Count Dracula’s aim is to make uploading, downloading and querying counts easy for Authority staff as well as other interested parties outside the organization. The Count Dracula code base has been designed to be reusable by other agencies with similar needs, and it’s built on GeoDjango, a geographic web framework.
Count Dracula includes a web-based map GUI for visualizing where counts are located (and where more counts are needed), and it includes a query interface so that specific types of counts can be batch-downloaded (for example, midweek counts from the last three years). As it was developed by a modeling team, there is a specific emphasis on counts seamlessly interfacing with model transportation networks. Counts can also be uploaded using this interface, and moderated through an admin interface. This presentation will explain the development of Count Dracula and convince everyone attending to download it and dive in.
This presentation shows how the data we gathered from the smart phone application, CycleTracks, was used to develop a bicycle route choice model which was then integrated into SF-CHAMP, the San Francisco activity-based travel demand model
Abstract
One of the most persistent problems faced by the San Francisco County Transportation Authority (the Authority) is that of handling a growing collection of counts. Traffic, pedestrian and bicycle counts have been collected by staff, consultants and sister agencies for numerous planning studies at various locations in San Francisco over the years. But how should these counts be organized? Some are in Excel workbooks of varying and spontaneous formats, others consist of scanned handwritten documents, and finally some are on (gasp!) paper.
Since the modeling team at the Authority has a continuous need for these counts in order to calibrate and validate the travel demand model as well as to inform model development, these counts have come under the team’s purview. After a couple of failed attempts to standardize Excel formats and directory structures, the modeling team decided to modernized its counts management system. The Authority first explored proprietary software products, but found them either too expensive, cumbersome, or inflexible. Instead, Authority staff embarked on developing Count Dracula, an open source counts management tool. Count Dracula’s aim is to make uploading, downloading and querying counts easy for Authority staff as well as other interested parties outside the organization. The Count Dracula code base has been designed to be reusable by other agencies with similar needs, and it’s built on GeoDjango, a geographic web framework.
Count Dracula includes a web-based map GUI for visualizing where counts are located (and where more counts are needed), and it includes a query interface so that specific types of counts can be batch-downloaded (for example, midweek counts from the last three years). As it was developed by a modeling team, there is a specific emphasis on counts seamlessly interfacing with model transportation networks. Counts can also be uploaded using this interface, and moderated through an admin interface. This presentation will explain the development of Count Dracula and convince everyone attending to download it and dive in.
This presentation shows how the data we gathered from the smart phone application, CycleTracks, was used to develop a bicycle route choice model which was then integrated into SF-CHAMP, the San Francisco activity-based travel demand model
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A Flat Fee Suffices: Taxi Cab Phenomenon in SingaporeBernard Leong
Have you ever wondered why you are never able to get a taxi during certain times of the day in Singapore? Have you ever wondered why the taxi drivers are always complaining? Are the incentives really that bad for them? Gathering some anecdotal evidence coupled with economics and physics, I seek to analyze the cultural phenomenon of taxi cabs in Singapore. At the end, he presents a solution based on free market and competition to how the problem might be solved.
Presented in Blinkbl-nk on 20 June @ Blu Jazz, Singapore: http://blinkbl-nk.com/
How to Design an On-Demand Transit ServiceGurjap Birring
There have been hundreds of on-demand transit projects deployed around the world, but are transit agencies designing them for success? Pantonium’s team will discuss various approaches to designing an on-demand transit service based on our experiences deploying projects around North America and our observations from other similar projects.
Final pitch by #jpreneur students John Stevenson, Aidan Galasso, Robert Kohler, Darius Hence. Where to Run is an app designed to help runners find the best routes when traveling.
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Shih-Fen Cheng is Associate Professor of Information Systems and Deputy Director of the Fujitsu-SMU Urban Computing and Engineering Corp Lab at the Singapore Management University. He received his Ph.D. degree in industrial and operations engineering from the University of Michigan, Ann Arbor, and B.S.E. degree in mechanical engineering from the National Taiwan University.
His research focuses on the modeling and optimization of complex systems in engineering and business domains. He is particularly interested in the application areas of transportation, computational markets, and human decision-making. He is a member of INFORMS, AAAI, and IEEE, and serves as Area Editor for Electronic Commerce Research and Applications.
This paper compares the potential benefits and impacts of two types of congestion pricing: oad- or cordon-based, and parking-based, that the San Francisco County Transportation Authority studied as a part of the Mobility, Access, and Pricing Study. The study is evaluating comprehensive pricing and mobility-enhancing packages to improve access and offer more sustainable travel choices to and within San Francisco. The Study Team evaluated the cordon and parking congestion charges using the SF-CHAMP regional travel demand model (also known as RPM-9). This paper discusses the current representation of parking in SF-CHAMP and its limitations, and then summarizes the development of an improved parking representation including additional data needs.
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Analyzing Travel Demand Management Strategies: the Promise of Activity-Based Travel Demand Models
1. Analyzing Travel Demand
Management Strategies: the Promise of Activity-
Based Travel Demand Models
Elizabeth A. Sall
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Transportation Research Board Annual Meeting
Sunday January 13th, 2013
2. Overview of Questions I Hope to Answer
1. What is an “Activity-Based Travel Demand Model”?
2. What is it about these activity models make them
better (or not) for analyzing TDM Strategies?
3. What does a specific TDM strategy look like in an
activity model? (a few examples)
4. Who has these activity models anyways?
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 2
3. Activity-Based Travel Demand Model?
A few principles
• People want to do activities, and they travel in order
to participate in them travel is a derived demand.
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 3
4. Activity-Based Travel Demand Model?
A few principles
• Household and social structures influence activity
and travel there‟s a lot more to „you‟ than the zone
around you.
Flickr: MNicoleM
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 4
5. Activity-Based Travel Demand Model?
A few principles
• No cart before the horse / driving home if you
walked to work / leaving work before you got there
interdependence explicitly recognized.
If it looks like But you don’t
this outside have a car at
every morning… work now…
then you’ll So even if
probably the evening auto
decide to… commute is cake,
you’ll need to…
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6. Activity-Based Travel Demand Model?
A few principles
• No cart before the horse driving home if you walked
to work / leaving work before you got there
interdependence explicitly recognized.
If this area where you You realize that if
work has a congestion you drive like this
fee from 4 to 6 pm… In the AM…
That it will cost
you like this $
In the PM…
And you live here…
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7. Activity-Based Travel Demand Model?
A few principles
Sum of all small behavior types is the majority of
behavior we are all unique, and that is important.
0.18
0.16
0.14 Income $0-30k
P robability Dens ity
Income $30-60k
0.12 Income $60-100k
0.1 Income $100k+
0.08
0.06
0.04
0.02
0
$- $5 $10 $15 $20 $25 $30
Value of T ime ($/Hour)
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 7
8. Activity-Based Travel Demand Model?
What it comes down to:
• A less-cool-looking version of sim-city.
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9. Activity-Based Travel Demand Model?
Every Sim has a:
• role
• income
• age
• gender
• household
Population Synthesis
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 9
10. Activity-Based Travel Demand Model?
Every Worker Sim
chooses a workplace
based on:
• Job type/income
match
• Accessibility to work
Workplace Destination Choice
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 10
11. Activity-Based Travel Demand Model?
Every Sim Household
decides how many cars
they want:
• How easy is it to get to
work with and without
a car?
• How easy is it to get to
other stuff around the
house with and
without a car?
• Demographics
Auto Availability
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 11
12. Activity-Based Travel Demand Model?
Every morning, each Sim
wakes up and asks
themselves “Gee, what
am I going to do today?”
• How easy is it to get to
work?
• What else do I need to
do today?
• Demographics
Tour Generation
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13. Activity-Based Travel Demand Model?
If they are going
somewhere other than to
work, the Sim will pick
what the primary reason
for leaving the house is:
• Accessibility
• Schedule
Tour Destination Choice
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14. Activity-Based Travel Demand Model?
Then they need to pick
the primary way that they
are going to get there.
• Accessibility by mode
Tour Mode Choice
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 14
15. Activity-Based Travel Demand Model?
If they need to make
stops along the way, they
pick places that are
convenient to the tour
mode that they have
chosen to their primary
destination.
• How far a detour is it
from my main trip?
• Near transit transfer
point/freeway exit?
Intermediate Stop Choice
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 15
16. Activity-Based Travel Demand Model?
Once the entire tour is
planned out, the Sim
choses the exact mode
that they will use for each
sub-part of their
excursion from home
(aka tour).
Trip Mode Choice
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 16
17. Activity-Based Travel Demand Model?
Based on congestion
levels and other
preferences, the Sim then
navigates their way
through the network to
participate in activities
and carry out their plans.
Route Choice
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 17
18. Activity-Based Travel Demand Model?
All of these choices are
carried out by the
individual with full
knowledge of their:
• Other choices
• Activity needs /
Household
• Physical / Temporal
constraints
• Demographics
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 18
19. Good for TDM Strategy Analysis?
• Depends on the strategy. Can it be represented in
ABM framework? Can it be just as well represented
in 4S model?
• Existing Tools? Do you already have an AB model?
• Got Data? Does the data already give you the answer
to the question that you have?
• Risk/Reward? Depends on the questions that you
need to have answered and how sure you want to be
about them.
(And if you can‟t articulate a question that you are trying to answer, there are much more
rewarding places to sink time and money)
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 19
20. ABM -- Good for TDM Strategy Analysis?
Pros
• Understands induced and latent demand
If you build that HOT lane, more people are going to
drive.
If you make it easier to park, more people are going
to drive.
Hard to build your way out of congestion.
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 20
21. ABM -- Good for TDM Strategy Analysis?
Pros
• Explicit representation of interdependencies
If my child has swim practice, I need to drive today.
If I need to stay late at work and transit is bad after
rush hour, I will drive.
If I bike down a hill, I have to bike back up it.
Bike Accessibility To Bike Accessibility From
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 21
22. ABM -- Good for TDM Strategy Analysis?
Pros
• Lots of choices for performance measures
Each sim is tracked and can be accounted for
individually or across multiple demographic or
geographic dimensions.
Future GHG Production per HH
sfmobility.org
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 22
23. ABM -- Good for TDM Strategy Analysis?
Pros
• Simulation framework is flexible
You can “do something else” to your Sim
Your Sim can become aware of new information
Your Sim‟s decisionmaking can have
more/new/better rules or models
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24. ABM -- Good for TDM Strategy Analysis?
Pros
• Simulation framework is flexible
Our Sims just learned about the benefits of bike lanes
Trip Difference - Bike Projects
Daily Tours v4.1 Harold v4.3 Fury
Bike 500 0.1% 3,000 0.8%
Walk 1,100 0.0% -500 -0.0%
Transit 850 0.0% -600 -0.0%
Auto -2,400 -0.0% -1,300 -0.0%
Total 0 0.0% 600 0.0%
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25. ABM -- Good for TDM Strategy Analysis?
Pros
• Can easily implement what if scenarios:
Can still do sans data. Test a hypothesis!
What if we had a compressed work week?
We could assume that:
commuting would be 20% less
work duration a few hours longer
only full time workers affected
Tested in Burlington:
-4% overall trips
Only slight VMT decrease
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 25
26. ABM -- Good for TDM Strategy Analysis?
- Pros
• Understand the system
• Recognize unintended consequences
Can we build our way out of GHG issues with bike
lanes?
If our transit system becomes the most popular thing
on earth how many people would ride it?
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 26
27. ABM -- Good for TDM Strategy Analysis?
Cons
• Is it overkill ?
• Is it easier to run a pilot and find out vs. spend a few
years and $1 million developing a model ?
• Do you really just need a back of the envelope guess?
….but once you have it you will find so many questions
that it is good at answering.
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 27
28. Who has these tools?
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 28
29. Thanks for inviting me
to this community to speak!
(we are open to suggestions about how to
provide info for better decisions)
elizabeth@sfcta.org
www.sfcta.org/modeling
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
30. “…A major advantage of activity based modeling lies in
its ability to give a better understanding and prediction
of traveler responses to TDM measures and other
types of transportation policies. “
“ These improved estimates of the changes in important
transportation variables, then, provide the basis for the
development of more accurate estimates of emission
reductions that would result from the implementation
of one or more TDM strategies.”
Shiftan Y. et al “Activity-based Modeling as a Tool for Better
Understanding Travel Behaviour” IATBR Conference
Paper. Lucern, 10-15 August 2003.
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31. “…trip based four step aggregate methodology, is
inadequate in analyzing the sustainable transport
policies. “
“A review of … activity based travel demand modeling
found that they are capable of handling such policies
better than conventional models and are assistive to
the decision makers in arriving at right mix of polices
specific to the situations. ”
Malayath, Manoj and Ashish Verma. “Activity based travel
demand models as a tool for evaluating sustainable
transportation policies” Research in Transportation
Economics Volume 38/Issue 1. Feb 2013.
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Editor's Notes
…but I might not answer them in order!
Cat Telecommute photo: License Some rights reserved by seandreilingerI’m sure Academics and practitioners alike can argue with this list, but I think these are important for our discussion today.Travel is a derived demand. This is an important consideration especially if we are thinking about serving people’s needs with less of it. However (unrelated), it’s also important to consider that just as there is a concept of a “travel budget” for a day (a ceiling amount of traveling you are willing to do), there is also a psychology concept of a “social interaction floor” – the fact that people who aren’t going to work often partake in many more out of home activities…they go to the grocery store much more frequently, etc.This is an important consideration when devising TDM strategies such as telecommuting. We see it in the data, and we can also actually also show it with an AB model.Telecommuting can mean different things in different places – and that can mean different things for the efficacy of using it as a TDM strategy.Person on the left is being social with his cat…and looking out the window at suburbia – he’ll probably make a few trips (probably driving) out of the house just to interact with real people.People on the right walked down the street to a coffee shop.A good AB model understands that there isn’t just substitution effects for in- and out-of home activity…but that there is latent demand for out of home activity.
A lot of models or sketch planning tools tend to aggregate together demographics from what is around you into “you”. AB modeling explicitly recognizes that these sums of people actually exist in a structure…and that structure matters. Households with school-age children behave extremely differently than households without children. A parent that needs to drive their kid to school at a certain time is far less like to flex their time at work to take advantage of off-peak commuting hours…and is also a lot less likely to vanpool/carpool to work. Most newer activity-based models explicitly understand how a kid’s school location and transportation options directly impact their parent’s day-patterns.Even older activity-based models contain variables and understandings of how your household structure affects your choices.